J. Sens. Actuator Netw., Volume 9, Issue 3 (September 2020) – 14 articles

Population growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under different environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment. Full article

In the design of multi-loop Networked Control Systems (NCSs), wherein each control system is characterized by heterogeneous dynamics and associated with a certain set of timing specifications, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. The majority of the design approaches for sampling, scheduling, and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic Event-Triggered (ET) metric. We consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared communication network. We investigate the co-design across the NCS and discuss the pros and cons with the AoI and ET approaches in terms of asymptotic control performance measured by Linear-Quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic ET metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with purely randomized policies. Our theoretical analysis is validated through several simulation scenarios. Full article

High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM. Full article

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs. Full article

A vehicular ad hoc network (VANET) is a technology in which moving cars are used as routers (nodes) to establish a reliable mobile communication network among the vehicles. Some of the drawbacks of the routing protocol, Ad hoc On-Demand Distance Vector (AODV), associated with VANETs are the end-to-end delay and packet loss. We modified the AODV routing protocols to reduce the number of route request (RREQ) and route reply (RREP) messages by adding direction parameters and two-step filtering. The two-step filtering process reduces the number of RREQ and RREP packets, reduces the packet overhead, and helps to select the stable route. In this study, we show the impact of the direction parameter in reducing the end-to-end delay and the packet loss in AODV. The simulation results show a 1.4% reduction in packet loss, an 11% reduction in the end-to-end delay, and an increase in throughput. Full article

The world is experiencing the new development of smart cities. Smart cities’ infrastructure in its core is based on wireless sensor networks (WSNs) and the internet of things (IoT). WSNs consist of tiny smart devices (Motes) that are restricted in terms of memory, storage, processing capabilities, and sensing and communication ranges. Those limitations pose many security issues where regular cryptography algorithms are not suitable to be used. Besides, such capabilities might be degraded in case cheap sensors are deployed with very large numbers in applications, such as smart cities. One of the major security issues in WSNs that affect the overall operation, up to network interruption, in smart cities is the sinkhole routing attack. The paper has three-fold contributions: (1) it utilizes the concept of clustering for energy saving in WSNs, (2) proposing two light and simple algorithms for intrusion detection and prevention in smart cities—threshold-based intrusion detection system (TBIDS) and multipath-based intrusion detection system (MBIDS), and (3) utilizing the cross-layer technique between the application layer and network layer for the purpose of intrusion detection. The proposed methods are evaluated against recent algorithms—S-LEACH, MS-LEACH, and ABC algorithms. Full article

The paper describes the elements of the developed MATLAB Simulink library for building the models of Bluetooth Low Energy (BLE) wireless sensor networks to simulate the communication between BLE devices in the presence of interference and channel noise. Various parameters can be configured for the devices including their 2D positions to take into account the distances between them for calculating the attenuation coefficients of the transmitted signals. Two simulation examples are provided, one of which demonstrates the data exchange between one master device and one slave at high data packet transmission rate (2 kHz), while the other example shows the data exchange between one master and multiple slaves simultaneously, in which case the data packet transmission rate can be no larger than 133 Hz. Full article

Cyber–Physical Systems (CPS) connect the physical world (systems, environments, and humans) with the cyber world (software, data, etc.) to intelligently enhance the operational environment they serve. CPS are distributed software and hardware components embedded in the physical world and possibly attached to humans. They offer smart features, such as enhancing and optimizing the reliability, quality, safety, health, security, efficiency, operational costs, sustainability, and maintainability of physical systems. CPS are also very vulnerable to security attacks and criminal activities. In addition, they are very complex and have a direct impact on their environment. Therefore, it is hard to detect and investigate security attacks, while such attacks may have a catastrophic impact on the physical world. As a result, CPS must incorporate security measures in addition to suitable and effective forensics capabilities. When the security measures fail and an attack occurs, it becomes imperative to perform thorough forensics analysis. Adding effective forensics tools and capabilities will support the investigations of incidents. This paper defines the field of CPS forensics and its dimensions: Technical, Organizational, and Legal. Then, it reviews examples of current research efforts in the field and the types of tools and methods they propose for CPS forensics. In addition, it discusses the issues and challenges in the field that need to be addressed by researchers and developers of CPS. The paper then uses the review outcomes to discuss future research directions to address challenges and create a more effective, efficient, and safe forensics tools and for CPS. This discussion aims to create a starting point for researchers where they can identify the gaps and challenges and create suitable solutions through their research in CPS forensics. Full article

