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Ambient Intelligence Based on the Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 9198

Special Issue Editors


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Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things; wireless sensor and actuator networks; WSN frameworks; multi agent systems
Special Issues, Collections and Topics in MDPI journals
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: Internet of Things; wireless sensor networks; unmanned systems; swarm intelligence; network reliability; network modeling
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Software Engineering tools and methodologies for the modeling; analysis and implementation of complex time-dependent systems; agent-based systems; distributed simulation; parallel and distributed systems; real-time systems; workflow management systems; Internet of Things and cyber-physical systems; smart cities; Petri Nets; Timed Automata and the DEVS formalism
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things and Cyber-Physical Systems; definitions of platforms and methodologies for the design and implementation of cyber-physical systems; distributed algorithms for the efficient management of urban facilities; swarm intelligence and peer-to-peer techniques; and Data Mining; Ambient Intelligence; edge computing; GPU computing; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the Internet of Things and its pervasiveness are opening new and very interesting scenarios. Such scenarios, also favored by new advancements in terms of hardware (new sensors/actuators/computing devices), communication protocol, infrastructure and technologies, and algorithms, are leading to the realization of novel smart cities and smart environments applications.

In this context, it is very important the exploration of new solutions which leverages wireless sensor networks, smart objects, digital twin, edge-fog-cloud computing, artificial intelligence, machine learning, and reinforcement learning for reaching ambient intelligence.

Ambient intelligence is the capacity of a smart environment to sample itself, become aware of its context (by extracting knowledge), and react accordingly, taking also into consideration the presence, needs, and behaviors of the people operating and living in such environments. Ambient intelligence is also at the basis of the so-called cognitive buildings, which are the buildings of the future capable to exhibit self-learning and self-managing behaviors in order to reach high-level goals like environmental comfort, sustainability, safety, and resource optimization.

This Special Issue wants to involve both academic and industrial communities operating in the fields of computer science, human–computer interactions, electronics, control systems, and telecommunications, and that aim to give novel contributions to the above-mentioned topics, so introducing new paradigms, algorithms, methodologies, and applications for ambient intelligence based on the Internet of Things.

Dr. Antonio Guerrieri
Dr. Xiuwen Fu
Dr. Franco Cicirelli
Dr. Andrea Vinci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • smart and cognitive environments
  • edge, fog, cloud computing
  • smart building
  • architectures, methodologies, and infrastructures for IoT ambient intelligence
  • cognitive IoT ambient intelligence
  • digital twin for IoT ambient intelligence
  • green and sustainable IoT ambient intelligence
  • low energy and battery-less systems
  • machine learning and artificial intelligence for the IoT ambient intelligence
  • wireless sensor and actuator networks
  • standards and protocols for the IoT ambient intelligence

Published Papers (6 papers)

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Research

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15 pages, 582 KiB  
Article
A Deep Anomaly Detection System for IoT-Based Smart Buildings
by Simona Cicero, Massimo Guarascio, Antonio Guerrieri and Simone Mungari
Sensors 2023, 23(23), 9331; https://doi.org/10.3390/s23239331 - 22 Nov 2023
Viewed by 1418
Abstract
In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is [...] Read more.
In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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25 pages, 1345 KiB  
Article
DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs
by Walid Osamy, Ahmed M. Khedr, Bader Alwasel and Ahmed Salim
Sensors 2023, 23(12), 5433; https://doi.org/10.3390/s23125433 - 8 Jun 2023
Viewed by 984
Abstract
Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, [...] Read more.
Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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17 pages, 2537 KiB  
Article
Implementing Dual Base Stations within an IoT Network for Sustaining the Fault Tolerance of an IoT Network through an Efficient Path Finding Algorithm
by J. K. R. Sastry, Bhupati Ch and Raja Rao Budaraju
Sensors 2023, 23(8), 4032; https://doi.org/10.3390/s23084032 - 17 Apr 2023
Cited by 1 | Viewed by 1199
Abstract
The IoT networks for implementing mission-critical applications need a layer to effect remote communication between the cluster heads and the microcontrollers. Remote communication is affected through base stations using cellular technologies. Using a single base station in this layer is risky as the [...] Read more.
The IoT networks for implementing mission-critical applications need a layer to effect remote communication between the cluster heads and the microcontrollers. Remote communication is affected through base stations using cellular technologies. Using a single base station in this layer is risky as the fault tolerance level of the network will be zero when the base stations break down. Generally, the cluster heads are within the base station spectrum, making seamless integration possible. Implementing a dual base station to cater for a breakdown of the first base station creates huge remoteness as the cluster heads are not within the spectrum of the second base station. Furthermore, using the remote base station involves huge latency affecting the performance of the IoT network. In this paper, a relay-based network is presented with intelligence to fetch the shortest path for communicating to reduce latency and sustain the fault tolerance capability of the IoT network. The results demonstrate that the technique improved the fault tolerance of the IoT network by 14.23%. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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20 pages, 5224 KiB  
Article
MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking
by Muhammad Imran, Muhammad Salah Ud Din, Muhammad Atif Ur Rehman and Byung-Seo Kim
Sensors 2023, 23(3), 1411; https://doi.org/10.3390/s23031411 - 27 Jan 2023
Cited by 1 | Viewed by 1582
Abstract
The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the [...] Read more.
The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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Review

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44 pages, 4021 KiB  
Review
Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions
by Irfanullah Khan, Ouarda Zedadra, Antonio Guerrieri and Giandomenico Spezzano
Sensors 2024, 24(11), 3276; https://doi.org/10.3390/s24113276 - 21 May 2024
Viewed by 419
Abstract
In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when [...] Read more.
In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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25 pages, 6166 KiB  
Review
A Survey of Authentication in Internet of Things-Enabled Healthcare Systems
by Mudassar Ali Khan, Ikram Ud Din, Tha’er Majali and Byung-Seo Kim
Sensors 2022, 22(23), 9089; https://doi.org/10.3390/s22239089 - 23 Nov 2022
Cited by 14 | Viewed by 2602
Abstract
The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its [...] Read more.
The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system’s security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead. Full article
(This article belongs to the Special Issue Ambient Intelligence Based on the Internet of Things)
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