Showing papers on "Wireless sensor network published in 2019"

6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

Abstract: A key enabler for the intelligent information society of 2030, 6G networks are expected to provide performance superior to 5G and satisfy emerging services and applications. In this article, we present our vision of what 6G will be and describe usage scenarios and requirements for multi-terabyte per second (Tb/s) and intelligent 6G networks. We present a large-dimensional and autonomous network architecture that integrates space, air, ground, and underwater networks to provide ubiquitous and unlimited wireless connectivity. We also discuss artificial intelligence (AI) and machine learning [1], [2] for autonomous networks and innovative air-interface design. Finally, we identify several promising technologies for the 6G ecosystem, including terahertz (THz) communications, very-large-scale antenna arrays [i.e., supermassive (SM) multiple-input, multiple-output (MIMO)], large intelligent surfaces (LISs) and holographic beamforming (HBF), orbital angular momentum (OAM) multiplexing, laser and visible-light communications (VLC), blockchain-based spectrum sharing, quantum communications and computing, molecular communications, and the Internet of Nano-Things.

Energy storage: The future enabled by nanomaterials

Abstract: Lithium-ion batteries, which power portable electronics, electric vehicles, and stationary storage, have been recognized with the 2019 Nobel Prize in chemistry. The development of nanomaterials and their related processing into electrodes and devices can improve the performance and/or development of the existing energy storage systems. We provide a perspective on recent progress in the application of nanomaterials in energy storage devices, such as supercapacitors and batteries. The versatility of nanomaterials can lead to power sources for portable, flexible, foldable, and distributable electronics; electric transportation; and grid-scale storage, as well as integration in living environments and biomedical systems. To overcome limitations of nanomaterials related to high reactivity and chemical instability caused by their high surface area, nanoparticles with different functionalities should be combined in smart architectures on nano- and microscales. The integration of nanomaterials into functional architectures and devices requires the development of advanced manufacturing approaches. We discuss successful strategies and outline a roadmap for the exploitation of nanomaterials for enabling future energy storage applications, such as powering distributed sensor networks and flexible and wearable electronics.

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks

Abstract: This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor’s local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system’s true state always resides in each sensor’s bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.

A wireless body area sensor network based on stretchable passive tags

Abstract: A body area sensor network (bodyNET) is a collection of networked sensors that can be used to monitor human physiological signals. For its application in next-generation personalized healthcare systems, seamless hybridization of stretchable on-skin sensors and rigid silicon readout circuits is required. Here, we report a bodyNET composed of chip-free and battery-free stretchable on-skin sensor tags that are wirelessly linked to flexible readout circuits attached to textiles. Our design offers a conformal skin-mimicking interface by removing all direct contacts between rigid components and the human body. Therefore, this design addresses the mechanical incompatibility issue between soft on-skin devices and rigid high-performance silicon electronics. Additionally, we introduce an unconventional radiofrequency identification technology where wireless sensors are deliberately detuned to increase the tolerance of strain-induced changes in electronic properties. Finally, we show that our soft bodyNET system can be used to simultaneously and continuously analyse a person’s pulse, breath and body movement. By integrating wireless stretchable on-skin sensor tags and flexible readout circuits attached to textiles using an unconventional radiofrequency identification design, a body area sensor network can be created that can continuously analyse a person’s pulse, breathing and body movement.

A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Abstract: Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application

Abstract: Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Precision Agriculture Techniques and Practices: From Considerations to Applications.

Abstract: Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications.

Abstract: Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

Wireless body sensor networks based on metamaterial textiles

Abstract: Wireless networks of sensors, displays and smart devices that can be placed on a person’s body could have applications in health monitoring, medical interventions and human–machine interfaces. Such wireless body networks are, however, typically energy-inefficient and vulnerable to eavesdropping because they rely on radio-wave communications. Here, we report energy-efficient and secure wireless body sensor networks that are interconnected through radio surface plasmons propagating on metamaterial textiles. The approach uses clothing made from conductive fabrics that can support surface-plasmon-like modes at radio communication frequencies. Our body sensor networks enhance transmission efficiencies by three orders of magnitude compared to conventional radiative networks without the metamaterial textile, and confine wireless communication to within 10 cm of the body. We also show that the approach can offer wireless power transfer that is robust to motion and textile-based wireless touch sensing. Energy-efficient and secure wireless body sensor networks can be created by using conductive fabrics that support surface-plasmon-like modes at radio communication frequencies.

