Journal of Sensor and Actuator Networks

50 pages, 1152 KiB

Open AccessEditor’s ChoiceReview

AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions

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Reem A. Alzahrani

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Malak Aljabri

J. Sens. Actuator Netw. 2023, 12(1), 4; https://doi.org/10.3390/jsan12010004 - 31 Dec 2022

Cited by 3 | Viewed by 5915

Abstract

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements [...] Read more.

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. Full article

(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)

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29 pages, 584 KiB

Open AccessEditor’s ChoiceArticle

A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans

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J. Sens. Actuator Netw. 2022, 11(4), 70; https://doi.org/10.3390/jsan11040070 - 26 Oct 2022

Cited by 2 | Viewed by 2431

Abstract

The smart ocean has been regarded as an integrated sensing, communication, and computing ecosystem developed for connecting marine objects in surface and underwater environments. The development of the smart ocean is expected to support a variety of marine applications and services such as [...] Read more.

The smart ocean has been regarded as an integrated sensing, communication, and computing ecosystem developed for connecting marine objects in surface and underwater environments. The development of the smart ocean is expected to support a variety of marine applications and services such as resource exploration, marine disaster rescuing, and environment monitoring. However, the complex and dynamic marine environments and the limited network resources raise new challenges in marine communication and computing, especially for these computing-intensive and delay-sensitive tasks. Recently, the space–air–ground–sea integrated networks have been envisioned as a promising network framework to enhance the communication and computing performance. In this paper, we conduct a comprehensive survey on the integrated sensing, communication, and computing networks (ISCCNs) for smart oceans based on the collaboration of space–air–ground–sea networks from four domains (i.e., space layer, aerial layer, sea surface layer, and underwater layer), and five aspects (i.e., sensing-related, communication-related, computation-related, security-related, and application-related). Specifically, we provide the key technologies for the ISCCNs in smart oceans, and introduce the state-of-the-art marine sensing, communication, and computing paradigms. The emerging challenges with the potential solutions of the ISCCNs for smart oceans are illustrated to enable the intelligent services. Moreover, the new applications for the ISCCNs in smart oceans are discussed, and potential research directions in smart oceans are provided for future works. Full article

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34 pages, 6494 KiB

Open AccessEditor’s ChoiceReview

Wireless Body Area Network (WBAN): A Survey on Architecture, Technologies, Energy Consumption, and Security Challenges

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Mohammad Yaghoubi

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Khandakar Ahmed

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Yuan Miao

J. Sens. Actuator Netw. 2022, 11(4), 67; https://doi.org/10.3390/jsan11040067 - 18 Oct 2022

Cited by 17 | Viewed by 9657

Abstract

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- [...] Read more.

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- or nano-sensors capable of processing and sending information to the base station (BS). Sensors embedded in the bodies of individuals can enable vital information exchange over wireless communication. Network forming of these sensors envisages long-term medical care without restricting patients’ normal daily activities as part of diagnosing or caring for a patient with a chronic illness or monitoring the patient after surgery to manage emergencies. This paper reviews WBAN, its security challenges, body sensor network architecture and functions, and communication technologies. The work reported in this paper investigates a significant security-level challenge existing in WBAN. Lastly, it highlights various mechanisms for increasing security and decreasing energy consumption. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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30 pages, 1038 KiB

Open AccessEditor’s ChoiceArticle

Improving the Performance of Opportunistic Networks in Real-World Applications Using Machine Learning Techniques

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Samaneh Rashidibajgan

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Thomas Hupperich

J. Sens. Actuator Netw. 2022, 11(4), 61; https://doi.org/10.3390/jsan11040061 - 26 Sep 2022

Cited by 1 | Viewed by 1762

Abstract

In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables carried by individuals, can communicate and save-carry-forward their messages. The message transmission is often in the short range supported by communication protocols, such as Bluetooth, Bluetooth Low Energy, and Zigbee. These devices [...] Read more.

In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables carried by individuals, can communicate and save-carry-forward their messages. The message transmission is often in the short range supported by communication protocols, such as Bluetooth, Bluetooth Low Energy, and Zigbee. These devices carried by individuals along with a city’s taxis and buses represent network nodes. The mobility, buffer size, message interval, number of nodes, and number of messages copied in such a network influence the network’s performance. Extending these factors can improve the delivery of the messages and, consequently, network performance; however, due to the limited network resources, it increases the cost and appends the network overhead. The network delivers the maximized performance when supported by the optimal factors. In this paper, we measured, predicted, and analyzed the impact of these factors on network performance using the Opportunistic Network Environment simulator and machine learning techniques. We calculated the optimal factors depending on the network features. We have used three datasets, each with features and characteristics reflecting different network structures. We collected the real-time GPS coordinates of 500 taxis in San Francisco, 320 taxis in Rome, and 196 public transportation buses in Münster, Germany, within 48 h. We also compared the network performance without selfish nodes and with

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selfish nodes. We suggested the optimized configuration under real-world conditions when resources are limited. In addition, we compared the performance of Epidemic, Prophet, and PPHB++ routing algorithms fed with the optimized factors. The results show how to consider the best settings for the network according to the needs and how self-sustaining nodes will affect network performance. Full article

(This article belongs to the Topic Wireless Sensor Networks)

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46 pages, 2308 KiB

Open AccessEditor’s ChoiceReview

Mobile Edge Computing in Space-Air-Ground Integrated Networks: Architectures, Key Technologies and Challenges

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J. Sens. Actuator Netw. 2022, 11(4), 57; https://doi.org/10.3390/jsan11040057 - 22 Sep 2022

Cited by 3 | Viewed by 4283

Abstract

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is [...] Read more.