The highly dynamical entrainment and transport processes of solids due to geophysical flows is a major challenge studied by water infrastructure engineers and geoscientists alike. A miniaturised instrumented particle that can provide a direct, non-intrusive, low-cost and accessible method compared to traditional approaches for the assessment of coarse sediment particle entrainment is developed, calibrated and tested. The instrumented particle presented here is fitted with inertial microelectromechanical sensors (MEMSs), such as a triaxial accelerometer, a magnetometer and angular displacement sensors, which enable the recording of the particle’s three-dimensional displacement. The sensor logs nine-axis data at a configurable rate of 200–1000 Hz and has a standard mode of deployment time of at least one hour. The data can be obtained and safely stored in an internal memory unit and are downloadable to a PC in an accessible manner and in a usable human-readable state. A plethora of improved design specifications have been implemented herein, including increased frequency, range and resolution of acceleration and gyroscopic sensing. Improvements in terms of power consumption, in comparison to previous designs, ensure longer periods of data logging. The embedded sensors are calibrated using simple physical motions to validate their operation. The uncertainties in the experiments and the sensors’ readings are quantified and an appropriate filter is used for inertial sensor fusion and noise reduction. The instrumented particle is tested under well-controlled lab conditions, where the beginning of the destabilisation of a bed surface in an open channel flow, is showcased. This is demonstrative of the potential that specifically designed and appropriately calibrated instrumented particles have in assessing the initiation and occurrence of water infrastructure hazards. Full article

This work presents a characterization of a low-cost, low-latency Visible Light Communication (VLC) prototype for infrastructure-to-vehicle (I2V) communication for future Intelligent Transportation Systems (ITS). The system consists of a regular traffic light as a transmitter (the red light is modulated with the information), and a photodetector as a receiver. The latter is equipped with low-cost Fresnel lenses as condensers, namely, 1 ${}^{\prime }$ ${}^{\prime }$ Fresnel and 2 ${}^{\prime }$ ${}^{\prime }$ Fresnel, to increase the optical gain of the system at the receiver. The system is capable of Active Decode and Relay (ADR) of information to further incoming units. The experimental characterization of amplitude and Packet Error Rate (PER) for the proposed system has been performed for distances up to 50 m. The results show that by incorporating the 2 ${}^{\prime }$ ${}^{\prime }$ Fresnel lens in the photodetector, an error free ( $\mathrm{PER}\le {10}^{-5}$ ) I2V communication is established up to 50 m. Furthermore, the prototype can be used for both broadcast and beaconing transmission modes. This low-cost VLC-based system could offer sub-millisecond latency in the full ADR process for distances up to 36 m, which makes it suitable for integration in Cellular-V2X (C-V2X) and 5G platforms. Full article

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained. Full article

Research related to Wireless Body Area Networks (WBAN) has recently gained more attention due to its application in enabling smart healthcare systems. A WBAN consists of several sensing nodes and a dedicated coordinator. The distributed nodes communicate with the coordinator by accessing the physical communication channel in a randomly distributed fashion. Random channel access may cause frame re-transmission of corrupted frames due to frame collisions. As a result of that, there will be degradation in the WBAN throughput, an increase in delay, and a waste of node energy. Nodes within a WBAN can be classified using specific user priorities allowing for prioritized communication to reduce possible frame collisions. To improve the performance and energy efficiency, this work aims to reduce collisions among nodes that belong to the same users’ priority (homogeneous collisions) and collisions among nodes of different users’ priorities (heterogeneous collisions). Homogeneous collisions can be reduced by scaling the minimum Contention Window (CW) among nodes within the same user priority, whereas heterogeneous collisions can be reduced by allowing higher user priority nodes to transmit while lower user priority nodes enter into a backoff state. This paper presents an analytical model and extensive simulations to show the enhanced performance of the proposed collision avoidance mechanism. The results show that the throughput and node energy efficiency is improved by a factor of three and two times, respectively. Full article

One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, visible light communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information provided can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm. Full article

This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage. Full article