![An example to depict the selection process. A circle node represents a sensor node, and a rectangle node represents a mobile target. A dotted circle represents the maximum transmission range of a node [14].](https://www.researchgate.net/profile/Khaled-Elleithy/publication/265167908/figure/fig1/AS:295953434202117@1447572205550/An-example-to-depict-the-selection-process-A-circle-node-represents-a-sensor-node-and-a_Q320.jpg)
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A Dynamic Clustering Algorithm for Object Tracking and Localization in WSN
Abstract and Figures
A Wireless Sensor Network (WSN) is an assemblage of cooperative sensor nodes acting together into an environment to monitor an event of interest. However, one of the most limiting factors is the energy constrain for each node; therefore, it is a trade-off is required for that factor in designing of a network, while reporting, tracking or visualizing an event to be considered. In this paper, two object tracking techniques used in Wireless Sensor Networks based on cluster algorithms have been combined together to perform many functions in the proposed algorithm. The benefit of using clusters algorithms can be count as the detection node in a cluster reports an event to the Cluster Head (CH) node according to a query, and then the CH sends all the collected information to the sink or the base station. This way reduces energy consuming and required communication bandwidth. Furthermore, the algorithm is highly scalable while it prolongs the life time of the network.
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... e authors adopted a two-object tracking strategy used in WSNs primarily based on cluster algorithms which have been combined together to perform many features in the proposed algorithm. Musafer et al. 2 Complexity benefited from using cluster algorithms to count and detect node in a cluster by reporting an event to the cluster center (also cluster head) node according to a query, conveying all audible information to the base station [19]. Numerous device-free localization methods are launched in wireless systems. ...
Localization is recognized among the topmost vital features in numerous wireless sensor network (WSN) applications. This paper puts forward energy-efficient clustering and localization centered on genetic algorithm (ECGAL), in which the residual energy, distance estimation, and coverage connection are developed to form the fitness function. This function is certainly fast to run. The proposed ECGAL exhausts a lesser amount of energy and extends wireless network existence. Finally, the simulations are carried out to assess the performance of the proposed algorithm. Experimental results show that the proposed algorithm approximates the unknown node location and provides minimum localization error.
... Where M is the number of positive node The positioning performance is affected by the accuracy of the path loss model. If the loss model can accurately estimate the propagation distance, the positioning accuracy will be improved [14]; otherwise, it will reduce. Here, we use the RSSI-based circular correction method to solve the accuracy problem [21]. ...
Target tracking is a typical and important application of wireless sensor networks (WSNs). In consideration of the network scalability and energy efficiency for target tracking in large-scale WSNs, it has been employed as an effective solution by organizing the WSNs into clusters. However, tracking a moving target in cluster-based WSNs suffers a boundary problem when the target moves across or along the boundaries of clusters, as the static cluster membership prevents sensors in different clusters from sharing information. In this paper, we propose a novel mobility management protocol, called hybrid cluster-based target tracking (HCTT), which integrates on-demand dynamic clustering into a cluster-based WSN for target tracking. By constructing on-demand dynamic clusters at boundary regions, nodes from different static clusters that detect the target can temporarily share information, and the tracking task can be handed over smoothly from one static cluster to another. As the target moves, static clusters and on-demand dynamic clusters alternately manage the target tracking task. Simulation results show that the proposed protocol performs better in tracking the moving target when compared with other typical target tracking protocols.
Target tracking is one of the non trivial applications of wireless sensor network which is set up in the areas of field surveillance, habitat monitoring, indoor buildings, and intruder tracking. Various approaches have been investigated for tracking the targets, considering diverse metrics like scalability, overheads, energy consumption and target tracking accuracy. This paper for the first time contributes a survey of target tracking protocols for sensor networks and presents their classification in a precise manner. The five main categories explored in this paper are, hierarchical, tree-based, prediction- based, mobicast message-based tracking and hybrid methods. To be more precise, the survey promotes overview of recent research literature along with their performance comparison and evaluation based on simulation with real data. Certainly this task is challenging and not straight forward due to differences in estimations, parameters and performance metrics, therefore the paper concludes with open research challenges.
