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Review

A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications

Department of Electrical and Electronic Engineering, University of West Attica, Thivon Avenue 250, GR-12244 Athens, Greece
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2023, 12(2), 30; https://doi.org/10.3390/jsan12020030
Submission received: 1 March 2023 / Revised: 20 March 2023 / Accepted: 25 March 2023 / Published: 3 April 2023

Abstract

:
The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT (Internet of Things) have become a way for devices to communicate, share their data, and control them remotely. Machine-to-Machine (M2M) scenarios can be easily implemented as the cost of the components needed in that network is now affordable. Some of these solutions seem to be more affordable but lack important features, while other ones provide them but at a higher cost. Furthermore, there are ones that can cover great distances and surpass the limits of a Smart Home, while others are more specialized for operation in small areas. As there is a variety of choices available, a more consolidated view of their characteristics is needed to figure out the pros and cons of each of these technologies. As there are a great number of technologies examined in this paper, they are presented regarding their connectivity: Wired, Wireless, and Dual mode (Wired and Wireless). Their oddities are examined with metrics based on user interaction, technical characteristics, data integrity, and cost factor. In the last part of this article, a comparison of these technologies is presented as an effort to assist home automation users, administrators, or installers in making the right choice among them.

1. Introduction

The continuous advances performed regarding Integrated Circuits (ICs) technology enabled the introduction of Micro Electro Mechanical Systems (MEMS) [1]. MEMS include microsensors, microactuators, micro-electronics, and other microstructures within tiny dimensions (Figure 1).
The next goal was to wirelessly interconnect sensing MEMS. In this way, WSNs (Wireless Sensor Networks) were introduced [2]. WSNs consist of MEMS-based devices, called sensing nodes that, despite their small size, have enhanced capabilities. Specifically, sensing nodes cannot only monitor ambient conditions in wide areas of interest but also process and transmit sensed data [3]. This makes WSNs ideal for supporting a practically endless range of applications [4,5,6,7,8,9,10], although their operation is obstructed mainly due to their limited energy sustainability [11,12,13], but also because of other problems such as congestion, vulnerable security, connectivity loss, insufficient coverage, and deteriorated Quality of Service (QoS) [14,15,16,17,18,19,20].
The interconnection of the sensing nodes through Internet technologies led to the birth of IoT [21,22,23]. With IoT technology, all Things (devices) can be managed and configured remotely, such as Cyber-Physical Systems (CPS) that interconnect the physical and cyber worlds with telecommunications.
The application of IoT in Home Automation (H.A.) systems led to the development of the so-called Smart Home sector [24,25,26]. In Smart Homes, systems controlled manually, such as heating, cooling, lighting, and other appliances, can be monitored and controlled remotely. A cyber-physical system, as depicted in Figure 2, takes measurements concerning temperature, humidity, and energy consumption, which can be displayed to a remote user, giving them the capability to initiate actions through appropriate actuators and modify them to a desirable level in a closed-loop feedback control scheme where communications are vital [27]. The most common example, regarding home comfort, is for a user to control their HVAC (Heating, Ventilation, and Air Conditioning) through a smartphone, tablet, or PC. The home temperature can be adjusted from a remote location to the appropriate one for its residents.
As the growth of IoT began, many companies developed sensors, actuators, and control systems that are IoT compatible [28]. The need for interconnecting them with sensors realized by other vendors has led to the development and growth of many standard communication technologies. BACnet [29], Dupline [30], Ethernet, and KNX [31] were the first to be used that focus on Home Automation and the establishment of the so-called Smart Home domain. Later on, new technologies, i.e., Bluetooth [32], Wi-Fi [33], Zigbee [34], and Z-Wave [35], were introduced. There are also ones used to offer more capabilities and innovative features, such as 6LowPAN [36], EnOcean [37], Insteon [38], mioty, and Thread [39]. Technologies such as LoRA, mioty, Sigfox, LTE (5G), and NB-IoT are also used but aim to cover larger distances. In the past years, an effort was made by large companies, e.g., Apple (HomeKit) [40], Google (Alexa), and Samsung (SmartThings) [41], with the introduction of consumer products targeting smart homes and unifying them with one application regardless of the short-range communication technology used (BLE, Wi-Fi, ZigBee).
The plethora of choices available for consumers confuses the optimal selection according to the target application. There is a plethora of articles dealing with this issue [42]. This review article aims to present a thorough comparative study among existing technologies of this kind. Specifically, in what follows, in the first section of this article, IoT-enabled communication technologies suitable for Sensor Networks as well as for Smart Home applications are presented. In Section 3, these technologies are comparatively examined in regard to appropriate metrics and criteria. These criteria are divided into four major categories: user perspective, technical characteristics, security/quality, and energy/cost. The first one represents the popularity of these technologies, the platforms that are used, their primary markets, and whether they are open-sourced or proprietary. The second category issues the technical characteristics of these technologies, such as their coverage-supported data rates and the maximum number of concurrent connections. In the next one, the data integrity of the data exchanged, as well as the security mechanisms provided, are examined. The last category is dedicated to the energy factor, which includes consumption, efficiency, and overall cost [43,44]. In Section 4, concluding remarks are drawn. At the end of this article, readers will be able to have a better understanding of the technologies presented, and the conclusion will be clear.

2. IoT-Based Technologies for Smart Home Automation

IoT-based technologies for home automation are now available for every home. There are ones that can be applied using the existing wired infrastructure (powerline cables or Ethernet), connecting sensors, controllers, and other devices, while others are using wireless technologies. In what follows in this section, the most widely used technologies of this kind are presented in three major categories: wired, wireless, and dual-mode (i.e., both wired and wireless).

2.1. Wired Communication Technologies

Wired technologies are the eldest ones, as the first smart home implementations were based on the existing home infrastructure (Power Lines). They provide more secure communications with limited interference.

2.1.1. BACnet

This technology is well-known and used in the US. As presented in “Smart Home Automation in the IoT Era: A Communication Technologies Review” [45], it is a specification based on three major characteristics. It can provide connectivity with the use of various wired communication technologies and is wireless with the collaboration of EnOcean technology [43,44,46,47,48,49].

2.1.2. Dupline

Dupline is a wired home automation technology mostly known in the EU. It uses powerlines or twisted pairs for exchanging information. The maximum number of devices that can be connected in this system is 128, whilst its packet size is very small (14 Bytes), achieving data rates up to 9.6 Kbps [30,45,50,51].

2.1.3. Ethernet

Ethernet is one of the most well-known technologies introduced by Bob Metcalfe and his team [52], who allied. It is known for interconnecting network devices such as computers and PLCs through its RS586 port. It is a proprietary technology, standardized with backward compatibility. It uses twisted pair or fiber optics as a data exchange medium while achieving very high data rates [45,53,54,55,56,57,58,59].

2.2. Dual-Mode Communication Technologies

These technologies take advantage of both wired and wireless technologies in home automation through communication redundancy and provide high availability.

2.2.1. KNX

KNX (aka KONNEX) is a very popular worldwide open standard technology. It supports powerline and ethernet cabling for wired communications and provides wireless connectivity as well. Furthermore, it can collaborate with other known wireless technologies such as EnOcean, WiFi, and Zigbee. According to the medium used, it can provide data rates from 9.6 Kbps up to 100 Gbps with a packet size of 16 Bytes [31,45,53,60,61,62,63].

2.2.2. LonWorks

This technology was presented by the Echelon Corporation Company in 1991. It first originated as BACnet’s competitor; therefore, compatibility is provided between them. LonWork is an abbreviation of “Local Operating NetWORK”. Its’ innovation is that each device has an embedded Neuron Chip, along with transceivers and electronics for application. In detail, it has an SoC (System on a chip) implementation containing many microprocessors, ROM (Read Only Memory), RAM (Read Access Memory) modules, and I/O (Input/Output) interface ports. Moreover, an electronic module is provided for physical interface connections with other devices.
It is a multi-medium technology, as it can provide connections through:
  • Twisted pair;
  • Ethernet;
  • Fiber Optics;
  • RF;
  • BACnet.
The message size is 228 Bytes, while the data rates provided can reach up to 1.25 Mbps. Regarding coverage, it can reach distances up to 2700 m in wired connections.
Devices or systems can be separated into smaller groups of intelligent elements, called nodes, creating a smart network [64,65,66].

2.2.3. X10

It is one of the oldest home automation systems, as it was introduced in 1975. This technology is open-sourced and used for remotely controlling X-10’s compliance transmitters and receivers. It has two modes for connections:
  • Wired: powerlines already pre-installed in every home;
  • Wireless: RF.
Short messages (commands) are used and broadcasted by transmitters to the receiving unit and processed by a unique ID. Receivers read this information and compare it with the receiving ID to determine if it matches their own. If the condition is met, then the whole message is downloaded and processed.
Due to the nature of this technology, data rates of 60 bps are provided, covering distances up to 30 m. Therefore, it is mostly used in automation networks such as lightning, home appliance, and security sensors [43,67].

2.2.4. Insteon

This technology is X10’s successor; it provides backward compatibility with all existing devices supporting the technology (operating in 132 KHz) despite the commands not being similar. Its network consists of both wired and wireless connections providing MESH connections between them and ensuring connection redundancy [38,43,44,64,66].

2.3. Wireless Communication Technologies

These technologies are introduced to solve the need for interfacing with the existing home infrastructure and to provide a low-cost, high-efficiency solution for device communication. They can be divided into two major categories, according to the distance they cover.

2.3.1. Short Range

Short-range technologies can be used primarily on small buildings and homes, providing high-speed and high-availability connections between devices.

6LowPAN

IPv6 over low-power wireless personal area networks is used for home automation systems and is characterized by its simplicity and low cost. It provides wireless connectivity where serious power constraints are needed. It makes use of IEEE 802.15.4 standard in the lowest OSI-7 layers [53] and IPv6 (IP version six) protocol for communications.
This technology is characterized as WPAN (Wireless Personal Area Network), exchanging data packets of 100 bit, while achieving data rates up to 250 Kbps and covering distances up to 200 m. It also provides mesh capabilities, but with a device making multiple wireless concurrent connections with more than one neighboring device. If its primary connectivity is lost, communication can still be established through another connection in the same network.
6LowPAN has compatibility with devices using the same standard (802.15.4) and can also communicate to a computer network through Wi-Fi using a gateway [36,68,69,70,71,72,73].

Bluetooth

Bluetooth is a very popular short-range networking technology mostly used in computers, smartphones, and peripheral interconnections. In 2015, the Bluetooth 4.0 standard was introduced [74]. Described as BLE (Bluetooth Low Energy), it has unlocked its potential to ensure proper communications to the devices in the network whilst improving battery life and data rates [32,69,74,75,76,77,78,79,80,81,82,83,84].

EnOcean

This technology is a wide-spreading technology, as it provides energy harvesting capabilities in wireless IoT and in-home automation. Every device in this WPAN (Wireless Personal Area Network) does not require any external energy source or battery [45].
The characteristics of this technology are:
  • It can cover distances up to 300 m (outdoors);
  • Network speeds up to 125 Kbps;
  • 50 μW is required for signal transmission;
  • Low band radio, which operates at 868 MHz and 315 MHz radio bands;
  • Short telegrams (messages of 4 Bytes) are exchanged between devices;
  • It can have heterogeneous connections with other technologies, such as:
    Ethernet gateway;
    Wi-Fi gateway;
    BACnet;
    LonWorks;
    BLE;
    Zigbee.
Furthermore, mesh connectivity is supported while providing quality mechanisms QoS (8-bit CRC) and encryption with the use of VAES (Variable AES) [37,44,47,60,77,78,85,86,87,88,89].

Thread

Thread is a technology that is also based on the 802.15.4 standard, originated by seven companies (ARM (Softbank), Big Ass Fans, Freescale (NXP), Nest Labs (Google), Samsung, Silicon Labs, Yale Locks), which formed the Thread Group Inc. in 2015 [45,90]. It is an open standard technology incorporating many known standards. Therefore, it provides compatibility with other ones like the Nest thermostat and several Zigbee devices [39,75,78,86,90,91,92,93,94,95,96,97].

