Showing papers on "Sensor node published in 2020"

Distributed Set-Membership Filtering for Multirate Systems Under the Round-Robin Scheduling Over Sensor Networks

Abstract: In this paper, the distributed set-membership filtering problem is dealt with for a class of time-varying multirate systems in sensor networks with the communication protocol. For relieving the communication burden, the round-Robin (RR) protocol is exploited to orchestrate the transmission order, under which each sensor node only broadcasts partial information to both the corresponding local filter and its neighboring nodes. In order to meet the practical transmission requirements as well as reduce communication cost, the multirate strategy is proposed to govern the sampling/update rate of the plant, the sensors, and the filters. By means of the lifting technique, the augmented filtering error system is established with a unified sampling rate. The main purpose of the addressed filtering problem is to design a set of distributed filters such that, in the simultaneous presence of the RR transmission protocol, the multirate mechanism, and the bounded noises, there exists a certain ellipsoid that includes all possible error states at each time instant. Then, the desired distributed filter gains are obtained by minimizing such an ellipsoid in the sense of the minimum trace of the weighted matrix. The proposed resource-efficient filtering algorithm is of a recursive form, thereby facilitating the online implementation. A numerical simulation example is given to demonstrate the effectiveness of the proposed protocol-based distributed filter design method.

A survey on data aggregation techniques in IoT sensor networks

Abstract: There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

Distributed State-Saturated Recursive Filtering Over Sensor Networks Under Round-Robin Protocol

Abstract: This article is concerned with the distributed recursive filtering issue for stochastic discrete time-varying systems subjected to both state saturations and round-robin (RR) protocols over sensor networks. The phenomenon of state saturation is considered to better describe practical engineering. The RR protocol is introduced to mitigate a network burden by determining which component of the sensor node has access to the network at each transmission instant. The purpose of the issue under consideration is to construct a distributed recursive filter such that a certain filtering error covariance’s upper bound can be found and the corresponding filter parameters’ explicit expression is given with both state saturations and RR protocols. By taking advantage of matrix difference equations, a filtering error covariance’s upper bound can be presented and then be minimized by appropriately designing filter parameters. In particular, by using a matrix simplification technique, the sensor network topology’s sparseness issue can be tackled. Finally, the feasibility for the addressed filtering scheme is demonstrated by an example.

Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network

Abstract: In a WSN, sensor node plays a significant role. Working of sensor node depends upon its battery's life. Replacements of batteries are found infeasible once they are deployed in a remote or unattended area. Plethora of research had been conducted to address this challenge, but they suffer one or the other way. In this paper, a particle swarm optimization (PSO) algorithm integrated with an energy efficient clustering and sink mobility ((PSO-ECSM) is proposed to deal with both cluster head selection problem and sink mobility problem. Extensive computer simulations are conducted to determine the performance of the PSO-ECSM. Five factors such as residual energy, distance, node degree, average energy and energy consumption rate (ECR) are considered for CH selection. An optimum value of these factors is determined through PSO-ECSM algorithm. Further, PSO-ECSM addresses the concern of relaying the data traffic in a multi-hop network by introducing sink mobility. PSO-ECSM's performances are tested against the state-of-the-art algorithms considering five performance metrics (stability period, network, longevity, number of dead nodes against rounds, throughput and network's remaining energy). Statistical tests are conducted to determine the significance of the performance. Simulation results show that the PSO-ECSM improves stability period, half node dead, network lifetime and throughput vis-a-vis ICRPSO by 24.8%, 31.7%, 9.8 %, and 12.2%, respectively.

New approach of multi-path reliable transmission for marginal wireless sensor network

Abstract: In the application environment having dense distribution of marginal wireless sensor network (WSN), the data transmission process will generate a large number of conflicts, which will result in loss of transmission data and increase of transmission delay. The multi-path data transmission method can effectively solve the problem of large data loss and transmission delay caused by collisions. A new approach of multi-path reliable transmission for application of marginal WSN (named RCB-MRT) is proposed in this paper. It adopts redundancy mechanism to realize the reliability of data transmission, and uses concurrent woven multi-path technology to improve the transmission efficiency of data packets. Firstly, it divides the data packets that the sensor node needs to transmit into several sub-packets with data redundancy, and then forwards the sub-packets to the aggregation node through multi-path by the intermediate nodes of marginal environment. The results of our experimental tests show that the proposed multi-path reliable transmission method can effectively reduce data packet loss rate, reduce transmission delay and increase network lifetime. The method is very useful for the applications of marginal wireless sensor network.

