J. Sens. Actuator Netw., Volume 10, Issue 3 (September 2021) – 22 articles

A wide-scale outdoor remote deployment involves a large number of low-cost nodes that are powered by green energy, such as solar. We deal with such a system for landslide monitoring where the tiny nodes with ultra-low memory as little as 2 KB are directly connected to the Internet using cellular networks, thereby constituting Cellular IoT’s (C-IoT). This makes them vulnerable to a wide range of Denial of Service (DoS) attacks during their collaborative communications. Further, due to memory constraints, the nodes are not able to run resource-hungry security algorithms. Existing IoT protocols also cannot offer resiliency to DoS attacks for these memory-constrained devices. This paper proposes the Voice Response Internet of Things (VRITHI), which addresses the above issues by using the voice channel between the nodes. To the best of our knowledge, this is the first solution in the IoT domain where both the voice and data channels are being used for collaborative communications. Evaluation results demonstrate that VRITHI is able to reduce external DoS attacks from 82–65% to less than 28% and improves real-time communications in such a memory-constrained environment. In addition, it also contributes to green IoT energy saving by more than 50% in comparison with other IoT protocols. Full article

Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network’s operation. It is for this reason that ML techniques are used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors’ values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy. Full article

Software-defined networking generally assumes ideal control channels between controller and network nodes. This may not be the case in challenged environments that are becoming more common due to dense and reduced-coverage 5G deployments and use cases requiring cost-effective wireless transport networks. In this paper, we evaluate the impact on network performance of unreliable controller-to-node communication channels, propose a hybrid SDN (hSDN) solution that switches between centralized and distributed operational modes depending on network conditions, and evaluate this solution under a variety of network scenarios (e.g., link impairments or packet loss ratios) designed to assess its operational limits. The results show that the proposed solution substantially improved the aggregated throughput, particularly when control channel packet loss ratios increased, while only showing a slight increase in average latency (e.g., 28% throughput improvement for 20% control packet losses). This enables network operation in hard conditions under which a canonical centralized SDN control would result in a nonoperational network. Full article

Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy. Full article

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error. Full article

Network slicing is a key enabler for providing new services to industry verticals. In order to enable network slice provisioning, it is important to study the network slice type allocation for different device types in a real industrial case. Furthermore, the costs of the required virtualization infrastructure need to be analyzed for various cloud deployment scenarios. In this paper, a cost model for the virtualization infrastructure needed for network slice provisioning is developed and subsequently applied to a real smart factory. In the model, slice types and devices are mapped such that each factory device is provisioned with one or more slice types, as required. The number of devices to be supported per slice type is forecasted for 2021–2030, and the total costs of ownership, costs per slice type, and costs for every slice type, for each device are calculated. The results are analyzed for three cloud deployment scenarios: local, distributed, and centralized. The centralized scenario was found to have the lowest cost. Moreover, sensitivity analysis is conducted by varying the device growth, the number of factories, the level of isolation between network slices, and resource overbooking. The resulting evaluation and cost breakdown can help stakeholders select a suitable deployment scenario, gauge their investments, and exercise suitable pricing. Full article

Wireless Sensor Networks (WSNs) is a major sensing technology that has revolutionized the way information is collected, processed, and used in many smart cities’ applications that rely on sensing technologies for event detection and monitoring. Despite the multiple benefits that such technology offers, the quick depletion of sensors’ battery power represents a major concern, mainly due to the extensive computational tasks and communication operations performed by individual sensors. Indeed, the cost of replacing batteries can be prohibitively expensive, especially when sensors are deployed in areas where access is difficult, in urbanized cities. To extend sensors’ lifetime, this paper proposes a new variant of LEACH protocol named LEACH enhanced with probabilistic cluster head selection (LEACH-PRO). LEACH-PRO introduces several measures to extend WSNs nodes’ lifetime such as cluster head node selection using a probabilistic function based on maximum residual energy and minimum distance to the sink. The obtained simulation results have proven the supremacy of LEACH-PRO over LEACH and direct transmission protocol in terms of the achieved network lifetime and the generated traffic overhead. Most importantly, LEACH-PRO will significantly extend the sensors’ lifetime, which would make this type of deployment more viable in smart city scenarios. Full article