Wi-Fi

This network technology is very popular, as it is known for the interconnection of various everyday devices. The 802.11ac standard (released in 2013) operates at the frequency bands of 2.4 GHz and 5 GHz, achieving data rates from 600 Mbps to 6.93 Gbps. With the introduction of 802.11ax in 2016 (Wi-Fi 6) and the use of M.I.M.O. (Multiple Input, Multiple Output) techniques, the technology can handle multiple connected devices, while providing data rates of 866.6 Mbps, 15 Gbps, 3466 Gbps of up to 9.6 Gbps. The protocol suite used for communications is the TCP/IP (v.4 and v.6), providing mechanisms for QoS (Quality of Service) with 32-bit CRC and security with encryption of AES128.
As a well-known and used technology for end-user devices, connections with almost all known home automation technologies are provided by vendors.
In 2014, the standard 802.11af of Wi-Fi enhanced its capabilities by improving energy management and efficiency. With the use of the existing WLAN topology, TV White Space frequencies (54 MHz–97 MHz band), and four channels for communications, the area covered remains 100 m, while the data rates can reach or surpass 400 Mbps.
802.11ah (WiFi HaLow) standard, newly introduced by IEEE and released in 2017, is an evolution of the 203.11af and is aimed at IoT devices. It provides many power-constrained stations, up to 100 m of area coverage, using frequencies of 900 MHz and network speeds of 4 Mbps. This technology also supports channels of 1 MHz/2 MHz for use in IoT, which can be increased up to 16 MHz to achieve greater data rates [33,56,75,98,99,100].

Z-Wave

Z-Wave is a proprietary technology used by many vendors for home automation and IoT. It is characterized as low-cost, providing low-power transmissions. As described in Paetz (2013) [101], this technology has static routing and is implemented with a centralized routing table. Routing calculations are embedded into the messages so their forwarding behavior can be indicated (Fuller) [102].
OpenZwave is a variation of Z-Wave, which is open-source [103]. It is used in PCs as a USB transceiver dongle, but still, its routing logic is not accessible. It is encrypted in the firmware of the device [35,44,75,78,81,86,96,102,104,105,106].

Zigbee

This technology is quite popular in wireless home automation systems and is one of the base competitors of Z-Wave. Due to the delay in the definition of the Bluetooth standard, several companies started the development of a new WPAN technology, named ZigBee, introduced in 2004 [44,61,75,77,78,79,80,86,88,94,98,105,107,108,109,110,111].

2.3.2. Long Range

Long-range connections are mostly used for connections in larger buildings or architecture, providing availability without the use of repeaters.

DASH7

Industry-standard DASH7 Alliance Protocol (D7AP) is a standard based on the ISO/IEC 18000-7 and applied in wireless sensors and actuators. As its primary function design, it is based on the BLAST (Bursty, Light Data, Asynchronous Transitive) concept [80]. It operates on unlicensed low wireless frequencies 433 MHz, 868 MHz (EU), and 916 MHz (US), making the technology capable of reaching distances of 10 Km. Its message length is 256 Byte and provides data rates up to 200 Kbps achieving low latency when connected with moving network-capable objects [80]. Communication is done instantly (bursty) without containing any heavy data, such as audio or video [112], making it capable of light data payloads used in conventional applications.
Due to its command–response communication method, DASH7 provides asynchronous communication. This is achieved as it requires a periodic “hand-shake” for its network and its’ upload-centric nature in addition to the download-centric other technologies that follow [68,80,99,112,113,114,115,116,117,118,119,120,121].

LoRa (Long Range)

LoRa (Long Range), as the acronym displays, was first introduced in 2015 by Cycleo [122] and promoted to consumers by Semtech [123], is an open standard, used mostly for wireless systems separated by great distances with the use of unlicensed RF bands.
It is described as LPWAN (Low Power Wide Area Network) or WWAN (Wireless Wide Area Network) and is used for long-range data transmissions, more than 10 km (up to 20 km), while consuming very low energy. This is achieved due to the low frequencies used, its’ message protocol (LoRaWAN) of 255 Bytes, and the low data rates supported (0.3–50 Kbps). As can be seen, it is suitable for applications exchanging a low set of data for long ranges without having to replace batteries for many years (more than 10 years) [124]. Mainly, it is used in agriculture, but there are also applications developed for H.A.
This technology provides a connection to existing computing networks, such as ethernet and Wi-Fi, through its gateway, providing an administration mechanism. Despite its low data nature, LoRa offers data encryption (AES) but no QoS mechanism [70,81,98,99,108,113,122,125,126,127,128,129,130,131,132,133,134,135].

LTE (5G/NB-IoT)

LTE (Long Term Evolution) is a very popular networking technology, well known for mobile phone communications used worldwide. It was introduced by 3GPP in 2008 and is based on the already existing networks GSM/EDGE and UMTS/HSPA, having as its primary objective faster data rates for mobile networking devices. To achieve that, new techniques of digital signal processing were introduced.
First, 2G was provided to 90% of the world’s population, but its’ main aim and target was mostly for voice [136]. Therefore, 3G has been introduced, providing more services than voice for a great amount of data. Emerging new technologies such as smartphones and tablets increased the need for higher data rates and led to its evolution in 2012 (4G).
Further, 5G was built on the foundations of its successors, providing even higher data rates of 10 Gbps, reliability (32-bit CRC), and encryption (AES256). For IoT and smart home implementations, special modes of this technology are used, LTE CAT-0, LTE CAT-M, and NB-IoT. These are optimized for IoT and smart buildings, reducing the complexity while covering the same range of the same infrastructure.
With the introduction of 3GPP release 12, a new category for UE (User Equipment) is supported. This evolution in Cat 0 (Category 0) devices resulted in a significant increase in their battery life. The most notable changes are:
  • The throughput for Uplink and Downlink is reduced to 1 Mbps;
  • The number of antennas is now decreased from 2 to 1;
  • The UE receiver bandwidth is reduced to 1.4 MHz, which can allow the reduction of substantial complexity;
  • UE can still operate in existing LTE system bandwidths up to 20 MHz;
  • A UE with a lower power class allows the integration of a power amplifier in a system provided in a single chip.
Table 1 displays the UE categories from the 3GPP Specification number 36.306.
The 3GPP releases 13 and 14 are widely known as NB-IoT, and it is mostly used for IoT implementations [137]. Huawei Technologies Co. Ltd. was one of the major companies focusing on this technology, providing enhanced, long-range coverage with the support of a substantial number of low-throughput devices [138]. Devices in this network are characterized by enhanced network architecture, low response time, low delay sensitivity, very low cost, and low energy consumption. The main advantage of this technology is the ability, when deployed in authorized frequency bands, to make use again of its core network.
As IoT devices do not require high data rates, the size of the message varies according to the network deployment, and transmission speed can be up to 204.8 Kbps and 234.7 Kbps for upload and download, respectively. Messages are encrypted with the AES256 Standard (Advanced Encryption), providing extra security for the information exchanged. Furthermore, this technology provides great data integrity, as it implements SNOW 3G [139] or ATR-128 CMAC (Cipher-based Message Authentication Code) with 4 Byte MIC (Message Integrity Code) QoS mechanism. Due to its origin, LTE, is supported by many developers creating applications for remote management of systems such as traffic control and home automation [81,99,129,132,134,137,138,140,141,142,143].

Mioty

This is a brand-new, open technology created and developed by Fraunhofer Institute in 2016 according to the ETSI telegram splitting ultra-narrow band (TS-UNB) technical specification for low-throughput networks (TS 103 357) and provided to the public through the Canadian company BehrTech. It is a long-range platform (WWAN) covering distances up to 15 km (without obstacles), with data rates of 512 bps. It provides low consumption and long battery life; therefore, it is characterized as LPWAN.The innovation in mioty is the way that messages are transmitted. They are formatted as short telegrams of packets sizing 10–192 Bytes, but they are not sent simultaneously. Using TSMA (Telegram Spread Multiple Access), a random MAC is used, dividing its’ message transmission into several shorter packages (fragments). Then, these are distributed randomly on different channels and timeslots. This technique, according to its developers, can tolerate packet loss of up to 50%. In Figure 3, the mioty telegram splitting technique is depicted.
As Mioty is a new technology, there is not so much information available and open-source developments concerning the design of Gateways and wireless nodes, and therefore it is difficult to analyze in-depth the performance of the technology compared with other LPWAN technologies [113,128,134,144,145].

Sigfox

It is a long-range communications technology created and developed in 2010 in Toulouse, France [129]. It is defined by the ETSI ERM TG28 standard and characterized as LTN (Low-Throughput Network).
Sigfox uses the free, unlicensed bands of:
  • 868 MHz (Europe);
  • 902 MHz (US);
  • 430 MHz globally;
  • 2.4 GHz globally.
The distances covered are greater than 10 km, achieving data rates up to 100 bps for uploading and 600 bps for downloading. Its’ message length is 8–12 Bytes and provides heterogeneity as modules are developed, providing a connection of its gateway to BLE and Wi-Fi. Unfortunately, due to its’ slow data rate, QoS is not provided but provides encryption to the packets exchanged.
One characteristic that can be pointed out is a feature called spatial diversity. As its terminal is not attached to a single base station, like most mobile cellular systems, when following the right deployment, a message sent by a terminal can be received by many base stations [70,81,99,129,133,134,146].

3. Technologies’ Comparison and Discussion

In this section, a detailed comparison of the technologies studied in this review will be presented.
These will be presented in four separate groups to reduce information overloading. The key groups are wired, wireless (short range), wireless (medium–long range), and dual mode (using both wired and wireless communications). The separation of wireless protocols into short-range and medium–long range is done due to the multiple technologies available.
Wireless technologies can be classified according to the distance they cover (Figure 4). These are:
  • Contact, also described as Proximity, is used for communications with distances up to 10 m.
  • Short, also known as WPAN (Wireless Personal Area Networks), covers areas from 10 m to 100 m.
  • Short/Medium networks are characterized as WLAN (Wireless Local Area Networks) and are used for areas from 100 m to 1000 m.
  • Medium (1–10 km), also described as WNAN (Wireless Neighborhood Area Networks), covers areas from 1 km to 10 km.
  • Long-range (10–100 km), also known as WWAN (Wireless Wide Area Networks), is used for greater distances of coverage from 10 km to 100 km.
Figure 4 illustrates all the technologies according to the distance they can cover.
Another classification of these technologies’ characteristics is:
  • User interaction;
  • Technical characteristics;
  • Data integrity;
  • Energy/Cost.

3.1. User Interaction

According to the information provided in Table 2, Table 3, Table 4 and Table 5, technologies, mostly wired, have been provided to consumers for over 20 years. These are BACnet, Bluetooth, Dupline, Ethernet, KNX, LonWorks, Wi-Fi, and X10. Three of them are known for computer and mobile device networking. These are the most popular ones in building automation. In Europe, the most known technologies in the market are Dupline and KNX, while in the US, they are BACnet, X10, and its successor, Insteon. Among the most recent technologies, are Zigbee and Z-Wave the most known ones.
The demands for wireless communications with better energy efficiency have led to the old ones being improved, as well as the appearance of new ones. Many of them are based on IEEE 802.15.4, as can be seen in Table 3, a standard for low cost and energy with high efficiency. Other technologies use their proprietary solutions.
While there are many solutions regarding home automation in the market, many of them are extending their capabilities by providing support for security systems, remote metering, and medical appliances. Most of them offer an API (Application Programming Interface) to developers for them to include their technology in their products.
Figure 5 is a schematic representation of the technologies’ popularity according to the literature findings displayed in the aforementioned tables.