We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
Advances on wireless communication and sensor systems enabled the growing usage of Wireless Sensor Networks. This kind of
network is being used to support a number of new emerging applications, thus the importance in studying the efficiency of
new approaches to program them. This paper proposes a performance study of an application using high-level mobile agent model
for Wireless Sensor Networks. The analysis is based on a mobile object tracking system, a classical WSN application. It is
assumed that the sensor nodes are static, while the developed software is implemented as mobile agents by using the AFME framework.
The presented project follows a Model-Driven Development (MDD) methodology using UML (Unified Modeling Language) models. Metrics
related to dynamic features of the implemented solution are extracted from the deployed application, allowing a design space
exploration in terms of metrics such as performance, memory and energy consumption.
A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption.
We propose two energy efficient algorithms for locating a target object moving in an area covered by a wireless ad hoc network. The first algorithm developed conserve energy by efficiently identifying sensor nodes, as Home Nodes, and use only local messages between neighboring nodes to follow the trail of the object. Since we avoid the long-range transmission and maximize the localization, the algorithms reduce the communication cost. The dynamic nature of the second algorithm exploits the predefined parameters such as the object velocity. Our algorithm represents query shipping against the conventional data shipping as a means to reduce the amount of data being shipped across the network. Hence, it locates the objects over the network with minimal energy conservation using short-range message transmissions. The performance analysis (both experimental and theoretical) shows the effectiveness of the two algorithms in comparison to another tracking algorithm. Copyright © 2006 John Wiley & Sons, Ltd.
Developing an efficient object tracking system has been an interesting challenge in wireless sensor network communities. Due to the severe resource constraints of sensor hardware, the accuracy of the tracking system could be compromised by the processing power or energy consumption. A sophisticated tracking algorithm is therefore not applicable to sensor applications, and any tracking system should explicitly consider the energy issue. In this paper, we present energy-aware location error handling techniques, namely error avoidance and error correction, to prevent and handle errors efficiently. Real situations such as an unexpected change in the mobile event’s direction, failure of event detection, or transmission failure of an error message are considered in the design of the proposed mechanisms. The prototype system is built with real sensor hardware, and the functionality is validated in real experiments. The experimental evaluation, together with simulation analysis, shows that the proposed mechanism saves energy while achieving good tracking accuracy.
The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
Wireless sensor network is an emerging technology that enables remote monitoring objects and environment. This paper proposes a protocol to track a mobile object in a sensor network dynamically. The previous researches almost focus on how to track object accurately and they do not consider the query for mobile sources. Additionally, they need not report the tracking information to user. The work is concentrated on mobile user how to query target tracks and obtain the target position effectively. The mobile user can obtain the tracking object position without broadcast query. The user is moving and approaching the target when he/she knows the target’s position. Wireless sensor networks can assist user to detect target as well as keep the movement information of the target. Sensor nodes establish face structure to track the designated target and keep target tracks. The source follows the tracks to approaching target. To chase the object quick and maintain an accurate tracking route, the sensors cooperate together to shorten the route between target and source dynamically. A source can quickly approach a target along a shortened route. Finally, we compare the proposed scheme with three flooding-based query methods. By the simulation results, the proposed protocol has better performance than that of flooding-based query methods.
In location tracking, there is a trade-off between data accuracy of mobile targets and energy efficiency of sensor nodes. If the number of nodes is increased to track a mobile target, the level of data accuracy increases, but the energy consumption of nodes increases. In this paper, we propose a new location tracking scheme that considers these two factors. The proposed scheme is designed to track mobile targets in a cluster-based sensor network. In order to increase the energy efficiency of sensor nodes, a portion of nodes that detect the mobile target is selected to track the target using the backoff procedure. In addition, we consider data accuracy as controlling the number of nodes by varying the transmission range of the nodes. We perform a simulation to evaluate the performance of the proposed scheme over sensor networks. We provide simulation results comparing the proposed scheme with the general approach. The simulation results show that the proposed scheme has the excellent performance over a broad range of parameters.