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work. Full article

(This article belongs to the Special Issue Edge Computing for the Internet of Things (IoT))

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31 pages, 29045 KiB

Open AccessEditor’s ChoiceArticle

Perception Enhancement and Improving Driving Context Recognition of an Autonomous Vehicle Using UAVs

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Abderraouf Khezaz

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Manolo Dulva Hina

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Amar Ramdane-Cherif

J. Sens. Actuator Netw. 2022, 11(4), 56; https://doi.org/10.3390/jsan11040056 - 20 Sep 2022

Cited by 2 | Viewed by 1591

Abstract

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors [...] Read more.

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors are currently being used in recognizing driving context. To further enhance such driving environment perception, this paper proposes the use of UAVs (unmanned aerial vehicles, also known as drones). In this work, drones are equipped with sensors (radar, lidar, camera, etc.), capable of detecting obstacles, accidents, and the like. Due to their small size and capability to move places, drones can be used collect perception data and transmit them to the vehicle using a secure method, such as an RF, VLC, or hybrid communication protocol. These data obtained from different sources are then combined and processed using a knowledge base and some set of logical rules. The knowledge base is represented by ontology; it contains various logical rules related to the weather, the appropriateness of sensors with respect to the weather, and the activation mechanism of UAVs containing these sensors. Logical rules about which communication protocols to use also exist. Finally, various driving context cognition rules are provided. The result is a more reliable environment perception for the vehicle. When necessary, users are provided with driving assistance information, leading to safe driving and fewer road accidents. As a proof of concept, various use cases were tested in a driving simulator in the laboratory. Experimental results show that the system is an effective tool in improving driving context recognition and in preventing road accidents. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 1392 KiB

Open AccessEditor’s ChoiceReview

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

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J. Sens. Actuator Netw. 2022, 11(3), 50; https://doi.org/10.3390/jsan11030050 - 29 Aug 2022

Cited by 3 | Viewed by 3070

Abstract

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural [...] Read more.

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification. Full article

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50 pages, 2628 KiB

Open AccessEditor’s ChoiceArticle

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

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Daisy Nkele Molokomme

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Adeiza James Onumanyi

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Adnan M. Abu-Mahfouz

J. Sens. Actuator Netw. 2022, 11(3), 47; https://doi.org/10.3390/jsan11030047 - 21 Aug 2022

Cited by 9 | Viewed by 3271

Abstract

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended [...] Read more.

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs. Full article

(This article belongs to the Special Issue Advanced Smart Grids)

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36 pages, 612 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Blockchain as IoT Economy Enabler: A Review of Architectural Aspects

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J. Sens. Actuator Netw. 2022, 11(2), 20; https://doi.org/10.3390/jsan11020020 - 29 Mar 2022

Cited by 15 | Viewed by 3636

Abstract

In the IoT-based economy, a large number of subjects (companies, public bodies, or private citizens) are willing to buy data or services offered by subjects that provide, operate, or host IoT devices. To support economic transactions in this setting, and to pave the [...] Read more.

In the IoT-based economy, a large number of subjects (companies, public bodies, or private citizens) are willing to buy data or services offered by subjects that provide, operate, or host IoT devices. To support economic transactions in this setting, and to pave the way for the implementation of decentralized algorithmic governance powered by smart contracts, the adoption of the blockchain has been proposed both in scientific literature and in actual projects. The blockchain technology promises a decentralized payment system independent of (and possibly cheaper than) conventional electronic payment systems. However, there are a number of aspects that need to be considered for an effective IoT–blockchain integration. In this review paper, we start from a number of real IoT projects and applications that (may) take advantage of blockchain technology to support economic transactions. We provide a reasoned review of several architectural choices in light of typical requirements of those applications and discuss their impact on transaction throughput, latency, costs, limits on ecosystem growth, and so on. We also provide a survey of additional financial tools that a blockchain can potentially bring to an IoT ecosystem, with their architectural impact. In the end, we observe that there are very few examples of IoT projects that fully exploit the potential of the blockchain. We conclude with a discussion of open problems and future research directions to make blockchain adoption easier and more effective for supporting an IoT economy. Full article

(This article belongs to the Special Issue Journal of Sensor and Actuator Networks: 10th Year Anniversary)

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31 pages, 544 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Survey of Outlier Detection Techniques in IoT: Review and Classification

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J. Sens. Actuator Netw. 2022, 11(1), 4; https://doi.org/10.3390/jsan11010004 - 04 Jan 2022

Cited by 23 | Viewed by 5968

Abstract

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these [...] Read more.

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided. Full article

(This article belongs to the Special Issue Journal of Sensor and Actuator Networks: 10th Year Anniversary)

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