3.2. Technical Characteristics

Technical characteristics are an important category to examine the technologies’ capabilities in more detail. According to “Trends in Home Automation Systems and Protocols” [53], all of them follow the OSI-7 reference model. According to its specification, it has seven layers named from the bottom up: physical, data link, network, transport, session, presentation, and application. These technologies either have the same structure or many of them merge two or more layers into one, described then as OSI-5 of OSI-4. Table 6, Table 7, Table 8 and Table 9 display these characteristics.
An important technical characteristic has to do with data rates. While in building automation and IoT, high data rates are not required; since the required data to be exchanged are low, it is considered an advantage for handling future network speed demands. This feature can be seen in technologies where Ethernet or fiber optics media are used. Apart from the Ethernet, which natively uses these mediums, technologies that also use this kind of medium include BACnet (Ethernet medium), KNX (Ethernet medium), and LonWorks (fiber optics). For wireless communications, Wi-Fi, also used for computer networks and multimedia applications, is the fastest one. Other wired and many wireless technologies can provide low data rates, capable of managing the traffic issued by IoT devices. There are also wireless, long-range ones that offer even lower data rates due to the low frequency used for transmission. Figure 6 is a representation of the data rate used in each technology, while Figure 7 illustrates the data rates according to the packet size used in each one.
Of course, the most devices that can be supported by network technology, the more versatile this technology is. Technologies based on the 802.15.4 standard (apart from Thread) can have a significant number of devices in their network, 64,000. Some, such as Bluetooth, Dupline, Z-Wave, and X10, offer a small number of connected devices, while others are capable of a very high number of devices, such as Ethernet, Insteon, Wi-Fi (v.6), LTE, NB-IoT, DASH7, mioty.
Mesh capability is offered by many networking technologies. It can be provided in two ways, in the first, a device has its data (routing) table with its neighborhood ones in the network it belongs to. If a connection is lost (dropped) to one element of the network, it can still be connected to the WPAN/WLAN using multi-hop mesh networking through their neighbor devices. All of the technologies (6LowPAN, Thread, Zigbee) based on the 802.15.4 standard for the PHY layer offer this feature, as well as BLE, DASH7, EnOcean, and Z-Wave. The second way is for each device to be connected to the same network, both wired and wirelessly. Insteon is the only technology supporting this feature. Mesh, though, can be achieved with the collaboration of wired and wireless technologies. Ethernet technology follows the same OSI model as Wi-Fi and also uses the TCP/IP protocol. Therefore, like a PC or a laptop, an IoT device can have both wired and wireless connections to the same network, and therefore mesh can be achieved. KNX can offer this feature by implementing its wired connection in collaboration with its wireless features or with the use of other technologies such as Zigbee or EnOcean. If one of these connections fails, connectivity to the network is provided by the other medium. A graphical representation of mesh technology in comparison with other known ones is shown in Figure 8 as indicated in [45].
Another important feature examined has to do with the communication protocols used for their communications. TCP/IP, also used in computer networks, is supported by most of the technologies presented. This is important for data exchanged from one technology to another. Other technologies exchange a very small amount of data; therefore, small frames or telegrams are used.
In each technology, variations can be seen regarding the distance covered and the data rates they achieve. Low-frequency wireless transmission data can be transmitted over large distances and consume a small amount of energy, but the network speeds are not so fast. These are 802.15.4-based technologies (6LowPAN, Thread, and Zigbee), Dash7, EnOcean, LoRa, NB-IoT, mioty, Sigfox, Wi-Fi (802.11ah), and Z-Wave. Technologies that use higher frequencies can only cover small distances, as the attenuation is significant while consuming significant energy, but in addition, they provide great data rates. In this category, LTE and Wi-Fi (802.11a/b/n/ac/ax) can cover large distances, and the energy used is significant. Bluetooth operates at a high frequency and can cover small distances, but with the introduction of BLE, the amount of energy used is low.
In wired mediums, technologies based on Ethernet can be used in large areas, but they consume a significant amount of energy. Other technologies, such as Dupline, can cover great distances with small energy usage, but with the impact of low data rates.
Heterogeneity and the ability of each technology to co-exist and cooperate with other technologies is a future study in this paper. As can be seen in Figure 9, many technologies, such as Ethernet and Wi-Fi, are provided through the gateways of other technologies to be configured and controlled by a computer or smart device.

3.3. Security/Quality

Security is a significant factor for each technology, and the assurance of the information exchanged through these technologies is necessary. Either at a basic level or enhanced, a form of security is provided by almost all of the technologies examined. Old technologies, e.g., Dupline and X10, are the only ones in this review that do not provide this feature. For wired mediums, BACnet provides encryption to the transport layer with the use of TLS (Transport Layer Security), which is a 256-bit frame used with the rest of the data payload exchanged to provide encryption. Ethernet technology, according to its’ 802.11AE definition, uses MACsec (Medium Access Control Security) and S-ARP (Secure Address Resolution Protocol) for data transfer security [148], applied in the Data link layer. In addition, in the network layer, with the use of TCP/IP protocol, IPSec encryption is provided, as well as TLS. In KNX, EIBsec is provided, making the technology’s communications safe. In Table 10, Table 11, Table 12 and Table 13, the main security and quality features are displayed.
Technologies using wireless transmission provide encryption to lower OSI layers and physical and data links. The most common method used is 128-bit size AES used by following the 802.15.4 standard. There are also long-range ones, such as Dash7, LoRa, and mioty, which use the same method. Bluetooth, Insteon, LTE, NB-IoT, and WiFi provide enhanced encryption with 256 bit. EnOcean provides a different method of encryption as it uses VAES (Variable AES) for providing extra security. Sigfox, as a low data rate technology, uses as a security mechanism for Key Generation and MAC verification sequence.
Apart from data security, integrity is an important feature of home automation technologies. Most of them use CRC (Cyclic Redundancy Check) to provide QoS (Quality of Service). This feature is provided by a set of bits, usually at the end of the message, as a verification of the data being transmitted or received. If this information is confirmed by the counted data, then the information has been exchanged successfully. There are variations of the bits used for this feature, KNX (with the use of TCP/IP protocol), EnOcean, and Z-Wave provide 8. Technologies with more data exchanged, such as6LoWPAN, BACnet, Bluetooth, Insteon, Thread, and Zigbee, use 16 bit. There are also ones proving an even larger number of bits (32), e.g., BACnet, Ethernet, KNX, and WiFi. Low-data transmission technologies, e.g., Dupline, LoRa, Sigfox, and X10, do not provide integrity mechanisms.
NB-IoT varies in this category as it uses one of the algorithms (SNOW 3G or ATR-128 CMAC) with 4 Byte MIC. The first one is used by 3GPP to provide confidentiality of data based on UMTS (Universal Mobile Telecommunications Service) mobile networks [138]. It consists of two modules interacting with each other, using 32-bit registers [139]. ATR-128 is a proprietary algorithm performing 128-bit encryption, while CMAC is MAC (Media Access Control) scheme based on block-cipher providing data integrity and authenticity. MIC is a mechanism that ensures, through hash in the messages, that messages have not been altered.
Mioty, with its TSMA technique, provides a QoS mechanism, as it is designed for zero packet loss. If packets are lost, it can tolerate up to 50% of the packets to arrive to decode the information [134]. In Figure 10, the QoS and Encryption characteristics of each technology are presented.
As depicted in Figure 11, wireless technologies use specific transmission bands. As a result, in a specific frequency, interference can occur, affecting the quality of data exchanged from one technology to a co-existing one. For this phenomenon to be avoided, each technology uses a specific mechanism to ensure data integrity. Most of them (6LoWPAN, Dash7, Thread, WiFi, Z-Wave, Zigbee) use CSMA/CA (Carrier Sense Multiple Access—Collision Avoidance). It is applied in the Data Link OSI layer and performs a specific procedure to ensure the data is transmitted without collisions [97]. Another technique used for interference avoidance is frequency hopping. If the data exchanged use the same band, the transmitted channel (frequency) is changed to the next supported/available one [150].

3.4. Energy/Cost

Energy consumption constitutes one of IoT and home automation’s major goals. Every improvement presented for this technology aims for this characteristic. This feature is most needed for wireless-based technologies, where a component can be in an area without a stable power supply; therefore, batteries must be used. Therefore, there is a need for them to consume a small amount of energy for their operation and to have better efficiency (power loss). All wireless technologies provide this feature except Wi-Fi (802.11 a/b/n/ac/ax) and LTE, as they are mainly used for high data rates (multimedia applications), and therefore more energy is needed. Transmission power is very low in technologies that use short-range (0–100 mW) features that vary according to the obstacles in between transceivers causing interference.
Wired medium technologies, as they are constantly connected to a power supply, are not so focused on low energy consumption. As in wireless, apart from the ones used for high data rates, energy consumption is small.
The best technology regarding power consumption and efficiency is EnOcean. As an energy harvesting technology, the power needed for its operation is produced by it; therefore, the overall number is zero. Its’ transmission power (TX-power) is still the lowest at about 50 μW. Most of the short-range wireless technologies also have this factor in very low numbers from 0–100 according to the distance and the obstacles blocking their transmission. Two factors have an impact on the power transmitted and increase its value: the frequency used (the greater, the more power is needed) and the distance they cover. Therefore, long-range technologies such as DASH7, Sigfox, LTE, NB-IoT, LoRa, and mioty values greater than 10 mW boost the signal to cover the distance, with the first two being the most efficient while the following two (due to the high data transmission and their design) consume the most (200 mW). In contrast to the TX-power, NB-IoT, LoRa, and mioty provide very low consumption, while Sigfox has high energy consumption, and DASH7 is low. In Table 14, Table 15, Table 16 and Table 17, energy and cost features are displayed.
A very important characteristic examined is cost. Almost all of the technologies based on the 802.15.4 standard are open-sourced; therefore, there is no need to buy licenses or extra fees in developing/operating them, so they can be available to everyone. Z-Wave is a proprietary technology, requiring device certification, which adds extra cost to the total value of a product. EnOcean devices are manufactured by a limited number of contractors, as it is not very popular yet. As a result, their value is a little higher than their competition. Wi-Fi and Bluetooth (in many cases) technologies are available in almost every end-user device in the market and can be used without the extra cost of acquiring an extra device or gateway. As a result, there are many solutions available at very low prices. Technologies using BLE standard to better manage their connected devices, require a gateway, adding a small additional cost. Wired technologies, which need a legitimate medium (hardware), are more expensive than wireless ones, which can be further increased with better quality cabling. As technologies become more popular, there are new solutions of devices introduced that can serve as a gateway (multi-mode gateway) in more than one smart home technology, decreasing the cost of acquiring one for each of them. In Figure 12, a schematic representation of the technology behavior regarding energy and cost is given.

4. Discussion

There are many technologies offering a variety of capabilities in various applications.
This review is focused on covering a more detailed and extensive coverage of the most known and used technologies available in WSN and home automation. This article is based on reviews focusing on statistical usage of home automation or IoT, on a number of technologies, or even some technical characteristics. This article is an attempt to gather most of the information available regarding popularity, technical characteristics, QoS, and even a small approach regarding energy/cost. It is an effort to compare many technologies at once.
The advantages of each technology vary. There are ones that focus on energy consumption, aimed at Green Energy, a term very popular nowadays. Some technologies combine this characteristic and also provide greater coverage. There are others providing higher data rates but with greater energy costs. Mesh propagation is another useful feature provided by technologies, ensuring connectivity, even if one network link is not working.
Security and quality are important factors for intercommunication between devices and ever for the end user. Proper data exchange is provided by most of the technologies covered in this review. There are also very important issues that need to be examined, such as security and more cyber-attacks. There is a lot of research available in this area, but this article does not provide much information as it would be expanding exponentially.