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Abstract: Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

Fuzzy Logic Based Effective Clustering of Homogeneous Wireless Sensor Networks for Mobile Sink

Abstract: In recent trends, wireless sensor networks (WSNs) are mostly used for a number of applications where conventional networks are infeasible. To perform WSN operation, a number of sensors are deployed in desired network cross-section. Since sensors are embedded with limited resources, it has the limitation while using sensor node’s battery power. Clustering schemes are one of them which can utilize sensor’s power effectively in WSNs. In this paper, a Fuzzy Logic based Effective Clustering (FLEC) of Homogeneous Wireless Sensor Networks for Mobile Sink has been introduced. The problem associated with existing protocol, A Fuzzy logic based Clustering Algorithm for WSN to extend the Network Lifetime (LEACH-Fuzzy), is resolved in FLEC by utilizing the concept of average energy based probability and average threshold for appropriate cluster heads (CHs) selection. In addition, average energy of CHs has been also included in Fuzzy descriptors to make proper super cluster head (SCH) election among CHs. Simulation results justify that the proposed FLEC scheme outperforms over LEACH, DEEC, and LEACH-Fuzzy protocols.

Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey

Abstract: As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.

On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things

Abstract: Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data are first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs, and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with the consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links, and integration with edge computing.

LD2FA-PSO: A novel Learning Dynamic Deterministic Finite Automata with PSO algorithm for secured energy efficient routing in Wireless Sensor Network

Abstract: Wireless Sensor Networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Energy efficiency and secured data transmission are considered as the most important design goals for WSN. As the complexity of computer networks increased, the erudition of network-based attacks drew the attention of various researchers from several sectors. As a result of this, many Intrusion Detection System (IDS) have been deployed in such a way that it resolves various aspects of network security such as DoS, worms, viruses, malware, etc. Automation of IDS has been proposed in order to improve the energy efficient routing in Wireless Sensor Network in a secured manner. The proposed work incorporates the Deterministic Finite Automata (DFA) and Particle Swarm Optimization (PSO) for intrusion detection and the data transmission is done in a secured manner by determining and following the optimized route. A novel LD2FA (Learning Dynamic Deterministic Finite Automata) has been proposed so that the dynamic nature of the network is cultured. In turn, LD2FA - PSO provides the information about the node, packet and route inspection for detection and elimination of intruders so that the data transmission is done in an energy efficient manner through the optimal path. Routing through optimal path improves the overall performance of the sensor network and it has been examined through various metrics such as energy consumption, throughput, network lifetime, alive and dead nodes. The performance is evaluated by simulating in MATLAB with sensor nodes ranging from 100–700 in a region of 500 × 500 m^2 and comparing the obtained experimental results with the existing PSO, Greedy Load Balanced Clustering Algorithm (GLBCA), Genetic Algorithm (GA) based clustering, Least Distance Clustering (LDC), cluster-based IDS, light weight IDS. It is observed that, LD2FA-PSO obtained 16% increase in throughput than cluster-based IDS, almost 70% increase in throughput than light weight IDS, 6% and 32% improvement in network lifetime than PSO and GLBCA respectively, almost 30% and 54% improvement in network lifetime than GA and LDC respectively. The energy consumed is almost 3% and 6% lesser than PSO and GA, and 13% higher energy is consumed than LDC. The alive sensor node becomes nil at 2600th round which is better than cluster-based IDS and light weight IDS.

A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks

Abstract: Other than energy consumption, precision is of the utmost importance in node localization. Various wireless-sensor-network applications require the accurate information of sensor nodes’ locations. For instance, an enemy intrusion detection system (e.g., geo-fencing) needs accurate sensor nodes’ locations to detect where intruding enemies are located. As practical examples, forest fire, landslide, and water quality monitoring systems require the early identification of root causes’ exact locations before they can widely spread. In general, range-based localization techniques often yield higher accuracies because the localization estimation can be directly derived from the distance between hops and can leverage received signal strength indicator (RSSI) values but require model approximation of various hops and distances as in range-free localization techniques. However, the important factor that affects the accuracies is sensor node positioning, especially when sensor nodes (SNs) are spread across areas filled with obstructions causing less localization accuracy. Due to the diffraction caused by obstructions, the approximate distances between pairs of anchor nodes and unknown nodes using RSSI can differ substantially from the actual values. This research, therefore, aims to improve sensor node localization in situations where SNs are in areas with obstructions. We propose a novel technique, node segmentation with improved particle swarm optimization (NS-IPSO) that divides SNs into segments to improve the accuracy of the estimated distances between pairs of anchor nodes and unknown nodes. First, we determine candidate sensor nodes that could potentially be used to segment anchor nodes in the area. Such sensor nodes (STs) are those on the shortest paths between anchor nodes that appear more often than the average appearances of all sensor nodes. Then, segment nodes (SMs, sensor nodes for segmenting the anchor nodes) are selected from all the other STs based on certain specified conditions. To further improve the localization precision, we enhance the fitness function for each anchor node by taking into account the number of hops between each anchor node and unknown nodes. Furthermore, we enhance particle swarm optimization (PSO) by considering only particles that do not change positions to possibly reduce the chance of the local optimal trap. In this research, we test our proposed scheme's performance considering three forms of sensor node positioning: C-shape, H-shape, and S-shape. The simulation results show that the proposed scheme achieves higher accuracy in comparison with the recent state-of-the-art methods, i.e., hybrid discrete PSO (HDPSO), Hybrid PSO, approximate distances node localization (ADNL), the weight-search localization algorithm (WSLA), and min-max PSO techniques, particularly the situation where sensor nodes are in areas with obstacles.