Industrial Communication Systems and Their Future Challenges: Next-Generation Ethernet, IIoT, and 5G

Abstract: Industrial communication systems represent one of the most important innovations of the last decades in the context of factory and process automation systems. They are networks specifically designed to cope with the tight requirements of these challenging application fields such as real time, determinism, and reliability. Moreover, industrial networks are often deployed in environments characterized by strong electromagnetic interference, mechanical stress, critical temperature, and humidity. Over the last three decades, different classes of industrial networks have been developed according to changing requirements and available communication and information technologies. In this paper, we first provide an account of the state of the art, reviewing classical fieldbuses, real-time Ethernet networks, and industrial wireless networks, along with their most relevant features, applications, and performance figures. We introduce the complex standardization framework and analyze the market status and assumptions for future development. In the second part, we address the future perspectives focusing on new technologies, standards, and fields of application. In particular, we consider the time-sensitive networking (TSN) family of standards, Industrial Internet-of-Things (IIoT) systems, high-performance wireless LANs, industrial applications of cellular networks, and Ethernet networks for automotive communication.

A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks

Abstract: In recent years, unmanned aerial vehicles (UAVs) have received considerable attention from regulators, industry and research community, due to rapid growth in a broad range of applications. Particularly, UAVs are being used to provide a promising solution to reliable and cost-effective wireless communications from the sky. The deployment of UAVs has been regarded as an alternative complement of existing cellular systems, to achieve higher transmission efficiency with enhanced coverage and capacity. However, heavily utilized microwave spectrum bands below 6 GHz utilized by legacy wireless systems are insufficient to attain remarkable data rate enhancement for numerous emerging applications. To resolve the spectrum crunch crisis and satisfy the requirements of 5G and beyond mobile communications, one potential solution is to use the abundance of unoccupied bandwidth available at millimeter wave (mmWave) frequencies. Inspired by the technique potentials, mmWave communications have also paved the way into the widespread use of UAVs to assist wireless networks for future 5G and beyond wireless applications. In this paper, we provide a comprehensive survey on current achievements in the integration of 5G mmWave communications into UAV-assisted wireless networks. More precisely, a taxonomy to classify the existing research issues is presented, by considering seven cutting-edge solutions. Subsequently, we provide a brief overview of 5G mmWave communications for UAV-assisted wireless networks from two aspects, i.e., key technical advantages and challenges as well as potential applications. Based on the proposed taxonomy, we further discuss in detail the state-of-the-art issues, solutions, and open challenges for this newly emerging area. Lastly, we complete this survey by pointing out open issues and shedding new light on future directions for further research on this area.

Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks

Abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN), in which a UAV is dispatched as a mobile access point (AP) to serve a set of ground users periodically. The UAV employs the radio frequency (RF) wireless power transfer (WPT) to charge the users in the downlink, and the users use the harvested RF energy to send independent information to the UAV in the uplink. Unlike the conventional WPCN with fixed APs, the UAV-enabled WPCN can exploit the mobility of the UAV via trajectory design, jointly with the wireless resource allocation optimization, to maximize the system throughput. In particular, we aim to maximize the uplink common (minimum) throughput among all ground users over a finite UAV’s flight period, subject to its maximum speed constraint and the users’ energy neutrality constraints. The resulted problem is nonconvex and thus difficult to be solved optimally. To tackle this challenge, we first consider an ideal case without the UAV’s maximum speed constraint, and obtain the optimal solution to the relaxed problem. The optimal solution shows that the UAV should successively hover above a finite number of ground locations for downlink WPT, as well as above each of the ground users for uplink communication. Next, we consider the general problem with the UAV’s maximum speed constraint. Based on the above multilocation-hovering solution, we first propose an efficient successive hover-and-fly trajectory design, jointly with the downlink and uplink wireless resource allocation, and then propose a locally optimal solution by applying the techniques of alternating optimization and successive convex programming (SCP). Numerical results show that the proposed UAV-enabled WPCN achieves significant throughput gains over the conventional WPCN with fixed-location AP.

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications

Abstract: Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

On the Role of Age of Information in the Internet of Things

Abstract: In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT). We start by summarizing the concept of AoI and its variants with emphasis on the differences between AoI and other well-known performance metrics in the literature, such as throughput and delay. Building on this, we explore freshness-aware IoT design for a network in which IoT devices sense potentially different physical processes and are supposed to frequently update the status of these processes at a destination node (e.g., a cellular base station). Inspired by recent interest, we also assume that these IoT devices are powered by wireless energy transfer by the destination node. For this setting, we investigate the optimal sampling policy that jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices with the goal of minimizing long-term weighted sum-AoI. Using this, we characterize the achievable AoI region. We also compare this AoI-optimal policy with the one that maximizes average throughput (throughput-optimal policy), and demonstrate the impact of system state on their structures. Several promising directions for future research are also presented.