5. Conclusions

This review provides a detailed comparison of all technologies used in IoT-based networking for home automation. It is an effort to support end-users as well as installers and administrators to determine which one of these technologies is the most suitable, according to specific metrics. This survey, according to the knowledge of its authors, is the most complete when compared to other similar reviews. Specifically, various criteria have been taken into consideration, such as popularity (adoption) and technical characteristics such as network size and network speed, quality, and cost. Another factor examined is heterogeneity and the ability of wireless technologies to avoid collisions when using the same frequency as other ones.
The most known technologies in IoT-based networking for home automation were examined from the older ones to the newly introduced ones. Some of them are open-sourced, while others require a license to be purchased. Technologies such as KNX, Ethernet, WiFi, BLE, and Zigbee are quite popular with consumers, while LoRa, EnOcean, and mioty are rapidly increasing their popularity. Most focus on providing low-cost solutions and cover wide ranges providing low data rates. In addition, almost all of them provide a quality mechanism to ensure proper data exchange and security features for end-to-end information transmission. Regarding energy efficiency, technologies such as EnOcean and mioty provide high availability, but if cost is taken into account, LoRa is a more affordable choice. It can provide high efficiency, low budget, and satisfactory data rates.
Whilst there is a variety of solutions provided, and new ones are introduced every year, there is a need for these technologies to have a common interface for ensuring communication among them. This can be achieved with a method or a universal interface for managing each of them. This task is not simple, and an approach needs to be made focusing on the common characteristics these technologies have, such as the medium used (power line, Ethernet, frequency), the OSI layers [53] approach they use, and the protocol for exchanging their data. An approach using the TCP/IP v.6 protocol is a solution to be examined with the use of simulation tools and lab tests.

Author Contributions

Conceptualization, V.A.O. and S.D.K.; methodology, V.A.O. and S.D.K.; formal analysis, P.P. and D.K.; investigation, V.A.O.; resources, V.A.O.; data curation, V.A.O., P.P. and D.K.; writing—original draft preparation, V.A.O.; writing—review and editing, P.P. and D.K.; visualization, D.P.; supervision S.D.K.; project administration, S.D.K.; funding acquisition, S.D.K. and D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was waived by MDPI.

