BAYESIAN LOCALIZED ENERGY OPTIMIZED SENSOR DISTRIBUTION FOR EFFICIENT TARGET TRACKING (Preprint)
Authors: Aloncia 2nd Aloncia 
Published: August 2019

BACKGROUND

Existing approaches for sensor node localization such as location anonymization algorithm [8], typically focus on aggregating k-anonymous locations with the aim of providing high quality location monitoring system. Say for example, Trajectory-based data forwarding [9] using link delay model has developed a data forwarding scheme to light traffic vehicular ad hoc networks. A passive vehicular traffic measurement to increase sensor time synchronization error has been designed [10] using autonomous passive localization algorithm.
The WSNs are progressively being imagined for productive information gathering, from a geological area of intrigue. In a few observation utilizations of WSNs, target following is one of the principle destinations once the sensor nodes are confined. In the meantime vitality effective target following has been broadly examined in the proceedings. In [11], Channel Adaptive Multiple Input Multiple Output instrument has intended to diminish the intricacy while identifying objects are planned. A trial exertion dependent on Redundant Radix-based Approach [12] has been intended for broadening the system lifetime. In [13], astute portable focus in a versatile sensor arrange has been investigated by showing ideal development strategy. The DOC [Depth of Coverage] approach is used to optimize the detection capability of this class of sensor networks for hundreds of cooperative agents, while minimizing their energy consumption and avoiding collisions with the obstacles. The results show that the DOC [Depth of Coverage] approach significantly has improved the probability of detection compared to other scalable strategies known as uniform, [22], [23] grid, random and stochastic gradient methods.

First, tracking has been studied for minimization of sensor installation cost [14]. Second target tracking has been considered as an anomaly detection problem [15] by applying sequential testing. Third in certain cases, target tracking has been considered as an optimal decision rule problem [16] using Bayesian formulation. Fourth, few efforts has been conducted based on distributed target tracking [18] under ideal binary sensor model. Finally, a few target tracking approaches have used adaptive node selection [19] for reducing the energy cost and computational complexity as well as ensuring the tracking accuracy. A voronoi-based distributed sensor handover protocol [20] has been designed using localized algorithm with the objective of target tracked ratio, i.e., the quality of target tracking service. A quantized measurement fusion framework has been investigated in [21] using probabilistic quantization scheme to reduce the average communication energy saving.
In contrast all the above mentioned approaches, this paper emphasize accuracy of target tracking, energy consumption rate with localized node density. The proposed BLEOSD scheme is robust and efficient target tracking approach. It provides competitive tradeoffs between energy-efficiency, tracking accuracy, and time for target tracking.

OBJECTIVE

In wireless sensor network application, the localization of nodes are carried out for extended life time of the node. Many applications in wireless sensor network perform localization of nodes over an extended period of time with energy variance. However, optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this work, it is proposed to develop a Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme for efficient target tracking in Wireless Sensor Network. The sensor node localized with Bayesian average scheme that estimates the sensor node’s energy are optimized as per data transfer capacity verification. The Bayesian average energy level of the sensor network is compared with the energy of each sensor node. The sensor nodes are localized and energy distribution based on the Bayesian energy estimate for efficient target tracking. The sensor node distribution strategy improves the accuracy to identify the targets effectively. Experiments are conducted using simulation of WSN by varying number of nodes, energy levels of the node and target object density using the Network Simulator Tool (NS2) The proposed BLEOSD technique is compared with various recent methods by evaluating accuracy of target tracking, energy consumption rate, localized node density and time for target tracking. The

experimental results shows that the performance of BLESOD is more encouraging compared to contemporary

METHODS

Existing approaches for sensor node localization such as location anonymization algorithm [8], typically focus on aggregating k-anonymous locations with the aim of providing high quality location monitoring system. Say for example, Trajectory-based data forwarding [9] using link delay model has developed a data forwarding scheme to light traffic vehicular ad hoc networks. A passive vehicular traffic measurement to increase sensor time synchronization error has been designed [10] using autonomous passive localization algorithm.
The WSNs are progressively being imagined for productive information gathering, from a geological area of intrigue. In a few observation utilizations of WSNs, target following is one of the principle destinations once the sensor nodes are confined. In the meantime vitality effective target following has been broadly examined in the proceedings. In [11], Channel Adaptive Multiple Input Multiple Output instrument has intended to diminish the intricacy while identifying objects are planned. A trial exertion dependent on Redundant Radix-based Approach [12] has been intended for broadening the system lifetime. In [13], astute portable focus in a versatile sensor arrange has been investigated by showing ideal development strategy. The DOC [Depth of Coverage] approach is used to optimize the detection capability of this class of sensor networks for hundreds of cooperative agents, while minimizing their energy consumption and avoiding collisions with the obstacles. The results show that the DOC [Depth of Coverage] approach significantly has improved the probability of detection compared to other scalable strategies known as uniform, [22], [23] grid, random and stochastic gradient methods.

First, tracking has been studied for minimization of sensor installation cost [14]. Second target tracking has been considered as an anomaly detection problem [15] by applying sequential testing. Third in certain cases, target tracking has been considered as an optimal decision rule problem [16] using Bayesian formulation. Fourth, few efforts has been conducted based on distributed target tracking [18] under ideal binary sensor model. Finally, a few target tracking approaches have used adaptive node selection [19] for reducing the energy cost and computational complexity as well as ensuring the tracking accuracy. A voronoi-based distributed sensor handover protocol [20] has been designed using localized algorithm with the objective of target tracked ratio, i.e., the quality of target tracking service. A quantized measurement fusion framework has been investigated in [21] using probabilistic quantization scheme to reduce the average communication energy saving.
In contrast all the above mentioned approaches, this paper emphasize accuracy of target tracking, energy consumption rate with localized node density. The proposed BLEOSD scheme is robust and efficient target tracking approach. It provides competitive tradeoffs between energy-efficiency, tracking accuracy, and time for target tracking.

RESULTS

Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme is stimulated using NS-2 simulator with the network range of 1400*1400 m size. The number of sensor nodes selected for experimental purpose is 70 nodes and uses Random Way Point (RWM) model for BLEOSD scheme. The BLEOSD scheme uses Destination Sequence Based Distance Vector (DSDV) as routing protocol

Stimulation Environment
The experimental process of Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) states that each node is to track its neighbor node by sending calm request. The request-response process is handled and the receiver node send the response by accepting the request from the sender node using copy-verification-forward process. This function takes place till the sender node completes its data transmission process with the tracking nodes.

CONCLUSIONS

In this paper, a Bayesian Sensor Node Localization algorithm is proposed based on the concept of Bayesian average energy level to satisfy the two different requirements, high accuracy of target tracking and low energy consumption rate, with same WSN simultaneously. The Bayesian Sensor Node Localization algorithm is proved to be stable using the sensor node energy optimality. Moreover, the experiment results on a small test and the simulation results on BLEOSD scheme demonstrate that Bayesian Sensor Node Localization algorithm can significantly improve the target tracking accuracy and decrease the energy consumption rate. BLEOSD scheme also improves tracking accuracy at less time interval because only single hop sensor node distribution strategy is applied, which simplifies the implementation. In further process, dripping of data is done on the sink by applying shortest Path from the sensor nodes simultaneously reduces the data loss and by energy consumption and with high efficiency with tracking accuracy.