Dynamic clustering for acoustic target tracking in wireless sensor networks

Dynamic clustering is an effective approach in target tracking. However, the energy consumption of cluster head is relatively high, and the failure of cluster head will lead to target loss. In this paper, we proposed a new cluster with two heads which can track the targets collaboratively based on improved extended Kalman filter with a changeable sampling period. An auxiliary cluster head, selected according to the principles of maximum residual energy and shortest distance, is used to predict the target trajectory using improved EKF. The targets are tracked collaboratively by the cluster head and auxiliary head with a lightweight fault-tolerant mechanism. An adaptive sampling algorithm is designed based on average velocity estimation to change the sampling period, and the target loss probability and energy consumption are reduced significantly. The protocol is tested extensively and compared to other existing methods. The results clearly indicate the benefits of our algorithm in terms of the tracking accuracy, energy consumption and target loss probability.

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

The present cluster head selection is fixed as a static node, so the cluster head becomes malicious the entire cluster communication is discarded in wireless sensor networks (WSNs). As per our new approach, the cluster heads are using fault tolerance schemes supports for selection cluster head through the game theory. This process may take additional energy to detect malicious cluster heads and to recover the network communication in the cluster. This article proposes fault-tolerant backup cluster head (BCH) selection influenced by game theory in wireless sensor networks WSNs by utilizing less energy. In WSNs, the malicious cluster heads can be exhibited efficiently using game theory through the different network conditions such as scalability, mobility, and energy utilization. Due to scalable WSNs, the cluster node is increasing in the cluster head becomes malicious thus network lifetime is reduced. So, the fault-tolerant BCH technique is incorporated to increase the lifetime of WSNs. So, the efficiency of the sensor network is reduced due to the cooperation of malicious and normal nodes. The malicious cluster heads being detected based on node parameters and the clustered network operations are carried out through fault-tolerant backup cluster head in WSNs. The simulation results have obtained for the proposed scheme compared with the network parameters such as dead nodes, alive nodes, and average energy of active nodes in the network.KeywordsWireless sensor networkGame theoryFault-tolerant networkCluster head selectionBackup cluster head

Maneuvering target tracking in wireless sensor networks (WSNs) has been a prominent research topic over the past twenty years. In many situations, WSNs are vulnerable to natural or man-made interference, resulting in uncertainty problems. In this paper, we propose an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain WSNs. First, based on the Generalized Pseudo-Bayesian estimator of first order (GPB1) and the unscented information filter (UIF), a GPB1-UIF algorithm with estimation feedback is presented. Inspired by the theory of information quality, an information-quality metric framework is proposed for sensor selection in uncertain WSNs. And the information-theoretic selection metrics are redesigned based on consistency measuring operator. Moreover, without knowing the number of selected sensors a priori, a multi-objective optimization problem is considered to determine the final selection strategy. Simulation results demonstrate that the proposed approach outperform the previous sensor selection methods.

Maximisation of network lifetime is the issue of prime importance in Wireless Sensor Network (WSN). An energy–efficient clustering algorithm with load balancing can achieve this challenging goal. We propose Density–based Dynamic Clustering (DDC) algorithm for clustering and cluster head election mechanism with the use of independence set. Furthermore, a distributed algorithm (DISD - Distributed Independence Set Discovery) is designed for cluster head election in O(1) complexity per sensor node. In this, we dynamically find an optimal sensor cover to dominate maximum sensors within a cluster, therefore forming an InDependence Set (IDS). Only the members of IDS are allowed to participate in the cluster head selection (rotation), thereby establishing load balancing and energy management. Our model is designed for dual modes: low– and high–traffic session. According to the flow of packets, the sleep management technique is modified. Network simulator is used for the simulation of our proposed algorithms.

Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations.

Wireless sensor networks consist of large number of sensor nodes that have an ability to interact with the surrounding environment. The importance of finding the suitable protocol to estimate the trajectory for any moving target in a certain environment is a case of study for researchers. In this paper, new and accurate protocol for target tracking with minimum energy consumption is proposed. The proposed approach uses the static cluster and dynamic cluster head mechanism (SCDCH) to collect data from the active nodes, and then forwards it to the cluster head (CH), which in turn will send the collected data to the base station, without changing cluster border during the network's lifetime. Simulation results show that the proposed protocol has higher accuracy and save more energy compared with static clustering protocols which in turn increases the network lifetime.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

In this paper, 3D target tracking in wireless sensor network (WSN) is considered. Unlike other published work, this paper addresses the problem of estimating locations of different targets in 3D environment. Each sensor makes T observations to get local decisions. These decisions are modulated using on-off keying (OOK) and sent to a central node called fusion center. The fusion center detects all decisions sent from all sensors to process them in order to estimate the targets locations using maximum likelihood estimator. This approach generalizes the existing work in the literature about a single target localization if we considered that the number of targets equal one. The corresponding Cramer-Rao lower bound (CRLB) has been also derived. Furthermore, simulation results are provided and discussed. 

Since real-time and communication amount is crucial for the wireless sensor network target tracking, the performance of target tracking in the wireless sensor network is critically depended on real-time and communication amount reduction. This paper presents a target tracking method based on distributed adaptive particle filtering in binary wireless sensor network. Based on dynamic clustering, the adaptive particle filter receives the observations from children nodes and formulates the local estimate with the cluster head as the processing center. Simulation results show that the method can effectively improve the real-time tracking and reduce communication amount.

In Wireless Sensor Networks (WSN), cluster networks structures have the advantage of providing scalable and energy-efficient solutions as the sensor nodes are highly energy-constrained.In this paper, we propose a dynamic clustering algorithm for WSN which is in application to event monitoring.In this clustering algorithm, the cluster head selection approach takes the received signal strength residual energy of node and distance between the cluster head and the sink node into consideration.Through this method, the cluster size is constrained and the energy consumption of intra-cluster communication is reduced.The simulation results show that the proposed algorithm can efficiently conserve energy and prolong the lifetime of WSN, especially when the sink node is far from the network.

Target localization based on energy considerations in distributed sensor networks