Data Availability Statement

No new data were created.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Judy, J.W. Microelectromechanical Systems (MEMS): Fabrication, Design and Applications. Smart Mater. Struct. 2001, 10, 1115–1134. [Google Scholar] [CrossRef] [Green Version]
  2. Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. A Survey on Sensor Networks. IEEE Commun. Mag. 2002, 40, 104–112. [Google Scholar] [CrossRef] [Green Version]
  3. Yick, J.; Mukherjee, B.; Ghosal, D. Wireless Sensor Network Survey. Comput. Netw. 2008, 52, 2292–2330. [Google Scholar] [CrossRef]
  4. Kandris, D.; Nakas, C.; Vomvas, D.; Koulouras, G. Applications of Wireless Sensor Networks: An Up—to—Date Survey. Appl. Syst. Innov. 2020, 3, 14. [Google Scholar] [CrossRef] [Green Version]
  5. Batra, I.; Verma, S.; Kavita; Alazab, M. A Lightweight IoT-Based Security Framework for Inventory Automation Using Wireless Sensor Network. Int. J. Commun. Syst. 2020, 33, e4228. [Google Scholar] [CrossRef]
  6. Papadakis, N.; Koukoulas, N.; Christakis, I.; Stavrakas, I.; Kandris, D. An Iot-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities. Smart Cities 2021, 4, 47. [Google Scholar] [CrossRef]
  7. Khedo, K.K.; Bissessur, Y.; Goolaub, D.S. An Inland Wireless Sensor Network System for Monitoring Seismic Activity. Futur. Gener. Comput. Syst. 2020, 105, 520–532. [Google Scholar] [CrossRef]
  8. Pantazis, N.A.; Nikolidakis, S.A.; Kandris, D.; Vergados, D.D. An Automated System for Integrated Service Management in Emergency Situations. In Proceedings of the 2011 15th Panhellenic Conference on Informatics, Kastoria, Greece, 30 September–2 October 2011; pp. 154–157. [Google Scholar] [CrossRef]
  9. Nikolidakis, S.A.; Kandris, D.; Vergados, D.D.; Douligeris, C. Energy Efficient Automated Control of Irrigation in Agriculture by Using Wireless Sensor Networks. Comput. Electron. Agric. 2015, 113, 154–163. [Google Scholar] [CrossRef]
  10. Bashir, N.; Boudjit, S.; Zeadally, S. A Closed-Loop Control Architecture of UAV and WSN for Traffic Surveillance on Highways. Comput. Commun. 2022, 190, 78–86. [Google Scholar] [CrossRef]
  11. Evangelakos, E.A.; Kandris, D.; Rountos, D.; Tselikis, G.; Anastasiadis, E. Energy Sustainability in Wireless Sensor Networks: An Analytical Survey. J. Low Power Electron. Appl. 2022, 12, 65. [Google Scholar] [CrossRef]
  12. Nakas, C.; Kandris, D.; Visvardis, G. Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey. Algorithms 2020, 13, 72. [Google Scholar] [CrossRef] [Green Version]
  13. Alhasnawi, B.N.; Jasim, B.H.; Mansoor, R.; Alhasnawi, A.N.; Rahman, Z.A.S.A.; Haes Alhelou, H.; Guerrero, J.M.; Dakhil, A.M.; Siano, P. A New Internet of Things Based Optimization Scheme of Residential Demand Side Management System. IET Renew. Power Gener. 2022, 16, 1992–2006. [Google Scholar] [CrossRef]
  14. Farsi, M.; Elhosseini, M.A.; Badawy, M.; Arafat Ali, H.; Zain Eldin, H. Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey. IEEE Access 2019, 7, 28940–28954. [Google Scholar] [CrossRef]
  15. Kandris, D.; Vergados, D.J.; Vergados, D.D.; Tzes, A. A Routing Scheme for Congestion Avoidance in Wireless Sensor Networks. In Proceedings of the 6th annual IEEE Conference on Automation Science and Engineering Marriott Eaton Centre Hotel Toronto, Toronto, ON, Canada, 21–24 August 2010. [Google Scholar]
  16. Ploumis, S.E.; Sgora, A.; Kandris, D.; Vergados, D.D. Congestion Avoidance in Wireless Sensor Networks: A Survey. In Proceedings of the 2012 16th Panhellenic Conference on Informatics, Piraeus, Greece, 5–7 October 2012; pp. 234–239. [Google Scholar] [CrossRef]
  17. Kandris, D.; Alexandridis, A.; Dagiuklas, T.; Panaousis, E.; Vergados, D.D. Multiobjective Optimization Algorithms for Wireless Sensor Networks. Wirel. Commun. Mob. Comput. 2020, 2020, 4652801. [Google Scholar] [CrossRef]
  18. Sharma, N.; Singh, B.M.; Singh, K. QoS-Based Energy-Efficient Protocols for Wireless Sensor Network. Sustain. Comput. Inform. Syst. 2021, 30, 100425. [Google Scholar] [CrossRef]
  19. Tarnaris, K.; Preka, I.; Kandris, D.; Alexandridis, A. Coverage and K-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparsative Study. Electronics 2020, 9, 675. [Google Scholar] [CrossRef] [Green Version]
  20. Yu, J.Y.; Lee, E.; Oh, S.R.; Seo, Y.D.; Kim, Y.G. A Survey on Security Requirements for WSNs: Focusing on the Characteristics Related to Security. IEEE Access 2020, 8, 45304–45324. [Google Scholar] [CrossRef]
  21. Farooq, M.U.; Waseem, M.; Mazhar, S.; Khairi, A.; Kamal, T. A Review on Internet of Things (IoT). Int. J. Comput. Appl. 2015, 113, 135–144. [Google Scholar] [CrossRef]
  22. Mohamad Noor, M.B.; Hassan, W.H. Current Research on Internet of Things (IoT) Security: A Survey. Comput. Netw. 2019, 148, 283–294. [Google Scholar] [CrossRef]
  23. Lee, S.K.; Bae, M.; Kim, H. Future of IoT Networks: A Survey. Appl. Sci. 2017, 7, 1072. [Google Scholar] [CrossRef]
  24. Pirbhulal, S.; Zhang, H.; Alahi, M.E.E.; Ghayvat, H.; Mukhopadhyay, S.C.; Zhang, Y.T.; Wu, W. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69. [Google Scholar] [CrossRef] [PubMed]
  25. Risteska Stojkoska, B.L.; Trivodaliev, K.V. A Review of Internet of Things for Smart Home: Challenges and Solutions. J. Clean. Prod. 2017, 140, 1454–1464. [Google Scholar] [CrossRef]
  26. Hammi, B.; Zeadally, S.; Khatoun, R.; Nebhen, J. Survey on Smart Homes: Vulnerabilities, Risks, and Countermeasures. Comput. Secur. 2022, 117, 102677. [Google Scholar] [CrossRef]
  27. Lee, E.A.; Seshia, S.A. Introduction to Embedded Systems. A Cyber-Physical Systems Approach, 2nd ed.; Mit Press: Cambridge, MA, USA, 2017; Volume 195, ISBN 978-0-557-70857-4. [Google Scholar]
  28. Koshy, S.N.; Kamsin, I.F.B.; Zainal, N.K. A Comprehensive Review on Smart IoT Applications. In Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021), Bangalore, India, 6–7 August 2021; Volume 4, pp. 548–553. [Google Scholar] [CrossRef]
  29. BacNET BACnet—The New Standard Protocol. Available online: https://bacnet.org/wp-content/uploads/sites/4/2022/06/BACnet-The-New-Standard-Protocol-1.pdf (accessed on 5 February 2023).
  30. Carlo Gavazzi, S.A. Carlo Gavazzi Automation Components. Available online: http://gavazziautomation.com/nsc/ES/ES/company_profile (accessed on 5 February 2023).
  31. KNX Association KNX Association Webpage. Available online: http://www.knx.org/knx-en/index.php (accessed on 5 February 2023).
  32. Bluetooth.com Medical|Bluetooth Technology Website. Available online: https://www.bluetooth.com/ (accessed on 5 February 2023).
  33. Gratton. D A WiFi Alliance. Available online: http://www.wi-fi.org/certification/programs (accessed on 5 February 2023).
  34. ZigBee Alliance. Available online: http://zigbee.com.my/ (accessed on 5 February 2023).
  35. Z-Wave Safer, Smarter Homes Start with Z-Wave. Available online: https://www.z-wave.com/ (accessed on 5 February 2023).
  36. 6lowpan FrontPage—6lowpan Wiki. Available online: http://6lowpan.tzi.org/ (accessed on 5 February 2023).
  37. EnOcean Sustainable IoT. Available online: https://www.enocean.com/en/ (accessed on 5 February 2023).
  38. Insteon. Available online: https://www.insteon.com/ (accessed on 5 February 2023).
  39. Threadgroup Home—Threadgroup. Available online: https://www.threadgroup.org/ (accessed on 1 April 2019).
  40. Apple Inc. Apple Developer Documentation. Available online: https://developer.apple.com/documentation/ (accessed on 5 February 2023).
  41. SmartThings Inc. SmartThings. Add a Little Smartness to Your Things. Available online: https://www.smartthings.com/about (accessed on 5 February 2023).
  42. Alaa, M.; Zaidan, A.A.; Zaidan, B.B.; Talal, M.; Kiah, M.L.M. A Review of Smart Home Applications Based on Internet of Things. J. Netw. Comput. Appl. 2017, 97, 48–65. [Google Scholar] [CrossRef]
  43. Singh, P.; Chotalia, K.; Pingale, S.; Kadam, S. A Review Paper on Smart GSM Based Home Automation System. Int. Res. J. Eng. Technol. 2016, 3, 1838–1843. [Google Scholar]
  44. Withanage, C.; Ashok, R.; Yuen, C.; Otto, K. A Comparison of the Popular Home Automation Technologies. In Proceedings of the 2014 IEEE Innovative Smart Grid Technologies—Asia (ISGT ASIA), Kuala Lumpur, Malaysia, 20–23 May 2014; pp. 600–605. [Google Scholar] [CrossRef]
  45. Orfanos, V.A.; Kaminaris, S.D.; Piromalis, D.; Papageorgas, P. Smart Home Automation in the IoT Era: A Communication Technologies Review. AIP Conf. Proc. 2020, 2307, 020054. [Google Scholar] [CrossRef]
  46. Michael Newmanm, H. BACnet Basics User’s Guide. 2013. Available online: https://www.ashrae.org/File%20Library/Technical%20Resources/Bookstore/BACnet-explained-Pt1.pdf (accessed on 2 February 2023).
  47. Li, Y.C.; Hong, S.H. BACnet-EnOcean Smart Grid Gateway and Its Application to Demand Response in Buildings. Energy Build. 2014, 78, 183–191. [Google Scholar] [CrossRef]
  48. Fisher, D.; Isler, B.; Osborne, M. BACnet Secure Connect A Secure Infrastructure for Building Automation. Available online: https://www.ashrae.org/file%20library/technical%20resources/bookstore/bacnet-sc-whitepaper-v15_final_20190521.pdf (accessed on 27 March 2023).
  49. Haakenstad, L.K. The Open Protocol Standard for Computerized Building Systems: BACnet. In Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328), Kohala Coast, HI, USA, 22–27 August 1999; Volume 2, pp. 1585–1590. [Google Scholar]
  50. Marinakis, V.; Doukas, H.; Karakosta, C.; Psarras, J. An Integrated System for Buildings’ Energy-Efficient Automation: Application in the Tertiary Sector. Appl. Energy 2013, 101, 6–14. [Google Scholar] [CrossRef]
  51. Marinakis, V.; Doukas, H.; Psarras, J.; Adamopoulos, A. Interactive Software for Building Automation Systems towards Effective Energy and Eenvıronmental Management. Available online: http://www.carlogavazzi.gr/html/uk/news/documents/InteractiveSoftwareforBuildingAutomationSystems.pdf (accessed on 27 March 2023).
  52. Alliance, E. TEF 2013—′The Future of Ethernet′ Keynote Videos—Ethernet Alliance. Available online: http://ethernetalliance.org/tef-2013-the-future-of-ethernet-keynote/ (accessed on 5 February 2023).
  53. Orfanos, V.; Kaminaris, S.D.; Piromalis, D.; Papageorgas, P. Trends in Home Automation Systems and Protocols. AIP Conf. Proc. 2019, 2190, 020049. [Google Scholar] [CrossRef]
  54. P802.3ck—Standard for Ethernet Amendment: Physical Layer Specifications and Management Parameters for 100 Gb/s, 200 Gb/s, and 400 Gb/s Electrical Interfaces Based on 100 Gb/s Signaling. Available online: https://standards.ieee.org/project/802_3ck.html (accessed on 5 February 2023).
  55. P802.3cp—Standard for Ethernet Amendment: Bidirectional 10 Gb/s, 25 Gb/s, and 50 Gb/s Optical Access PHYs. Available online: https://standards.ieee.org/project/802_3cp.html (accessed on 5 February 2023).
  56. Jain, P.C. Recent Trends in next Generation Terabit Ethernet and Gigabit Wireless Local Area Network. In Proceedings of the 2016 International Conference on Signal Processing and Communication (ICSC), Noida, India, 26–28 December 2016; Volume 201314, pp. 106–110. [Google Scholar] [CrossRef]
  57. Ethernet Alliance Ethernet Alliance|New 2018 Ethernet Roadmap Looks to Future Speeds of 1.6 Terabits/S. Available online: https://ethernetalliance.org/library/article/new-2018-ethernet-roadmap-looks-to-future-speeds-of-1-6-terabitss/ (accessed on 17 July 2019).
  58. Feldman, D.; Oliva, V. Overview of 802.3bt—Power over Ethernet Standard. 2018, 6. Available online: https://ethernetalliance.org/wp-content/uploads/2018/04/WP_EA_Overview8023bt_FINAL.pdf (accessed on 27 March 2023).
  59. Ethernet Technologies: The Pursuit of Multi-Vendor Interoperability. Available online: http://iwcssite.conferencespot.org/polopoly_fs/1.4234330.1538745377!/fileserver/file/905016/filename/1-2.pdf (accessed on 27 March 2023).
  60. Bovet, G.; Hennebert, J. Web-of-Things Gateways for KNX and EnOcean Networks. In Proceedings of the International Conference on Cleantech for Smart Cities & Buildings from Nano to Urban Scale (CISBAT 2013), hal-00872187. Lausanne, Switzerland, 4–6 September 2013. [Google Scholar]
  61. Oudji, S.; Courrèges, S.; Paillard, J.N.; Magneron, P.; Meghdadi, V.; Brauers, C.; Kays, R. Radiofrequency Interconnection between Smart Grid and Smart Meters Using KNX-RF and 2.4 GHz Standard Protocols for Efficient Home Automation Applications. J. Commun. 2015, 10, 812–820. [Google Scholar] [CrossRef]
  62. Bevers, I. Intelligence at the Edge Part 1: The Edge Node (2018714). 2019, 1–6. Available online: https://www.analog.com/media/en/technical-documentation/tech-articles/Intelligence-at-the-Edge-Part-1-The-Edge-Node.pdf (accessed on 27 March 2023).
  63. Chen, Q.; Zhong, Q.; Sun, D.; Li, R.; Niu, L.; Ding, L. ETN-Ethernet Transport Network for 5G Mobile Transport, Metro, and DCI Network. In Proceedings of the 2018 IEEE International Conference on Communication Systems (ICCS), Chengdu, China, 19–21 December 2018; pp. 332–336. [Google Scholar] [CrossRef]
