Static Wireless Sensor Networks Research Articles

Towards invariant extended Kalman filter-based resilient distributed state estimation for moving robots over mobile sensor networks under deception attacks

This paper studies the resilient distributed state estimation problem for moving robots over wireless sensor networks (WSNs) under deception attacks. The state of a moving robot consists of its position and orientation, evolving according to a nonlinear motion model, and the WSNs are mobile. These settings make the problem more challenging than most existing researches on the resilient distributed state estimation which recover stationary or linear states by static WSNs. To achieve the resilient distributed state estimation for the robot over a mobile sensor network (MSN), we propose a novel invariant extended Kalman filter-based resilient distributed state estimation approach. Specifically, based on the mappings between a Lie group and its Lie algebra, we first define a new type of estimation errors and linearize it. Utilizing the linearization results, the calculation of the current prior estimate based on the last posterior one is achieved by each sensor, which avoids the estimation biases induced by the computation of Jacobian matrices. Then, sensors in the MSN fuse the local prior estimates of their neighbors according to the covariance intersection fusion rule, obtaining more accurate estimates than their local prior ones. Finally, measurements collected by the sensors are used to correct the estimates. In the correction process, an adaptive gain is designed for each sensor to restrict the negative effects of the attacks on the estimates, which is crucial for the accomplishment of the resilient estimation task. We illustrate the performance of the approach by convergence analysis of the estimation errors based on the boundedness of the covariance matrix estimates computed in the state estimation process and verify it by simulation and experimental results.

The Effect of Symmetry on the De-Anonymizability of Social Networks

This paper studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

FlowerCast: Efficient Time-sensitive Multicast in Wireless Sensor Networks with Link Uncertainty

This article studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from the source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates the Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with a high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths’ addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

Holes Problem Solving in Khalimsky Topology Protocol for Wireless Sensor Networks (WSNs)

In this paper, we propose an approach solving the holes problem in Wireless Sensor Networks (WSNs) based on Khalimsky k-Clustering and data routing protocol (MDKC). The aim of this solution is to establish optimized data routing paths between isolated nodes/clusters and the Sink in noisy environment with the presence of obstacles. This approach is an improvement of a previous work deploying stationary WSN not dealing with the problem of “holes”. At first, the MDKC algorithm divides the WSN into k-hop [Formula: see text] compact dynamic clusters. For each cluster, a node is elected cluster-head in its k-neighborhood according to some criteria such as the remaining energy, the k-degree and the communication probability average. Then, some nodes are selected as Khalimsky anchors to optimize the intra-cluster data routing process. The Khalimsky anchors at the border layers ensure the inter-cluster data routing between adjacent clusters. In the next phase of MDKC, Mobile Collectors (MCs) are used for data collecting and relaying from isolated nodes/clusters to the connected Khalimsky’s nodes. The simulation results prove that MDKC minimizes the energy consumption and improves the connectivity rate between sensors and the delivery rate compared to some existing approaches.

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

Linguistic Fuzzy Trust Model over Oscillating Wireless Sensor Networks

Simulation of the Linguistic Fuzzy Trust Model (LFTM) over oscillating Wireless Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Networks (BTRM-WSN) in terms of the accuracy and the average path length suggested by each model is presented. Both models give quite good and accurate outcomes over oscillating WSNs. Also it must be mentioned that the evaluation environment used here is Trust and Reputation Model Simulator for WSN.

Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Minimal Exposure Paths in Time-Varying Fields: A Semi-Lagrangian Approach

In the context of Wireless Sensor Networks (WSNs), the Minimal Exposure Path (MEP) represents an important metric to evaluate the quality of network services. Most of the current literature is concentrated on stationary sensors, while few works have addressed the existence of moving nodes. Mobile Wireless Sensor Networks (MWSNs) present a great potential for detecting invaders compared to static ones, but dealing with dynamic sensors significantly increases the coverage complexity since the overall exposure of the sensor field depends on time. Therefore, in this letter, we propose an approach to compute Minimal Exposure Paths in time-varying fields based on a control optimization method called semi-Lagrangian (SL) scheme, in such a way that an intruder will be able to penetrate the dynamic field with the lowest exposure. The SL has already been proven to reach the optimal Minimal Exposure Path (MEP) on static WSNs, but concerning dynamic nodes, the proof is much more complicated. Then, we propose a heuristics that provides convergence of the SL algorithm to a result we conjecture to be the optimal one. Results with different time-varying sensor models in obstacle-free and cluttered environments have been presented and discussed.

DCM–RPL: A Distributed Competition Mobility Management Scheme for RPL/6LoWPAN

The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as a reliable and energy-efficient routing mechanism has become the de facto standard routing protocol of IPv6 over Low-powered Wireless Personal Area Network (6LoWPAN). However, it is designed for static wireless sensor networks and is not suitable for mobile scenarios. When mobile nodes (MNs) exist in the 6LoWPAN network, repeated link disruptions and topology changes lead to data packets loss and affect network stability. An increasing number of mobile applications force the improvement of RPL protocols for supporting mobility. In this study, a distributed competition and mobility-aware RPL routing protocol (DCM–RPL) is proposed to carry out mobility management and alleviate the network burden in the mobile scenario. A new preferred parent-discovering mechanism for MNs based on a distributed competition algorithm integrated with Objective Function is designed, which can dramatically reduce power consumption in the new parent node (PN)-discovering phase. To reduce data loss during the node-moving process, a packet-caching mechanism is proposed in the PN update phase. Finally, test-beds are set up and simulated in the Cooja simulator. Comparing the performance of the DCM–RPL in terms of switching delay, energy consumption, and PDR with the RPL and its mobility-aware variant (mRPL and EC–MRPL); the simulation results show that the proposed scheme maintains efficiency.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Node Replication Attack Detection in Distributed Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

Large Scale Air Pollution Monitoring Using Static Multihop Wireless Sensor Networks

In the proposed paper, an effective system for a large-scale air pollution monitoring on a live basis is developed and tested. It is an Audrino based platform equipped with pre-calibrated sensors to sense gases like sulphur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), and particulate matter (PM10) in the air. Audrino board with the gas sensors, global system for mobile communications (GSM) and ZigBee module forms the wireless sensor motes used for the field deployment. Wireless sensor network built with ZigBee links can be scaled up using the GSM connectivity to interface with the external world. Air pollution monitoring is performed using a system of sensor nodes with the help of wireless communication via a ZigBee protocol. We proposed a static wireless sensor network to supervise air contamination level, and a multi-hop algorithm is implemented over ZigBee WSN. Designed low-cost WSN based prototype is tested under various atmospheric conditions which had provided stable and dependable data of air contaminants.

Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks

This article is concerned with collecting stored sensor data from a static Wireless Sensor Network utilizing a group of mobile robots that act as data mules. In this scenario, the static sensor nodes’ locations are known a priori, and the overall optimization problem is formulated as a variation of the Traveling Salesman Subset-tour Problem (TSSP). The constraints that are taken into account include: (a) the pairwise distance between static nodes, (b) the maximum time interval between consecutive visits of each static node, (c) the service time that is required for the collection of the sensor data from the mobile element that visits this sensor node, and (d) the energy efficiency for the mobile nodes. The optimal mobile robot paths are computed using an enhanced Mobile Element Scheduling scheme. This scheme extracts the sequential paths of the mobile elements in an attempt to eliminate any sensor data loss.

Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks

The development of the wireless sensor networks technology commonly named WSNs has been gaining a significantly increased amount of attention from researchers over the last few decades. Its large number of sensor nodes is one of the features that makes it beneficial to the technology. The sensors can communicate with each other to form a network. These sensor nodes are generally used for diverse applications, such as pressure monitoring, fire detection, target tracking, and health monitoring, etc. However, the downside is that WSNs are often deployed in hostile, critical environments where they do not restrain physical access. This reality makes them incredibly vulnerable to clone node attacks or node replication attacks. The adversary can capture the legitimate sensor nodes, extract them and then collect some sensitive information, such as node ID, keys and perform a replication attack. This possibility will afterward facilitate the attacker to be able to take control of the whole network and execute the same functions as that of the authorized nodes. Based on this vulnerability, it is of great importance for researchers to invent a detection protocol for the clone attacks as well as a mitigation method. From all of the researches that have been published, a lot of them proposed some techniques to detect the clone node attacks and also to mitigate the attacks. However, almost none of them semantically focused on the security layer establishment. Based on this fact, we proposed an ontology-based approach Ontology for Replication Attacks in Static Wireless Sensor Networks “ORASWSN”, which can semantically be used for the detection and mitigation of the attacks by taking into consideration the importance of using security layers.

Development of Energy Efficient and Optimized Coverage Area Network Configuration to Achieve Reliable WSN Network Using Meta-Heuristic Approaches

Energy optimization and coverage area optimization of wireless sensor networks (WSN) are two major challenges to accomplish reliability optimization in the field of WSN. Reliability optimization in the field of WSN is directly connected to the performance and efficiency and consistency of the network. In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. They have configured an optimized route/path using ant colony optimization (ACO) algorithm and deployed static WSN nodes. After configuring an efficient network, if we can maximize the coverage area, then we can ensure that the network is a reliable network. For coverage area optimization, they used a hybrid differential evolution-quantum behaved particle swarm optimization (DE-QPSO) algorithm. The result has been compared with existing literature, and the authors found good results applying those meta-heuristic and hybrid algorithms.

Mobile Wireless Sensor Networks: A Framework Based Solution for Physical Security Attacks

Mobile wireless sensor networks are made up of resource-constrained sensor nodes.The nodes in Mobile wireless sensor networks tend to move wirelessly from one location to another. In physically unattended and insecure areas, these networks are installed where they are vulnerable to numerous security threats, including physical security threats.Clone node attack is a physical attack in which the adversary physically compromises legitimate nodes and copies their data onto fake nodes to create clones of captured nodes. In static wireless sensor networks, comprehensive work isalready done to mitigate and detect the underlying attack. Mitigating and detecting this assault on mobile wireless sensor networks, however, remains a critical issue. To our knowledge, there is no framework-based method for bolstering multiple attacks onmobile wireless sensor networks.Consequently, this article proposes a framework-based approach that mitigates multiple attacks on mobile WSNs. To provide proof-of-concept for the framework, along with clone node attacks solution to jamming attacks is alsoincorporated in the framework. The implementation of the framework is done with the COOJA simulator and Contiki OS.

JSMCRP: Cross-Layer Architecture based Joint-Synchronous MAC and Routing Protocol for Wireless Sensor Network

