Object Monitoring and Tracking Algorithms in Wireless Sensor Networks
Date Issued
2010
Date
2010
Author(s)
Lee, Cheng-Ta
Abstract
There are two important challenges in WSNs design. One is to construct an efficient WSN for applications to guarantee desired quality of service (QoS). The other challenge is to prolong the lifetime of WSNs. From application viewpoint, the abilities of environment surveillance, object intrusion detection, and object tracking have to support the QoS. Besides, it is difficult to recharge or replace the battery for numerous sensors in the most scenarios. Therefore, how to prolong the lifetime of WSNs also becomes a key issue.
In this dissertation, we focus on the network planning problem to support object monitoring and object tracking services from various perspectives. We develop five algorithms to solve optimization problems based on Lagrangean relaxation method, simulation techniques, and heuristic approaches. In addition, we develop one prediction-based algorithm based on modified Viterbi algorithm to solve object tracking problem. We present each topic briefly as follows:
For boundary monitoring problem, we propose two algorithms, BMAFS and BMAMS, to support boundary monitoring services. The BMAFS is to construct boundary monitoring for grouping capabilities, and it tries to find the maximum k groups of sensors for boundary monitoring of the sensor field to prolong the system lifetime. In the test problems, the experiment results show that the proposed algorithm achieves optimality in the boundary monitoring for grouping capabilities. The BMAMS is to address the problem of boundary node relocation, and it can move previously deployed sensors to cover uncovered check points due to failure of other nodes or battery exhaustion of other nodes. The mechanism can further prolong the system lifetime. The experiment results show that the proposed BMAMS gets effectiveness in the boundary monitoring services for mobile and grouping capabilities.
For in-depth defense problem, we propose two algorithms, LDA and NLDA, to support in-depth defense services. The LDA is to construct layered defense for wireless sensor networks of grouping capabilities. It tries to find the maximum k groups of sensors for layered defense of the monitoring region to prolong the system lifetime. The experiment results show that the proposed LDA gets efficiency in the layered defense for grouping capabilities. The NLDA is to construct non-layered defense of supporting different types of intruders for grouping capabilities, and it tries to find the maximum k groups of sensors for non-layered defense subject to the constraints of defense rate, early warning rate, battery capacity, intruder behaviors, and defender strategies. The NLDA can prolong the system lifetime and provide lead time alarms. The experiment results show that the proposed NLDA gets applicability and effectiveness in the non-layered defense services of supporting different types of intruders for grouping capabilities.
For object tracking problem, we propose two algorithms, TOTA and POTA, to support object tracking services. The TOTA is to construct an object tracking tree for object tracking. Such tree-based algorithm can achieve energy-efficient object tracking for given arbitrary topology of sensor networks. The experiment results show that the proposed TOTA gets a near optimization in the energy-efficient object tracking. Furthermore, the algorithm is efficient and scalable in terms of the running time. The POTA is to construct a dynamic prediction-based algorithm for object tracking. Such the POTA can minimize the number of nodes participating in the tracking activities, minimize out of tracking probability, and maximize the accuracy of object predicted position. The POTA can prolong the system lifetime.
The experiment results show that all six algorithms can support object monitoring and tracking services efficiently.
Subjects
wireless sensor networks
object monitoring
intrusion detection
in-depth defense
object tracking
quality of services
energy-efficiency
system simulation
Lagrangean relaxation
mathematical modeling
network optimization
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