  64. Mendes, T.D.P.; Godina, R.; Rodrigues, E.M.G.; Matias, J.C.O.; Catalão, J.P.S. Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources. Energies 2015, 8, 7279–7311. [Google Scholar] [CrossRef] [Green Version]
  65. Toschi, G.M.; Campos, L.B.; Cugnasca, C.E. Home Automation Networks: A Survey. Comput. Stand. Interfaces 2017, 50, 42–54. [Google Scholar] [CrossRef] [Green Version]
  66. Echelon Corporation Introduction to the LonWorks® Platform Revision 2. Echelon Corp. 2009. Available online: https://www.echelon.com/assets/blt893a8b319e8ec8c7/078-0183-01B_Intro_to_LonWorks_Rev_2.pdf (accessed on 27 March 2023).
  67. X10 The Source for X10. X10 Pro Genuine Products. Available online: https://www.x10.com/ (accessed on 5 February 2023).
  68. Al-sarawi, S.; Anbar, M.; Alieyan, K.; Alzubaidi, M. Internet of Things (IoT) Communication Protocols. In Proceedings of the 2017 8th International conference on information technology (ICIT), Amman, Jordan, 17–18 May 2017. [Google Scholar]
  69. Raza, S.; Misra, P.; He, Z.; Voigt, T. Building the Internet of Things with Bluetooth Smart. Ad. Hoc. Netw. 2017, 57, 19–31. [Google Scholar] [CrossRef]
  70. Al-Kashoash, H.A.A.; Kemp, A.H. Comparison of 6LoWPAN and LPWAN for the Internet of Things. Aust. J. Electr. Electron. Eng. 2016, 13, 268–274. [Google Scholar] [CrossRef]
  71. Huang, Z.; Yuan, F. Implementation of 6LoWPAN and Its Application in Smart Lighting. J. Comput. Commun. 2015, 3, 80–85. [Google Scholar] [CrossRef] [Green Version]
  72. Ee, G.K.; Ng, C.K.; Noordin, N.K.; Ali, B.M. A Review of 6LoWPAN Routing Protocols. Proc. Asia-Pacific Adv. Netw. 2010, 30, 71. [Google Scholar] [CrossRef] [Green Version]
  73. Kamilaris, A. Enabling Smart Homes Using Web Technologies. Ph.D. Thesis, University of Cyprus, Nicosia, Cyprus. Available online: http://gnosis.library.ucy.ac.cy/handle/7/39570 (accessed on 27 March 2023).
  74. Bluetooth.com Archived Specifications|Bluetooth Technology Website. Available online: https://www.bluetooth.com/specifications/archived-specifications (accessed on 5 February 2023).
  75. Lopez, N.A.T.; Pasaoa, J.R.B.; Parado, J.A.; Morales, J.O. A Comparative Study of Thread Against ZigBee, Z-Wave, Bluetooth, and Wi-Fi as a Home-Automation Networking Protocol. Research Proposal 2016. [Google Scholar] [CrossRef]
  76. Learn About Bluetooth Mesh Networking. Available online: https://www.bluetooth.com/learn-about-bluetooth/recent-enhancements/mesh/ (accessed on 5 February 2023).
  77. Sivasankari, A.; Sudarvizhi, S.; Sarala, L. A Comparative Study of Wireless Technologies Based on Home Automation Bluetooth Low Energy, Zigbee, Insteon and ENOCEAN. Int. J. Comput. Sci. Inf. Technol. Res. 2014, 2, 255–259. [Google Scholar]
  78. Kambourakis, G.; Kolias, C.; Geneiatakis, D.; Karopoulos, G.; Makrakis, G.M.; Kounelis, I. A State-of-the-Art Review on the Security of Mainstream IoT Wireless PAN Protocol Stacks. Symmetry 2020, 12, 579. [Google Scholar] [CrossRef] [Green Version]
  79. Gunge, V.S.; Yalagi, P.S. Smart Home Automation: A Literature Review. Int. J. Comput. Appl. 2016, 6–10. [Google Scholar] [CrossRef]
  80. Piromalis, D.D.; Arvanitis, K.G.; Sigrimis, N. DASH7 Mode 2: A Promising Perspective for Wireless Agriculture; IFAC: New York, NY, USA, 2013; Volume 46, ISBN 9783902823441. [Google Scholar]
  81. Oliveira, L.; Rodrigues, J.J.P.C.; Kozlov, S.A.; Rabêlo, R.A.L.; de Albuquerque, V.H.C. MAC Layer Protocols for Internet of Things: A Survey. Future Internet 2019, 11, 16. [Google Scholar] [CrossRef] [Green Version]
  82. Zachariah, T.; Klugman, N.; Campbell, B.; Adkins, J.; Jackson, N.; Dutta, P. The Internet of Things Has a Gateway Problem. In Proceedings of the HotMobile ′15: The 16th International Workshop on Mobile Computing Systems and Applications, Santa Fe, NM, USA, 12–13 February 2015; 20 February 2015; pp. 27–32. [Google Scholar] [CrossRef] [Green Version]
  83. Baert, M.; Rossey, J.; Shahid, A.; Hoebeke, J. The Bluetooth Mesh Standard: An Overview and Experimental Evaluation. Sensors 2018, 18, 2409. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Collotta, M.; Pau, G. Bluetooth for Internet of Things: A Fuzzy Approach to Improve Power Management in Smart Homes. Comput. Electr. Eng. 2015, 44, 137–152. [Google Scholar] [CrossRef]
  85. EnOcean Security of EnOcean Radio Networks V2.5. 2018, 1–37. Available online: https://www.enocean-alliance.org/wp-content/uploads/2019/04/Security-of-EnOcean-Radio-Networks-v2_5.pdf (accessed on 27 March 2023).
  86. Marksteiner, S.; Jimenez, V.J.E.; Valiant, H.; Zeiner, H. An Overview of Wireless IoT Protocol Security in the Smart Home Domain. In Proceedings of the 2017 Internet of Things Business Models, Users, and Networks, Copenhagen, Denmark, 23–24 November 2017; pp. 1–8. [Google Scholar] [CrossRef] [Green Version]
  87. Rahmat, M.K.; Hor, K.C.W. Wireless Technology in Building Automation System. In AIP Conference Proceedings; AIP Publishing LLC: Melville, NY, USA, 2019; Volume 2129, p. 020135. [Google Scholar] [CrossRef]
  88. Sha, S.; Kaur, H. Simulation Study of Zigbee and Enocean Home Automation Standards. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2014, 3, 11711–11719. [Google Scholar] [CrossRef]
  89. Torres, G.G.; Bayan Henriques, R.V.; Pereira, C.E.; Müller, I. An EnOcean Wearable Device with Fall Detection Algorithm Integrated with a Smart Home System. IFAC-PapersOnLine 2018, 51, 9–14. [Google Scholar] [CrossRef]
  90. Unwala, I.; Taqvi, Z.; Lu, J. Thread: An IoT Protocol. In Proceedings of the 2018 IEEE Green Technologies Conference (GreenTech), Austin, TX, USA, 4–6 April 2018; pp. 161–167. [Google Scholar] [CrossRef]
  91. Lan, D.; Pang, Z.; Fischione, C.; Liu, Y.; Taherkordi, A.; Eliassen, F. Latency Analysis of Wireless Networks for Proximity Services in Smart Home and Building Automation: The Case of Thread. IEEE Access 2019, 7, 4856–4867. [Google Scholar] [CrossRef]
  92. Silicon Labs UG103.11: Thread Fundamentals, Rev.0.7. 2017, 1–25. Available online: https://www.silabs.com/documents/public/user-guides/ug103-11-fundamentals-thread.pdf (accessed on 27 March 2023).
  93. Alm, A. Internet of Things Mesh Network Using the Thread Networking Protocol; URN: urn:nbn:se:kau:diva-70809; Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013): Karlstad, Sweden, 2019; p. 64. [Google Scholar]
  94. Sandre, R. Thread and Zigbee for Home and Building Automation; Texas Instruments: Dallas, TX, USA, 2018. [Google Scholar]
  95. Kim, H.S.; Kumar, S.; Culler, D.E. Thread/Openthread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things. IEEE Commun. Mag. 2019, 57, 55–61. [Google Scholar] [CrossRef]
  96. Unwala, I.; Zafar, T.; Lu, J. IoT Security- ZWave and Thread. In Proceedings of the 2018 IEEE Green Technologies Conference (GreenTech), Austin, TX, USA, 4–6 April 2018; pp. 176–182. [Google Scholar]
  97. Lan, D. Experimental Study of Thread Mesh Network for Wireless Building Automation Systems; URN: urn:nbn:se:kth:diva-194440; KTH, School of Electrical Engineering (EES): Stockholm, Sweden, 2016. [Google Scholar]
  98. Mahmoud, M.S.; Mohamad, A.A.H. A Study of Efficient Power Consumption Wireless Communication Techniques/ Modules for Internet of Things (IoT) Applications. Adv. Internet Things 2016, 6, 19–29. [Google Scholar] [CrossRef] [Green Version]
  99. Ayoub, W.; Samhat, A.E.; Nouvel, F.; Mroue, M.; Prevotet, J. christophe Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs Standards and Supported Mobility. IEEE Commun. Surv. Tutorials 2018, 21, 1561–1581. [Google Scholar] [CrossRef] [Green Version]
  100. Kashyap, M.; Sharma, V.; Gupta, N. Taking MQTT and NodeMcu to IOT: Communication in Internet of Things. Procedia Comput. Sci. 2018, 132, 1611–1618. [Google Scholar] [CrossRef]
  101. Paetz, C. Z-Wave Basics:Remote Control in Smart Homes; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, 2013; ISBN 1490537368. [Google Scholar]
  102. Fuller, J.D.; Ramsey, B.W.; Rice, M.J.; Pecarina, J.M. Misuse-Based Detection of Z-Wave Network Attacks. Comput. Secur. 2017, 64, 44–58. [Google Scholar] [CrossRef] [Green Version]
  103. Open-ZWave OZW Utilities. Available online: http://www.openzwave.com/ (accessed on 20 December 2022).
  104. Badenhop, C.W.; Graham, S.R.; Ramsey, B.W.; Mullins, B.E.; Mailloux, L.O. The Z-Wave Routing Protocol and Its Security Implications. Comput. Secur. 2017, 68, 112–129. [Google Scholar] [CrossRef]
  105. Fouladi, B.; Ghanoun, S. Security Evaluation of the Z-Wave Wireless Protocol. Black Hat 2013, 24, 1–2. [Google Scholar]
  106. Paetz, C. Z-Wave Essentials; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, 2017; ISBN 9781545394540, 1545394547. [Google Scholar]
  107. ZigBee Specification. ZigBee Alliance. Available online: https://zigbeealliance.org/wp-content/uploads/2019/11/docs-05-3474-21-0csg-zigbee-specification.pdf (accessed on 27 March 2023).
  108. Del-Valle-Soto, C.; Valdivia, L.J.; Velázquez, R.; Rizo-Dominguez, L.; López-Pimentel, J.C. Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network. Energies 2019, 12, 3135. [Google Scholar] [CrossRef] [Green Version]
  109. Rai, A.; Vineeta, N. A Review on Wireless Home Automation Systems Based on Zigbee Technology. Int. J. Recent Innov. Trends Comput. Commun. 2017, 5, 40–45. [Google Scholar]
  110. Froiz-Míguez, I.; Fernández-Caramés, T.M.; Fraga-Lamas, P.; Castedo, L. Design, Implementation and Practical Evaluation of an Iot Home Automation System for Fog Computing Applications Based on MQTT and ZigBee-WiFi Sensor Nodes. Sensors 2018, 18, 2660. [Google Scholar] [CrossRef] [Green Version]
  111. Xiao, Y.; Sun, T.; Tung, L.-P.; Chen, L.-J.; Liang, N.-C.; Li, F.H.; Chen, H. The Impact of Node Heterogeneity on ZigBee Network Routing. In Handbook on Sensor Networks; World Scientific: Singapore, 2010; pp. 167–180. [Google Scholar]
  112. Dash, A.; Pal, S.; Hegde, C. Ransomware Auto-Detection In IoT Devices Using Machine Learning; ICBAI, 2018, 8, 1–10. Available online: https://www.researchgate.net/publication/329936193_Ransomware_Auto-Detection_In_IoT_Devices_Using_Machine_Learning (accessed on 28 March 2023).
  113. Shahjalal, M.; Hasan, M.K.; Islam, M.M.; Alam, M.M.; Ahmed, M.F.; Jang, Y.M. An Overview of AI-Enabled Remote Smart- Home Monitoring System Using LoRa. In Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19–21 February 2020; pp. 510–513. [Google Scholar] [CrossRef]
  114. Aragüés, A.; Martínez, I.; Del Valle, P.; Muñoz, P.; Escayola, J.; Trigo, J.D. Trends in Entertainment, Home Automation and e-Health: Toward Cross-Domain Integration. IEEE Commun. Mag. 2012, 50, 160–167. [Google Scholar] [CrossRef]
  115. del Campo, G.; Gomez, I.; Cañada, G.; Piovano, L.; Santamaria, A. Guidelines and Criteria for Selecting the Optimal Low-Power Wide-Area Network Technology; Academic Press. 2020, 281-305. Academic Press: Cambridge, MA, USA, 2020; pp. 281–305. [Google Scholar] [CrossRef]
  116. Weyn, M.; Ergeerts, G.; Berkvens, R.; Wojciechowski, B.; Tabakov, Y. DASH7 Alliance Protocol 1.0: Low-Power, Mid-Range Sensor and Actuator Communication. In Proceedings of the 2015 IEEE Conference on Standards for Communications and Networking (CSCN), Tokyo, Japan, 28–30 October 2015; pp. 54–59. [Google Scholar] [CrossRef]
  117. Dash7 Dash7 Alliance. Available online: https://www.dash7-alliance.org/ (accessed on 5 February 2023).
  118. Salman, T.; Jain, R. A Survey of Protocols and Standards for Internet of Things. arXiv 2019, arXiv:1903.11549. [Google Scholar] [CrossRef]
  119. Lobaccaro, G.; Carlucci, S.; Löfström, E. A Review of Systems and Technologies for Smart Homes and Smart Grids. Energies 2016, 9, 348. [Google Scholar] [CrossRef] [Green Version]
  120. Batista, N.C.; Melício, R.; Mendes, V.M.F. Services Enabler Architecture for Smart Grid and Smart Living Services Providers under Industry 4.0. Energy Build. 2017, 141, 16–27. [Google Scholar] [CrossRef] [Green Version]
  121. Ray, P.P. A Survey on Internet of Things Architectures. J. King Saud Univ.–Comput. Inf. Sci. 2018, 30, 291–319. [Google Scholar] [CrossRef] [Green Version]
  122. Adelantado, F.; Vilajosana, X.; Tuset-Peiro, P.; Martinez, B.; Melia-Segui, J.; Watteyne, T. Understanding the Limits of LoRaWAN. IEEE Commun. Mag. 2017, 55, 34–40. [Google Scholar] [CrossRef] [Green Version]
  123. semtech.com Semtech. Available online: https://www.semtech.com/ (accessed on 4 February 2023).
  124. McClelland, C. LPWAN—The Benefits of LPWAN Technology vs. Other IoT Connectivity Options. Available online: https://www.leverege.com/blogpost/lpwan-benefits-vs-iot-connectivity-options (accessed on 3 February 2023).
  125. LoRaWAN About LoRaWANTM|LoRa AllianceTM. Available online: https://lora-alliance.org/about-lorawan (accessed on 1 February 2023).
  126. Sallum, E.; Pereira, N.; Alves, M.; Santos, M. Improving Quality-of-Service in LOra Low-Power Wide-Area Networks through Optimized Radio Resource Management. J. Sens. Actuator Netw. 2020, 9, 10. [Google Scholar] [CrossRef] [Green Version]