Underwater Wireless Sensor Networks: A Survey on Enabling Technologies, Localization Protocols, and Internet of Underwater Things

Abstract: Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

InnoHAR: A Deep Neural Network for Complex Human Activity Recognition

Abstract: Human activity recognition (HAR) based on sensor networks is an important research direction in the fields of pervasive computing and body area network. Existing researches often use statistical machine learning methods to manually extract and construct features of different motions. However, in the face of extremely fast-growing waveform data with no obvious laws, the traditional feature engineering methods are becoming more and more incapable. With the development of deep learning technology, we do not need to manually extract features and can improve the performance in complex human activity recognition problems. By migrating deep neural network experience in image recognition, we propose a deep learning model (InnoHAR) based on the combination of inception neural network and recurrent neural network. The model inputs the waveform data of multi-channel sensors end-to-end. Multi-dimensional features are extracted by inception-like modules by using various kernel-based convolution layers. Combined with GRU, modeling for time series features is realized, making full use of data characteristics to complete classification tasks. Through experimental verification on three most widely used public HAR datasets, our proposed method shows consistent superior performance and has good generalization performance, when compared with the state-of-the-art.

On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

Abstract: In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS.

Scheduling Algorithms for Optimizing Age of Information in Wireless Networks With Throughput Constraints

Abstract: Age of Information (AoI) is a performance metric that captures the freshness of the information from the perspective of the destination. The AoI measures the time that elapsed since the generation of the packet that was most recently delivered to the destination. In this paper, we consider a single-hop wireless network with a number of nodes transmitting time-sensitive information to a base station and address the problem of minimizing the expected weighted sum AoI of the network while simultaneously satisfying timely-throughput constraints from the nodes. We develop four low-complexity transmission scheduling policies that attempt to minimize AoI subject to minimum throughput requirements and evaluate their performance against the optimal policy. In particular, we develop a randomized policy, a Max-Weight policy, a Drift-Plus-Penalty policy, and a Whittle’s Index policy, and show that they are guaranteed to be within a factor of two, four, two, and eight, respectively, away from the minimum AoI possible. The simulation results show that Max-Weight and Drift-Plus-Penalty outperform the other policies, both in terms of AoI and throughput, in every network configuration simulated, and achieve near-optimal performance.

The Sensable City: A Survey on the Deployment and Management for Smart City Monitoring

Abstract: In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles’ sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This paper addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network

Abstract: Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Abstract: Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

Lightweight and Physically Secure Anonymous Mutual Authentication Protocol for Real-Time Data Access in Industrial Wireless Sensor Networks

Abstract: Industrial wireless sensor network (IWSN) is an emerging class of a generalized WSN having constraints of energy consumption, coverage, connectivity, and security. However, security and privacy is one of the major challenges in IWSN as the nodes are connected to Internet and usually located in an unattended environment with minimum human interventions. In IWSN, there is a fundamental requirement for a user to access the real-time information directly from the designated sensor nodes. This task demands to have a user authentication protocol. To satisfy this requirement, this paper proposes a lightweight and privacy-preserving mutual user authentication protocol in which only the user with a trusted device has the right to access the IWSN. Therefore, in the proposed scheme, we considered the physical layer security of the sensor nodes. We show that the proposed scheme ensures security even if a sensor node is captured by an adversary. The proposed protocol uses the lightweight cryptographic primitives, such as one way cryptographic hash function, physically unclonable function, and bitwise exclusive operations. Security and performance analysis shows that the proposed scheme is secure, and is efficient for the resource-constrained sensing devices in IWSN.

Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures.

Abstract: Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have a wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. However, owing to the absence of a physical line-of-defense, i.e. there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: "Passive Attacks" and "Active Attacks". Understanding these attacks and their associated defense mechanisms will help to pave a secure path towards the proliferation and public acceptance of IoT technology.

Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Abstract: To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day’s real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Abstract: Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

Routing in Flying Ad Hoc Networks: Survey, Constraints, and Future Challenge Perspectives

Abstract: Owing to the explosive expansion of wireless communication and networking technologies, cost-effective unmanned aerial vehicles (UAVs) have recently emerged and soon they will occupy the major part of our sky. UAVs can be exploited to efficiently accomplish complex missions when cooperatively organized as an ad hoc network, thus creating the well-known flying ad hoc networks (FANETs). The establishment of such networks is not feasible without deploying an efficient networking model allowing a reliable exchange of information between UAVs. FANET inherits common features and characteristics from mobile ad hoc networks (MANETs) and their sub-classes, such as vehicular ad hoc networks (VANETs) and wireless sensor networks (WSNs). Unfortunately, UAVs are often deployed in the sky adopting a mobility model dictated by the nature of missions that they are expected to handle, and therefore, differentiate themselves from any traditional networks. Moreover, several flying constraints and the highly dynamic topology of FANETs make the design of routing protocols a complicated task. In this paper, a comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs. A classification, descriptions, and comparative studies of an important number of existing routing protocols dedicated to FANETs are detailed. Furthermore, the paper depicts future challenge perspectives, helping scientific researchers to discover some themes that have been addressed only ostensibly in the literature and need more investigation. The novelty of this survey is its uniqueness to provide a complete analysis of the major FANET routing protocols and to critically compare them according to different constraints based on crucial parameters, thus better presenting the state of the art of this specific area of research.