J. Sens. Actuator Netw., Volume 11, Issue 2 (June 2022) – 10 articles

Currently, there are billions of connected devices, and the Internet of Things (IoT) has boosted these numbers. In the case of private networks, a few hundred devices connected can cause instability and even data loss in communication. In this article, we propose a machine learning-based modeling to solve network overload caused by continuous monitoring of the trajectories of several devices tracked indoors. The proposed modeling was evaluated with over a hundred thousand of coordinate locations of objects tracked in three synthetic environments and one real environment. It has been shown that it is possible to solve the network overload problem by increasing the latency in sending data and predicting intermediate coordinates of the trajectories on the server-side with ensemble models, such as Random Forest, and using Artificial Neural Networks without relevant data loss. It has also been shown that it is possible to predict at least thirty intermediate coordinates of the trajectories of objects tracked with $R^2$ greater than $0.8$. Full article

The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts. Full article

The use of heterogeneous networks (HetNets) that combine macrocells and picocells in the same coverage is effective in increasing system capacity and improving user throughput. The use of high carrier frequency bands is also expected to help achieving higher data rates because it promises vast amounts of signal bandwidth. Therefore, multiband HetNets with picocells operating at high carrier frequency bands have attracted significant attention with the aim of increasing system capacity and achieving a high user throughput in fifth-generation mobile systems and beyond. In HetNet deployments, a picocell range expansion (CRE) technique that virtually expands the picocell coverage is well known to allow more user equipment (UE) to access the picocell providing a fixed cell selection offset (CSO) for all UE. Thus far, there has not been sufficient research on optimizing the transmission (Tx) power of pico-evolved node Bs (eNBs) operating at high carrier frequency bands in multiband HetNets. In addition, the effects of CRE in multiband HetNets have not been clarified. In this paper, we first investigated the optimal Tx power of pico-eNB in a multiband HetNet combining macrocells operating at 2 GHz and picocells operating at 4.5 GHz band with a wider signal bandwidth using system-level computer simulations. Then, from the user throughput perspective, we investigated the effects of CRE providing a positive CSO for UE using two pico-eNB Tx powers close to the optimal value. Using these results, we discussed how to choose the pico-eNB Tx power when CRE was activated and validated the design method for a multiband HetNet. Full article

In the context of future ultra-dense mobile networks, spectrum and energy efficiencies (SE and EE) are critical measures in designing efficient systems for the sixth-generation (6G) of wireless networks. Recognized for their benefits in increasing SE and EE, non-orthogonal multiple access (NOMA) and device-to-device (D2D) communications are combined in this work to present a new NOMA-based D2D scheme increasing the performance in terms of SE and EE. The users in the proposed scheme are split into coalitions. Coalition heads are served in NOMA directly from the base stations, while the other users within the coalitions get the service through D2D links. We investigate the system’s SE and EE for different mobility patterns, and we discuss optimal system configurations with the help of Monte Carlo simulations. The obtained results show that the proposed system exhibits a better performance compared to conventional OMA and NOMA models, especially in low mobility contexts. Full article

Data obtained from sensors connected to wireless sensor networks must be stored and processed to enable environments such as smart cities. However, with the exponential growth in the number of devices at the edge of the network, it is necessary to implement robust techniques, capable of selecting reliable data sources and meeting low latency requirements, in order to serve critical applications. Thus, to overcome these challenges, this research work presents FOCUSeR, a method for ranking sensors. The method uses the evaluation of data as a criterion for the ranking, allowing us to identify occurrences of failures in sensors and anomalies in environments. In order to meet the requirements inherent to WSNs, the proposed method was developed to run in a fog computing environment, using online learning and constant updating over time to avoid effects such as time drift. The generated ranking lists are managed through distributed hash tables. To provide reliability to the experimental results, a real experimental environment was developed. Moreover, using this developed testbed, a dataset with labels was created, to support the evaluation of the method. In addition, four other real datasets were used, three of which were labeled through artificial fault injection. These datasets were labeled in a related work that focused on injecting artificial faults. The experimental results obtained demonstrate that the proposed approach can provide reliability in the use of sensor data, using low computational resources and reducing latency in the sensor selection process. Precision rates are approximately 98% and Accuracy rates are greater than 94% across all datasets. In addition, the analyses carried out show that the Accuracy has an increasing rate as the number of samples also increases. Results obtained in the failure data recovery also demonstrate the feasibility of the proposal in this resource. Full article

Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks. Full article

Mobile Ad Hoc Networks (MANETs) are kind of wireless networks where the nodes move in decentralized environments with a highly dynamic infrastructure. Many well-known routing protocols have been proposed, with each having its own design mechanism and its own strengths and weaknesses and most importantly, each protocol being mainly designed for specific applications and scenarios. Most of the research studies in this field used simulation testbeds to analyze routing protocols. Very few contributions suggested the use of analytical studies and mathematical approaches to model some of the existing routing protocols. In this research, we have built a comprehensive mathematical-based model to analyze the Destination-Sequenced Distance Vector protocol (DSDV), one of the main widely deployed proactive protocols and studied its performance on estimating the path duration based on the concepts of the probability density function and the expected values to find the best approximation values in real scenarios. We have tested the validity of the proposed model using simulation scenarios implemented by the Network Simulator tool (NS3). The results extracted from both the mathematical model and the simulation have shown that the path duration is inversely proportional to both the speed of the node and the hop count. Furthermore, it had shown that the path duration estimated from the DSDV protocol is less than the actual path duration, due to the implementation of the settling time concept and keeping the “periodic routes’ update” parameter at a constant level, despite the fact that the node’s speed reduces the effective path utilization. Full article

In managing the COVID-19 pandemic, the Malaysian government enforced mandatory body temperature screening as a rudimentary form of infection detection at the entry points of establishments and public transportation. However, previous iterations of IoT body temperature screening systems were bulky, fragile, expensive, and designed for personal use instead of the screening of many people. Therefore, a standalone, portable, and rugged IoT-enabled body temperature screening system for detecting elevated temperatures was developed in this research work. This system uses a proximity sensor to detect subjects and determine their body temperature using a non-contact temperature sensor. Body temperature data is displayed on the device and uploaded over a Wi-Fi network to a cloud server for data storage and analysis. From the cloud server, body temperature information is retrieved and displayed on the Blynk IoT client dashboard for remote monitoring. The device also provides alerts for body temperatures above 37.5 °C. The prototype system performed impressively during the assessment. Body temperature readings were impressively accurate compared to a medical-grade non-contact thermometer, with an average variance of less than 1%. Additionally, the system was highly reliable, with a 100% IoT data broadcast success rate. Full article

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks, performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area. Full article

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