Continued advancements in microprocessors, electronics, and communication technology have led to the design and development of sensing devices with increased functionalities, smaller sizes, larger processing, storage, and communication capabilities, and decreased cost. A large number of these sensor nodes are used in many environmental, infrastructure, commercial, and military monitoring applications. Due to the linearity of a good number of the monitored structures such as oil, gas, and water pipelines, borders, rivers, and roads, the wireless sensor networks (WSNs) that are used to monitor them have a linear topology. This type of WSN is called a linear sensor network (LSN). In this paper, two distributed algorithms for topology discovery in thick LSNs are presented: the linear backbone discovery algorithm (LBD) and the linear backbone discovery algorithm with x backbone paths (LBDx). Both of them try to construct a linear backbone for efficient routing in LSNs. However, the LBD algorithm has the objective of minimizing the number of messages used during the backbone discovery process. On the other hand, the LBDx algorithm focuses on reducing the number of hops of the data messages transmitted from the nodes to the sink. LBD and LBDx exhibit good properties and efficient performance, which are confirmed by extensive simulations. Full article

Smart manufacturing and smart factories depend on automation and robotics, whereas human–robot collaboration (HRC) contributes to increasing the effectiveness and productivity of today’s and future factories. Industrial robots especially in HRC settings can be hazardous if safety is not addressed properly. In this review, we look at the collaboration levels of HRC and what safety actions have been used to address safety. One hundred and ninety-three articles were identified from which, after screening and eligibility stages, 46 articles were used for the extraction stage. Predefined parameters such as: devices, algorithms, collaboration level, safety action, and standards used for HRC were extracted. Despite close human and robot collaboration, 25% of all reviewed studies did not use any safety actions, and more than 50% did not use any standard to address safety issues. This review shows HRC trends and what kind of functionalities are lacking in today’s HRC systems. HRC systems can be a tremendously complex process; therefore, proper safety mechanisms must be addressed at an early stage of development. Full article

Due to the emergence of the Internet of Things, the need for effective identification and traceability has increased. Radio-frequency identification (RFID), a simple and cheap approach for gathering information, has therefore drawn the attention of research communities. However, this system suffers from problems caused by high density, such as collisions and duplication. Thus, the deployment of RFID is more effective in a dense environment where it may improve overage and delays. A wide range of solutions have been proposed; however, the majority of these are based on the application context. In this paper, we propose a general MAC layer protocol FTSMAC (Frequency Time Scheme MAC) in which the spectrum frequency is efficiently used by dividing the signal into different time slots via a messaging mechanism used by RFID readers. This limits the collisions in high-density RFID deployment that affect the performance of the system. Thus, our solution allows the communication system to converge to a stable state within a convenient time. Full article

The recent, massive diffusion of LED-based illumination devices makes Visible Light Communications (VLC) a widely recognised wireless communication technology with large potential impact in many indoor and outdoor applications. In the indoor scenario, one of the most promising VLC implementations is foreseen in museums, exhibitions and cultural heritage sites. In this context, digital data can be transmitted by the specific lighting system of each artwork and received by the nearby standing visitors, allowing a complete set of dedicated services such as augmented reality (AR) and real-time indoor positioning, exploiting the directionality of the optical channel. In this work, we achieve, for the first time, VLC transmission through diffusive LED illumination of three-dimensional artworks (wooden and marble sculptures) in a real museum, exploiting the available LED illumination system, demonstrating the feasibility of VLC technology also when complex three-dimensional artworks, such as sculptures or bas-reliefs, are involved. In our experimental campaign, performed inside the Basilica of Santa Maria Novella in Florence, we perform extensive Packet Error Rate (PER) and Signal-to-Noise Ratio (SNR) tests on two important wooden and marble sculptures (Crucifix by Brunelleschi and the Holy Water Font by Bordoni, respectively), for different distances, view angles and configurations, in order to mimic a wide set of situations that visitors may encounter in a realistic scenario. We achieve successful VLC transmission for distances up to 8 m from artworks, at baud rate of 28 kBaud. We also provide detailed results on the characterization of the transmission Field of View (FoV) for our prototype, as well as the effect of side shifts of the observer’s position on the quality of VLC transmission, providing essential information for future implementations of positioning protocols and dedicated services in realistic, indoor scenarios. Our work represents an important step forward towards the deployment of VLC technology in museums and, more in general, it opens for far-reaching developments in a wide set of real indoor environments, including the cultural heritage sector, where diffusive VLC links exploiting illumination of three-dimensional objects could represent a ground-breaking innovation. Full article