  127. Widianto, E.D.; Pakpahan, M.S.M.; Faizal, A.A.; Septiana, R. LoRa QoS Performance Analysis on Various Spreading Factor in Indonesia. In Proceedings of the 2018 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia, 23–24 October 2018. [Google Scholar] [CrossRef]
  128. Marquez, L.E.; Osorio, A.; Calle, M.; Velez, J.C.; Serrano, A.; Candelo-Becerra, J.E. On the Use of LoRaWAN in Smart Cities: A Study With Blocking Interference. IEEE Internet Things J. 2019, 7, 2806–2815. [Google Scholar] [CrossRef]
  129. Mekki, K.; Bajic, E.; Chaxel, F.; Meyer, F. Overview of Cellular LPWAN Technologies for IoT Deployment: Sigfox, LoRaWAN, and NB-IoT. In Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, 19–23 March 2018; pp. 197–202. [Google Scholar] [CrossRef]
  130. Bankov, D.; Khorov, E.; Lyakhov, A. On the Limits of LoRaWAN Channel Access. In Proceedings of the 2016 International Conference on Engineering and Telecommunication (EnT), Moscow, Russia, 29–30 November 2016; pp. 10–14. [Google Scholar] [CrossRef]
  131. Lavric, A.; Popa, V. LoRaTM Wide-Area Networks from an Internet of Things Perspective. In Proceedings of the Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI 2017), Targoviste, Romania, 29 June –1 July 2017. [Google Scholar]
  132. Sinha, R.S.; Wei, Y.; Hwang, S.H. A Survey on LPWA Technology: LoRa and NB-IoT. ICT Express 2017, 3, 14–21. [Google Scholar] [CrossRef]
  133. Nolan, K.E.; Guibene, W.; Kelly, M.Y. An Evaluation of Low Power Wide Area Network Technologies for the Internet of Things. In Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5–9 September 2016; pp. 439–444. [Google Scholar] [CrossRef]
  134. Jubin Sebastian, E.; Sikora, A.; Schappacher, M.; Amjad, Z. Test and Measurement of LPWAN and Cellular IoT Networks in a Unified Testbed. In Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 22–25 July 2019; pp. 1521–1527. [Google Scholar] [CrossRef]
  135. Nolan, K.; Kelly, M. IPv6 Convergence for IoT Cyber-Physical Systems. Inf. 2018, 9, 70. [Google Scholar] [CrossRef] [Green Version]
  136. Li, S.; Da Xu, L.; Zhao, S. 5G Internet of Things: A Survey. J. Ind. Inf. Integr. 2018, 10, 1–9. [Google Scholar] [CrossRef]
  137. Chen, M.; Miao, Y.; Hao, Y.; Hwang, K. Narrow Band Internet of Things. IEEE Access 2017, 5, 20557–20577. [Google Scholar] [CrossRef]
  138. Nair, K.K.; Abu-Mahfouz, A.M.; Lefophane, S. Analysis of the Narrow Band Internet of Things (NB-IoT) Technology. In Proceedings of the 2019 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 6–8 March 2019; pp. 1–6. [Google Scholar] [CrossRef]
  139. Orhanou, G. SNOW 3G Stream Cipher Operation and Complexity Study. Contemp. Eng. Sci. 2010, 3, 97–111. [Google Scholar]
  140. Biradar, A.; Murthy, N.S.; Awasthi, P.; Srivastava, A.K.; Akram, P.S.; Lakshminarayana, M.; Abidin, S.; Vadi, V.R.; Sisay, A. Massive MIMO Wireless Solutions in Backhaul for the 5G Networks. Wirel. Commun. Mob. Comput. 2022, 2022, 3813610. [Google Scholar] [CrossRef]
  141. Al-Turjman, F. Intelligence in IoT-Enabled Smart Cities; CRC Press: Boca Raton, FL, USA, 2019; ISBN 9781138316843. [Google Scholar]
  142. GSMA|Narrowband—Internet of Things (NB-IoT)|Internet of Things. Available online: https://www.gsma.com/iot/narrow-band-internet-of-things-nb-iot/ (accessed on 2 February 2023).
  143. Tengvall, J.; Wildmark, D. Designing Applications for Use of NB-IoT. Falmö högskola, Faculty of Technology and Society (TS) 2017. Available online: https://www.diva-portal.org/smash/get/diva2:1480547/FULLTEXT01.pdf (accessed on 28 March 2023).
  144. MIOTY® MIOTY®—Drahtlose LPWAN Technologie. Available online: https://www.iis.fraunhofer.de/de/ff/lv/net/telemetrie.html (accessed on 6 February 2023).
  145. BehrTech Next-Gen LPWAN Solution for Industrial IoT|MIOTYTM by BehrTech. Available online: https://behrtech.com/mioty/ (accessed on 6 February 2023).
  146. Sigfox Sigfox—The Global Communications Service Provider for the Internet of Things (IoT). Available online: https://www.sigfox.com/en (accessed on 6 February 2023).
  147. GitHub—Nootropicdesign/Lora-Mesh: LoRa Mesh Networking. Available online: https://github.com/nootropicdesign/lora-mesh (accessed on 28 March 2023).
  148. Lin, C.W.; Yu, H. Invited—Cooperation or Competition?: Coexistence of Safety and Security in next-Generation Ethernet-Based Automotive Networks. In Proceedings of the DAC ′16: Proceedings of the 53rd Annual Design Automation Conference, Austin, TX, USA, 5–9 June 2016. [Google Scholar] [CrossRef]
  149. Lee, H.; Chung, S.H.; Lee, Y.S.; Ha, Y. Performance Comparison of DASH7 and ISO/IEC 18000-7 for Fast Tag Collection with an Enhanced CSMA/CA Protocol. In Proceedings of the 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, Zhangjiajie, China, 13–15 November 2015; pp. 769–776. [Google Scholar] [CrossRef]
  150. Vadi, V.R.; Abidin, S.; Khan, A.; Izhar, M. Enhanced Elman Spike Neural Network Fostered Blockchain Framework Espoused Intrusion Detection for Securing Internet of Things Network. Trans. Emerg. Telecommun. Technol. 2022, 33, e4634. [Google Scholar] [CrossRef]
Figure 1. Graphical representation of the typical architecture of an MEMS.
Figure 1. Graphical representation of the typical architecture of an MEMS.
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Figure 2. Cyber-physical system example structure.
Figure 2. Cyber-physical system example structure.
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Figure 3. Mioty telegram splitting.
Figure 3. Mioty telegram splitting.
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Figure 4. Illustration of existing technologies for wireless networking regarding connectivity range.
Figure 4. Illustration of existing technologies for wireless networking regarding connectivity range.
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Figure 5. The popularity of home automation technologies.
Figure 5. The popularity of home automation technologies.
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Figure 6. Data rates of home automation technologies (Kbps).
Figure 6. Data rates of home automation technologies (Kbps).
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Figure 7. Data rates to packet sizes ratio (minimum to maximum).
Figure 7. Data rates to packet sizes ratio (minimum to maximum).
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Figure 8. Illustration of various network topology types [45].
Figure 8. Illustration of various network topology types [45].
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Figure 9. Graphical comparison of existing networking technologies regarding network characteristics (minimum to maximum).
Figure 9. Graphical comparison of existing networking technologies regarding network characteristics (minimum to maximum).
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Figure 10. Graphical comparison of existing networking technologies regarding encryption/QoS features, from minimum to maximum in each feature provided.
Figure 10. Graphical comparison of existing networking technologies regarding encryption/QoS features, from minimum to maximum in each feature provided.
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Figure 11. Graphical comparison of networking technologies regarding frequency co-existence.
Figure 11. Graphical comparison of networking technologies regarding frequency co-existence.
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Figure 12. Graphical comparison of existing networking technologies regarding energy/cost, from minimum to maximum gain.
Figure 12. Graphical comparison of existing networking technologies regarding energy/cost, from minimum to maximum gain.
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Table 1. UE Categories of the 3GPP specification number 36.306.
Table 1. UE Categories of the 3GPP specification number 36.306.
Rel-8 Cat-4Rel-8 Cat-1Rel-12 Cat-0Rel-13
Downlink peak rate150 Mbps10 Mbps1 Mbps~200 Kbps
Uplink peak rate50 Mbps5 Mbps1 Mbps~200 Kbps
Max number of downlink spatial layers2111
Number of UE RF receiver chains2211
Duplex ModeFullFullHalfHalf
UE receive bandwidth20 MHz20 MHz20 MHz1.4 MHz
Maximum UE transmit power23 dBm23 dBm23 dBm~20 dBm
Modern complexity relative to Cat-1125%100%50%25%
Table 2. Wired home automation technology user interaction.
Table 2. Wired home automation technology user interaction.
Home Automation Technologies Jsan 12 00030 i001Jsan 12 00030 i002Jsan 12 00030 i003
Released199520031980
Primary MarketsHome AutomationHome AutomationComputer networks,
Home automation
AdoptionMediumMediumExtremely High
Commercial devicesMediumMediumExtremely High
Open sourceYESProprietaryNO
APIs availableYESYESYES
PlatformsWindows/MAC/Linux
/IOS/Android
WindowsWindows/MAC/Linux IOS/Android
Connection typeWiredWiredWired
StandardASHRAE/ANSI Standard 135, ISO 16484-5:2003Proprietary802.3
Multimedia appsNONOYES
Table 3. WPAN and WLAN home automation technology user interaction.
Table 3. WPAN and WLAN home automation technology user interaction.
Home Automation Technologies Jsan 12 00030 i004Jsan 12 00030 i005Jsan 12 00030 i006Jsan 12 00030 i007Jsan 12 00030 i008Jsan 12 00030 i009Jsan 12 00030 i010
Released2007200220082014199720012004
Primary MarketsH. A.H. A.Building AutomationH. A.H. A.H. A.H. A.
AdoptionMediumExtremely HighMediumMediumExtremely HighWidely adoptedWidely adopted
Commercial devicesMediumExtremely HighMediumMediumExtremely HighHighVery High
Open sourceYESYESLicensedLicensed, OpenThreadYESLicensedYES
APIs availableYESYESYESYESYESYESYES
PlatformsWindows/MAC/IOS/AndroidWindows/MAC/Linux/IOS/AndroidWindows/MAC/Linux/IOS/AndroidWindows/MACWindows/MAC/Linux/IOS/AndroidWindows/MAC/IOS/AndroidWindows/MAC/IOS/Android
Connection typeWirelessWirelessWirelessWirelessWirelessWirelessWireless
Standard802.15.4802.15.1ISO/IEC 14543-3-1 802.15.4802.11a/b/g/n/ac/ahProprietary802.15.4
Multimedia appsNOYESNONOYESNONO
Table 4. WNAN and WWAN home automation technology user interaction.
Table 4. WNAN and WWAN home automation technology user interaction.
Home Automation Technologies Jsan 12 00030 i011Jsan 12 00030 i012Jsan 12 00030 i013Jsan 12 00030 i014Jsan 12 00030 i015Jsan 12 00030 i016
Released201120152012 (5G)201420162009
Primary MarketsSensors H. A.Building Automation, and Security, AgricultureCommunications,
Multimedia
Smart cities,
Smart buildings
Agriculture
Smart metering
Agriculture,
Smart metering
Building Automation, and Security, Agriculture
AdoptionMediumHighWidely adoptedHighLowMedium
Commercial devicesMediumMediumVery HighHighLowLow
Open sourceYESYESProprietaryYESProprietaryProprietary
APIs availableYESYESYESYESYESYES
PlatformsWindows/MAC/
Linux
Windows/MAC
Linux/IOS/Android
Windows/MAC
Linux/IOS/Android
Windows/MAC
Linux/IOS/Android
Windows/LinuxWindows/MAC
Linux/IOS/Android
Connection typeWirelessWirelessWirelessWirelessWirelessWireless
StandardISO/IEC 18000-7Proprietary3GPP-R143GPP-R13ETSI TS 103 357ETSI
EN 300 220-1,
EN 300 220-2
Multimedia appsNONOYESNONONO
Table 5. Dual-mode home automation technology user interaction.
Table 5. Dual-mode home automation technology user interaction.
Home Automation Technologies Jsan 12 00030 i017Jsan 12 00030 i018Jsan 12 00030 i019Jsan 12 00030 i020
Released1990199119752005
Primary MarketsHome AutomationHome AutomationHome AutomationHome Automation
AdoptionWidely AdoptedHighLowHigh
Commercial devicesVery HighHigh in USLowHigh US
Open sourceProprietaryProprietaryYESProprietary
APIs availableYESYESYESYES
PlatformsWindows/MAC/
Linux/IOS/Android
Widows/MAC/Linux/IOS/AndroidWidows/MAC/Linux/IOS/AndroidWindows/MAC/Linux
Connection typeWired/WirelessWired/WirelessWired/WirelessWired/Wireless
StandardISO/IEC 1454-3ISO/IEC 14908OpenProprietary
Multimedia appsYESNONONO
Table 6. Wired home automation technology technical characteristics.
Table 6. Wired home automation technology technical characteristics.
Home Automation Technologies Jsan 12 00030 i021Jsan 12 00030 i022Jsan 12 00030 i023
Network Size65,534128unlimited
Range (m)100010,000100–40,000
Connection MediumT.P, PLP, PLT.P, Optical Fiber
Message typeTelegram,
TCP/IP, LonTalk
TelegramTCP/IP
Packet length (Bytes)Ethernet: 1515,
ARCNET: 501
MS/TP: 501
LonTank:228
1.51522
Data RateEthernet: 10–1000 Mbps, ARCNET: 0.156–19 Mbps MS/TP: 9.6–78.4 Kbps LonTalk: 4.8–1250 Kbps9.6 Kbps~100 Gbps (TP),
~1.6 Tbps (FO)
GatewayYESYESYES
TopologyStar/P2PStar/P2PStar/P2P/mesh *
Mesh Enrollment--YES *
HeterogeneityEthernet,
LonWorks,
EnOcean
Ethernet,
LonWorks,
LTE
With almost all wired and wireless protocols
SoC SolutionYESYESYES
CPU architecture32-bitN/A32-bit–64-bit
* It can be achieved with extra hardware implementation.
Table 7. WPAN and WLAN home automation technologies technical characteristics.
Table 7. WPAN and WLAN home automation technologies technical characteristics.
Home AutomationTechnologies Jsan 12 00030 i024Jsan 12 00030 i025Jsan 12 00030 i026Jsan 12 00030 i027Jsan 12 00030 i028Jsan 12 00030 i029Jsan 12 00030 i030
Network Size64,000964,00025050/8191 (ah)23264,000
Range (m)10–10010030 (in)/
300 (out)
100100,
2000–4000 (1 Mbps)
30 (in)/
100 (out)
100–100
Frequency (MHz)868 (EU),
915 (US),
2400