Wireless Underground Sensor Networks Path Loss Model for Precision Agriculture (WUSN-PLM)

Abstract: Despite a large number of applications in the field of health care, military, ecology or agriculture, the Wireless Underground Sensor Network (WUSN) faces the problem of wireless Underground Communication (WUC) which largely attenuate the signal on the ground. For the case of precision agriculture, the motes are buried and they have to check the good growth of plants by verifying data like the water content. However, due to soil composition, the wave signal is attenuated as it travels across the ground. Thus, before a real deployment of WUSN, the prediction of the path loss due to signal attenuation underground is an important asset for the good network functioning. In this paper, we proposed a WUSN path loss for precision agriculture called WUSN-PLM. To achieve it, the proposed model is based on an accurate prediction of the Complex Dielectric Constant (CDC). WUSN-PLM allows evaluating the path loss according to the different types of communication (Underground-to-Underground, Underground to Aboveground and Aboveground to Underground). On each communication type, WUSN-PLM takes into account reflective and refractive wave attenuation according to the sensor node burial depth. To evaluate WUSN-PLM, intensive measurements on real sensor nodes with two different pairs of transceivers have been conducted on the botanic garden of the University Cheikh Anta Diop in Senegal. The results show that the proposed model outperforms the existing path loss models in different communication types. The results show that our proposed approach can be used on real cheap sensor with 87.13% precision and 85% balanced accuracy.

LAKS-NVT: Provably Secure and Lightweight Authentication and Key Agreement Scheme Without Verification Table in Medical Internet of Things

Abstract: Wireless body area networks (WBANs) and wireless sensor networks (WSNs) are important concepts for the Internet of Things (IoT). They have been applied to various healthcare services to ensure that users can access convenient medical services by exchanging physiological data between user and medical server. User physiological data is collected by sensor nodes and sent to medical service providers, doctors, etc. using public channels. However, these channels are vulnerable to various potential attacks, and hence, it is essential to design provably secure and lightweight mutual authentication (MA) schemes for medical IoT to protect user privacy and achieve secure communication. A lightweight mutual authentication and key agreement (MAKA) scheme was designed in 2019 to guarantee user privacy, but we found that the scheme does not withstand impersonation, stolen senor node and leaking verification table attacks, and it does not also ensure anonymity, untraceability and secure mutual authentication. This paper proposes a provably secure and lightweight MAKA scheme for medical IoT, called LAKS Non-verification table (NVT), that does not require a server verification table. We assess LAKS-NVT’s security against various potential attacks and demonstrate that it achieves secure MA between sensor node and server using Burrows-Abadi-Needham logic. We employ the well-known Real-Or-Random which is random oracle model to prove that LAKS-NVT provides a session key security. In addition, the formal security verification using the widely-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool has been performed and the results show that LAKS-NVT is also secure. We compare LAKS-NVT’s performance against contemporary authentication schemes, and verify that it achieves better security and comparable efficiency. The practical perspective of LAKS-NVT is also carried out via the Network Simulator 2 (NS2) simulation study.

Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology.

Abstract: The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs) WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring WBAN is a network of wearable devices implanted in or on the human body The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc) round-the-clock from patients that demand secure, optimal and efficient routing techniques The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR) Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Abstract: The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

P2P networking based internet of things (IoT) sensor node authentication by Blockchain

Abstract: Sensor nodes play a major role in IoT environment, and each sensor is a peer to peer networking. Due to limited physical size, IoT sensor nodes must have light-weight authentication protocol. The Internet of Things (IoT) is a collection of various technical elements. It is expected that interworking between heterogeneous terminals, networks, and applications. They will accelerate through the liberalization of the IoT platform. As a result, many technical and administrative security threats will arise in the IoT environment. Sensor node protocols must be light-weight and secure. As IoT devices are used for various purposes, for some devices that require performance, the OS with a high-performance chipset that works, most passwords protocol. However, to turn on / off the lights IoT devices that perform simple tasks such as based on a low-performance chipset with no OS running. If it does not support encryption protocol or certificate, then it is vulnerable, and it does not have enough performance to handle. Therefore, in this paper, Block-chain-based IoT device is proposed to get a more secure authentication scheme.

Efficient data aggregation with node clustering and extreme learning machine for WSN

Abstract: Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN.