The MEDIcal WARNing (MEDIWARN) system continuously and automatically monitors the vital parameters of pre-intensive care hospitalized patients and, thanks to an intelligent processing system, provides the medical teams with a better understanding of their patients’ clinical condition, thus enabling a prompt reaction to any change. Since the hospital units generally lack a wired infrastructure, a wireless network is required to collect sensor data in a server for processing purposes. This work presents the MEDIWARN communication system, addressing both the network architecture and a simple, lightweight and configurable routing protocol that fits the system requirements, such as the ability to offer path redundancy and mobility support without significantly increasing the network workload and latency. The novel protocol, called the MultiPath Routing Protocol for MEDIWARN (MP-RPM), was therefore designed as a solution to support low-latency reliable transmissions on a dynamic network while limiting the network overhead due to the control messages. The paper describes the MEDIWARN communication system and addresses the experimental performance evaluation of an implementation in a real use-case scenario. Moreover, the work discusses a simulative assessment of the MEDIWARN communication system performance obtained using different routing protocols. In particular, the timeliness and reliability results obtained by the MP-RPM routing protocol are compared with those obtained by two widely adopted routing protocols, i.e., the Ad-hoc On-demand Distance Vector (AODV) and the Destination-Sequenced Distance-Vector Routing (DSDV). Full article

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm. Full article

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed. Full article

Usually, the design of an Autonomous Vehicle (AV) does not take into account traffic rules and so the adoption of these rules can bring some challenges, e.g., how to come up with a Digital Highway Code which captures the proper behaviour of an AV against the traffic rules and at the same time minimises changes to the existing Highway Code? Here, we formally model and implement three Road Junction rules (from the UK Highway Code). We use timed automata to model the system and the MCAPL (Model Checking Agent Programming Language) framework to implement an agent and its environment. We also assess the behaviour of our agent according to the Road Junction rules using a double-level Model Checking technique, i.e., UPPAAL at the design level and AJPF (Agent Java PathFinder) at the development level. We have formally verified 30 properties (18 with UPPAAL and 12 with AJPF), where these properties describe the agent’s behaviour against the three Road Junction rules using a simulated traffic scenario, including artefacts like traffic signs and road users. In addition, our approach aims to extract the best from the double-level verification, i.e., using time constraints in UPPAAL timed automata to determine thresholds for the AVs actions and tracing the agent’s behaviour by using MCAPL, in a way that one can tell when and how a given Road Junction rule was selected by the agent. This work provides a proof-of-concept for the formal verification of AV behaviour with respect to traffic rules. Full article

The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R² of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences. Full article

This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of Internet of Things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper. Full article

Statistical traffic analysis has absolutely exposed the privacy of supposedly secure network traffic, proving that encryption is not effective anymore. In this work, we present an optimal countermeasure to prevent an adversary from inferring users’ online activities, using traffic analysis. First, we formulate analytically a constrained optimization problem to maximize network traffic obfuscation while minimizing overhead costs. Then, we provide OPriv, a practical and efficient algorithm to solve dynamically the non-linear programming (NLP) problem, using Cplex optimization. Our heuristic algorithm selects target applications to mutate to and the corresponding packet length, and subsequently decreases the security risks of statistical traffic analysis attacks. Furthermore, we develop an analytical model to measure the obfuscation system’s resilience to traffic analysis attacks. We suggest information theoretic metrics for quantitative privacy measurement, using entropy. The full privacy protection of OPriv is assessed through our new metrics, and then through extensive simulations on real-world data traces. We show that our algorithm achieves strong privacy protection in terms of traffic flow information without impacting the network performance. We are able to reduce the accuracy of a classifier from 91.1% to 1.42% with only 0.17% padding overhead. Full article