2400
868868 (EU),
915 (US),
2400
900 (ah),
2400
5000
6000
868 (EU),
908 (US),
2400
868 (EU),
915 (US)
2400
RF Channels16 (2.4 GHz), 10 (915 MHz), 1 (868 MHz)40116 (2.4 GHz),
10 (915 MHz),
1 (868 MHz)
14 (2.4 GHz), 8 (5 GHz),
4 (900 MHz)
216 (2.4 GHz),
10 (915 MHz),
1 (868 MHz)
ModulationBPSK/BPSK, O-QPSKGFSKASKBPSK/BPSK, O-QPSKB/QPSK, COFDM, QAMFSK/GFSKBPSK/BPSK, O-QPSK
SpreadingDSSSFHSSNoDSSSDSSS, CCK, OFDMNoDSSS
Message typeMQTT, TCP/IPFrames, TCP/IP, CoAPTelegramsMQTT, D.V.R., TCP/IPMQTT, TCP/IPNetwork identifiersFrames
Packet length (Bytes)1008–4741002304/100 (ah)32100
Data Rate20/40/250 Kbps1 Mbps125 Kbps20/40/250 Kbps1 Gbps/1 Mbps (ah)9.6/40/200 Kbps20/40/250 Kbps
GatewayNOYESYESYESNOYESYES
TopologyStar/P2P/meshStar/P2P/MeshStar/P2P/MeshStar/P2P/meshStar/P2P/MeshStar/P2P/meshStar/P2P/mesh
Mesh EnrollmentProceduralProceduralProceduralProceduralProceduralProceduralProcedural
HeterogeneityBluetooth,
Thread,
Wi-Fi,
Zigbee
Wi-FI,
6LoWPAN
EnOcean
Sigfox
BACnet, BLE, Ethernet, KNX, Wi-Fi, ZigbeeWi-Fi
Zigbee
Most technologiesWi-FiEnOcean,
Ethernet,
Wi-Fi,
Thread
SoC SolutionYESYESYESYESYESYESYES
CPU architecture32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit
Table 8. WNAN and WWAN home automation technologies technical characteristics.
Table 8. WNAN and WWAN home automation technologies technical characteristics.
Home Automation Technologies Jsan 12 00030 i031Jsan 12 00030 i032Jsan 12 00030 i033Jsan 12 00030 i034Jsan 12 00030 i035Jsan 12 00030 i036
Network SizeUnlimited>10,00050,000–100,000>50,00010,000>10,000
Range (m)200020,00050005000–15,0005000–15,00015,000
Frequency (MHz)433,
868 (EU),
916 (US)
169,
433,
868 (EU),
915 (US)
700–900,
1700–1900,
2000–4000,
24,000–40,000
450–3500433,
868 (EU),
916 (US)
868 (EU), 902z (US)
RF Channels88 (915 MHz),
10 (868 MHz),
6 (2U + 3D)6 (2U + 4D)N/A360 + 40 reserved
ModulationFSK/GFSKCSSGFSK/BPSKQPSK/BPSKGMSK (UNB)DBPSK (U)
GFSK (DL)
SpreadingCSMA/CAFHSSOFDMA/SC-FDMAOFDM, SC-FDMATSMADSSS
Message typeOpenTag,
TCP/IP
LoraWANTCP/IPFramesTelegramsFrames
Packet length (Bytes)256255Network Deployment DrivenNetwork Deployment Driven10–192U: 12
D: 8
Data Rate200 kbps0.3–50 kbps1 Mbps (CAT-M), 10 Gbps (CAT-0)U: 204.8 kbps
D: 234.7 kbps
512 kbpsU: 100 bps
D: 600 bps
GatewayYESYESYESYESYESYES
TopologyStar/P2P/meshStar/mesh *StarStarStarStar
Mesh EnrollmentProcedural-----
HeterogeneityWi-FiEthernet, Wi-FiNB-IoT, Wi-FiLTEEthernetWi-Fi, BLE
SoC SolutionYESYESYESYESYESYES
CPU architecture32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit32 bit–64 bit8-bit
* It can be achieved unofficially [147].
Table 9. Dual-mode home automation technologies technical characteristics.
Table 9. Dual-mode home automation technologies technical characteristics.
Home Automation Technologies Jsan 12 00030 i037Jsan 12 00030 i038Jsan 12 00030 i039Jsan 12 00030 i040
Network Size32,7684096256Unlimited
Range (m)350 wired,
100 wireless
2700 wired30 wired120 wired
45 wireless
Connection MediumTwisted pair,
Powerline
Twisted pair,
Powerline,
Fiber optics
Power LinePowerline
Frequency868 MHz120 KHz (PL)
432 MHz
132 KHz (pl)
310 MHz (US),
433.92 (EU)
132 KHz (pl),
869.5 (EU),
915 MHz (US),
921 (AU)
RF Channels51134
Modulation2-FSKN/ANOFSK
SpreadingOFDMA/SC-FDMANONONO
Message typeTelegrams,
TCP/IP
LonTalk,
Telegrams
FramesFrames
Packet length (Bytes)16228410–24
Data Rate Wired9.6 Kbps (pl)
100 Gbps (ethernet)
1.25 Mbps60 bps13,165 bps
Data Rate Wireless38.4 KbpsN/AN/A38.4 Kbps
GatewayYESYESYESYES
TopologyStar/P2P/mesh *Star/P2PStar/P2PP2P/mesh
Mesh EnrollmentYES *--Automatic
HeterogeneityEthernet, Wi-Fi,
Zigbee, EnOcean
BACnet, Ethernet,
KNX
NoX10,
Ethernet
SoC SolutionYESYESYESYES
CPU architecture16 bit, 32 bit8-bit, 16-bi, 48 bit8-bit8-bit
* It can be achieved via the collaboration with protocols such as Zigbee or EnOcean.
Table 10. Wired home automation technology security/quality characteristics.
Table 10. Wired home automation technology security/quality characteristics.
Home Automation Technologies Jsan 12 00030 i041Jsan 12 00030 i042Jsan 12 00030 i043
EncryptionTLS128,
TLS256
-MACsec, S-ARP,
IPSec, TLS
QoS (Error handling)CRC16,
CRC32
-CRC32
Table 11. WPAN and WLAN home automation technology Security/Quality characteristics.
Table 11. WPAN and WLAN home automation technology Security/Quality characteristics.
Home Automation Technologies Jsan 12 00030 i044Jsan 12 00030 i045Jsan 12 00030 i046Jsan 12 00030 i047Jsan 12 00030 i048Jsan 12 00030 i049Jsan 12 00030 i050
EncryptionAES128E0 Stream, AES-128ARC4, VAESAES128RC4 Stream, AES Block (128), WPA, IPSec, TLSAES-128AES-128
QoS16 bit CRC, ACK, CSMA-CA16 bit CRC8 bit CRC16 bit CRC, ACK,
CSMA-CA
32 bit CRC8 bit CRC, ACK, CSMA-CA16 bit CRC, ACK, CSMA-CA
Interference AvoidanceCSMA-CA [94]AFH (Adaptive Frequency-hopping) [75]315 MHzCSMA-CA CSMA-CA CSMA-CA CSMA-CA
Table 12. WNAN and WWAN home automation technology Security/Quality characteristics.
Table 12. WNAN and WWAN home automation technology Security/Quality characteristics.
Home Automation Technologies Jsan 12 00030 i051Jsan 12 00030 i052Jsan 12 00030 i053Jsan 12 00030 i054Jsan 12 00030 i055Jsan 12 00030 i056
EncryptionAES-128AES CCM 128AES 256NSA, AES 256AES-128Key Generation, Message Encryption, MAC verification, Sequence
QoS (Error handling)16 bit CRC-32 bit CRCSNOW 3G/
ATR-128 CMAC with 4 BYTE MIC
TSMA-
Interference AvoidanceCSMA-CA [149] Channel hopping, asynchronous nature, and high bandwidthOFDMA, SC-FDMAOFDMA, SC-FDMATSMAproprietary frequency hopping and frame repetition pattern
Table 13. Dual-mode home automation technologies Security/Quality characteristics.
Table 13. Dual-mode home automation technologies Security/Quality characteristics.
Home Automation Technologies Jsan 12 00030 i057Jsan 12 00030 i058Jsan 12 00030 i059Jsan 12 00030 i060
EncryptionEIBsec,
AES CCM128
NONoneUID/AES 256
QoS (Error handling)CRC8
CRC32
CRC16-CRC16
Interference AvoidanceΝ/A Ν/AUnique frequencyUnique frequency
Table 14. Wired home automation technologies energy/cost characteristics.
Table 14. Wired home automation technologies energy/cost characteristics.
Home Automation Technologies Jsan 12 00030 i061Jsan 12 00030 i062Jsan 12 00030 i063
Energy consumptionHighHighHigh
Energy EfficiencyNoneLowNone
CostHighHighHigh
Table 15. WPAN and WLAN home automation technology energy/cost characteristics.
Table 15. WPAN and WLAN home automation technology energy/cost characteristics.
Home Automation Technologies Jsan 12 00030 i064Jsan 12 00030 i065Jsan 12 00030 i066Jsan 12 00030 i067Jsan 12 00030 i068Jsan 12 00030 i069Jsan 12 00030 i070
TX-power (mW)0–1000–100.050–100>1, >10 (ah)10–100
Energy consumptionLowLowNoneLowHighHighMedium
Energy EfficiencyHighHighUnlimitedHighMediumHighHigh
CostLowLowLowLowMediumLowLow
Table 16. WNAN and WWAN home automation technology energy/cost characteristics.
Table 16. WNAN and WWAN home automation technology energy/cost characteristics.
Home Automation Technologies Jsan 12 00030 i071Jsan 12 00030 i072Jsan 12 00030 i073Jsan 12 00030 i074Jsan 12 00030 i075Jsan 12 00030 i076
TX-power (mW)10 (433 MHz), 501.19 (868/915 MHz)EU 20,
US 100
200200EU: 25
US: 40
10–100
Energy consumptionLowVery LowMediumVery LowVery LowHigh
Energy EfficiencyHighHighHighHighHighHigh
CostHighLowMediumLowHighLow
Table 17. Dual-mode home automation technology energy/cost characteristics.
Table 17. Dual-mode home automation technology energy/cost characteristics.
Home Automation Technologies Jsan 12 00030 i077Jsan 12 00030 i078Jsan 12 00030 i079Jsan 12 00030 i080
TX-power (mW)25N/AN/AN/A
interference AvoidanceΝ/A Ν/AUnique frequencyUnique frequency
Energy EfficiencyLowHighLowN/A
CostHighHighLowMedium
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Orfanos, V.A.; Kaminaris, S.D.; Papageorgas, P.; Piromalis, D.; Kandris, D. A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications. J. Sens. Actuator Netw. 2023, 12, 30. https://doi.org/10.3390/jsan12020030

AMA Style

Orfanos VA, Kaminaris SD, Papageorgas P, Piromalis D, Kandris D. A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications. Journal of Sensor and Actuator Networks. 2023; 12(2):30. https://doi.org/10.3390/jsan12020030

Chicago/Turabian Style

Orfanos, Vasilios A., Stavros D. Kaminaris, Panagiotis Papageorgas, Dimitrios Piromalis, and Dionisis Kandris. 2023. "A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications" Journal of Sensor and Actuator Networks 12, no. 2: 30. https://doi.org/10.3390/jsan12020030

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