An Energy Optimization Clustering Scheme for Multi-Hop Underwater Acoustic Cooperative Sensor Networks

Abstract: In this paper, we propose an improved energy optimization clustering algorithm (EOCA) for the multi-hop underwater acoustic cooperative sensor networks (UWA-CSN) due to the limited energy supply of the underwater sensor nodes. The proposed EOCA scheme considers multiple factors, such as the number of neighbor nodes, the residual energy of each node, the motion of the sensor nodes caused by the ocean currents, and the distance between the sink node and each underwater sensor node. All these factors are considered within the framework of the multi-hop cooperative communication. Moreover, in the proposed energy optimization scheme, we define a residual energy based maximum effective communication range (reE-MECR) according to the residual energy of each underwater sensor node, which can adaptively control the energy consumption of data transmission for each hop. Compared with the existing clustering scheme, the experimental results demonstrate that the proposed EOCA scheme can not only prolong the life time of the multi-hop UWA-CSN, but also keep a good communication and networking performance, including the package delivery ratio, the energy consumption efficiency, and the network coverage area. The proposed scheme can obtain both the clustering benefit and the cooperative communication benefit for the multi-hop UWA-SN in terms of energy saving in underwater environments.

Energy-Aware WBAN for Health Monitoring Using Critical Data Routing (CDR)

Abstract: Wireless body area network (WBAN) is the subfield of Wireless Sensor Network, employs in the area of monitoring the health of the patient. WBAN is also known as wireless body sensor network in which sensor nodes are fused inside the body of the person to detect their physiological changes. After processing or comparing those obtained data with the pre-stored default value, the packets are transmitted to the base station. Due to the inner-body sensor node, replacement of the battery may hazardous for the person. So, storing up and saving of energy is the main focus inside the WBANs. In this research, we employ the critical data routing code for transmitting the relevant data from inner-body node to the on-body medical super sensor (MSS) node. Here, MSS act as a controller that can manage all the injected sensor node inside the body of the person as their member. And, if inner-body sensor node is detecting any corporal activities from the human body then it compares those data with the pre-stored threshold level value of that sensor node, and if sensor obtained more deviation in their results then it follows the critical data routing (CDR) for the transmission process, unless it goes to the rest mode. In other words, the sensor node can only be transmitted the critical packet data to their near-by controller and avoiding the redundant picking of normal packet data. By following this procedure we can save the maximum of energy for the sensor so that it alive for the greatest period of time that lead to continuous monitoring of the patient and also maximizes the lifespan of the network. Simulation can be done on MATLAB that can show the finest outcomes in terms of energy spending, network lifespan, throughput- efficiency, hold up by any one of the steering protocol when there is a comparison between CDR, REEC, and SIMPLE respectively.

A distributed coverage hole recovery approach based on reinforcement learning for Wireless Sensor Networks

Abstract: In Wireless Sensor Networks (WSNs), various anomalies may arise and reduce their reliability and efficiency. For example, Coverage Hole can occur in such networks due to several causes, such as damaging events, sensors battery exhaustion, hardware failure, and software bugs. Modern trends to use relocation of deployed sensor nodes when the manual addition of nodes is neither doable nor economical in many applications have attracted attention. The lack of central supervision and control in harsh and hostile environments have encouraged researchers to shift from centralized to distributed node relocation schemes. In this paper, a new game theory approach based on reinforcement learning to recover Coverage Holes in a distributed way is proposed. For the formulated potential game, sensor nodes can recover Coverage Holes using only local acquaintances. To reduce the coverage gaps, the combined action of node reposition and sensing range adjustment is chosen by each sensor node. The simulation results prove that, unlike previous methods, the proposed approach can sustain a network overall coverage in the presence of random damage events.

On the Performance of Multihop Cognitive Wireless Powered D2D Communications in WSNs

Abstract: This paper studies the performance of multi-hop cognitive wireless powered device-to-device (D2D) communications in wireless sensor networks (WSNs). In our analysis, each sensor node harvests energy from multiple dedicated power beacons and shares the spectrum resources with multiple primary receivers (PRs) using underlay cognitive radio. Additionally, we consider a practical scenario of cognitive wireless powered D2D communications in WSNs, where the knowledge of interference channels is assumed to be imperfect. To improve the network performance, we propose two user scheduling schemes, namely dual-hop scheduling (DHS) and best-path scheduling (BPS) schemes. We then investigate the performance of the proposed scheduling schemes in terms of outage probability and outage floor. Through numerical results, we show that BPS scheme significantly outperforms DHS scheme, which in turns outperforms the state-of-the-art solution. Moreover, the advantages and drawbacks of each scheme are analyzed and discussed comprehensively. We also point out that the inaccurate knowledge of the interference channels significantly affects any performance metric of multi-hop cognitive D2D communications in WSNs such as outage probability, outage floor, and performance loss.