The exponential rise in wireless technologies and allied applications has revitalized academia-industries to develop more efficient and economic routing solution to meet Quality-of-Service (QoS) provision. Amongst the major wireless communication systems, Wireless Sensor Network (WSN) is the most sought technology for defense surveillance, healthcare monitoring, industrial monitoring and control, civic and strategic infrastructure surveillance etc. Additionally, the up surge in Internet of Things (IoT) and Machine to Machine (M2M) communication systems too have broadened the horizon for WSNs based communication. However, the parallel increase in communication environment, complex network conditions etc confine efficacy of the classical WSN protocols. Dynamic network and node conditions often force classical protocols to undergo pre-mature link outage, delayed transmission, energy exhaustion and eventual QoS violation. To address such issues enhancing Median Access Control (MAC) and allied routing decision can have vital significance. However major existing efforts either focus on MAC enhancement or routing optimization, even under static WSN topology. Considering it as motive in this paper a highly robust “Cross-layer architecture based Joint-Synchronous MAC and Routing Protocol for WSN communication (JSMCRP)” has been developed. Being cross layer model JSMCRP protocol employs Application Layer, Network Layer, MAC Layer and PHY Layer to perform Network Adaptive MAC scheduling and Dynamic Routing Decision. JSMCRP employs Data Traffic Assessment, Prioritization and Scheduling (DTAPS), Proactive Network Monitoring and Knowledge (PNMK), Dynamic Congestion Index Estimation (DCIE), Adaptive Link Quality, Packet Injection Rate and Cumulative Rank Sensitive Routing Decision (CRSRD) to perform routing decision. Additionally, exploiting dynamic network/node conditions it performs Cognitive MAC scheduling to ensure QoS centric communication over IEEE 802.15.4 protocol stack. JSMCRP exhibited higher PDR, lower PLR and low delay under varying network conditions, suitable for real-time communication over constrained mobile WSN conditions.

An Efficient Multi-constraint Relay Deployment Strategy for Wireless Sensor Network

The relay node deployment for wireless sensor network has many application constraints, such as the energy efficiency, stable connectivity, survivability and low cost. This paper explores this problem for static wireless sensor network. An efficient multi-constraint deployment strategy is proposed to save the deployment cost by deploying minimal relay nodes in candidate locations, to improve network lifetime by minimizing the maximum energy consumption of the relay nodes, and to improve network reliability by meeting a preset reliability threshold of the wireless communication. In order, the proposed strategy covers (a) extraction of three-dimensional discrete deployment space by shortest path search, (b) reliability evaluation of wireless channel in three-dimensional environment with electromagnetic obstacle, (c) energy consumption model for wireless communication with retransmission mechanism, (d) integer linear program-based formulation optimizing the amount, locations and lifetime of deployed relay nodes under the constraints of network survivability and reliability. The comparative simulation results indicate that the proposed strategy can reduce the solution time to minutes while the solution time of the existing methods is in hours when deploying dozens of sensors, and the result of the proposed deployment strategy can use the same number or slightly more relay nodes to achieve the best network lifetime, but the proposed strategy is not recommended to optimize the network reliability. In short, the proposed deployment strategy can ease the efficiency bottleneck while maintaining or even improving the algorithm performance.

Enhanced Particle Swarm Optimization for Effective Relay Nodes Deployment in Wireless Sensor Networks

One of the critical design problems in Wireless Sensor Networks (WSNs) is the Relay Node Placement (RNP) problem. Inefficient deployment of RNs would have adverse effects on the overall performance and energy efficiency of WSNs. The RNP problem is a typical example of an NP-hard optimization problem which can be addressed using metaheuristics with multi-objective formulation. In this paper, we aimed to provide an efficient optimization approach considering the unconstrained deployment of energy-harvesting RNs into a pre-established stationary WSN. The optimization was carried out for three different objectives: energy consumption, network coverage, and deployment cost. This was approached using a novel optimization approach based on the integration of the Particle Swarm Optimization (PSO) algorithm and a greedy technique. In the optimization process, the greedy algorithm is an essential component to provide effective guidance during PSO convergence. It supports the PSO algorithm with the required information to efficiently alleviate the complexity of the PSO search space and locate RNs in the spots of critical significance. The evaluation of the proposed greedy-based PSO algorithm was carried out with different WSN scenarios of varying complexity levels. A comparison was established with two PSO variants: the classical PSO and a PSO hybridized with the pattern search optimizer. The experimental results demonstrated the significance of the greedy algorithm in enhancing the optimization process for all the considered PSO variants. The results also showed how the solution quality and time efficiency were considerably improved by the proposed optimization approach. Such improvements were achieved using a simple integration technique without adding to the complexity of the system and introducing additional optimization stages. This was more evident in the RNP scenarios of considerably large search spaces, even with highly complex and challenging setups.