Probabilistic coverage based sensor scheduling for target tracking sensor networks

The target tracking issue has always been the hotspot in wireless sensor network, and with the emergence of new application in multimedia and real-time transmission, new requirements are proposed for transmission performance of target tracking routing; therefore, a software-defined network-based hierarchical adaptive routing algorithm of wireless sensor network is proposed in this article. The algorithm takes into account both network energy and throughput, uses Hopfield neural network algorithm to calculate the optimal path among adjacent clusters as a local routing (LR), and builds the Multi-choice Knapsack Problem model based on local paths to realize end-to-end global routing, in order to realize the routing of tracking target information under multi-objective conditions. The test bed includes physical and simulation tests. Experimental results show that the proposed algorithm is superior to low energy adaptive clustering hierarchy (LEACH) and Sequential Assignment Routing under different test scenarios.

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“…Put the particle into the state transition function to obtain the new estimated state. As shown in formula (5)…”

Section: Target Tracking Modelmentioning

confidence: 99%

A hierarchical adaptive routing algorithm of wireless sensor network based on software-defined network

Zhao

Wang

Guo

2018

International Journal of Distributed Sensor Networks

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“…Currently main prediction methods for target tracking includes particle filter, Kalman filtering etc [5,8] , but these methods require a large number of iterations and high processing power from each node. Although these techniques are more accurate, they are not suitable for video sensor nodes which have limited resources and low calculate ability.…”

Section: Non-uniform Motion Target Trajectory Prediction Modelmentioning

confidence: 99%

K-coverage prediction optimization for non-uniform motion objects in wireless video sensor networks

Jiang¹,

Sheng²,

Wang³

et al. 2016

Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

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Abstract. The existing wireless video sensor networks tracking algorithms have much higher performance requirements of the sensor node in networks. For solving this problem, we propose a new target trajectory prediction model for non-uniform motion, which takes all the possible locations of each target at next moment into consideration. Furthermore, we design a K-coverage dynamic optimization algorithm to gain benefit of distributed method based on the aforementioned mathematical model. The experimental results indicate that the proposed algorithm outperforms the existing algorithms.

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“…Hence, it is imperative to efficiently manage the sensors’ resources to prolong the lifetime of tracking networks without sacrificing performance. Much research effort has been dedicated to resolve the issue from different perspectives, for example, energy-efficient tracking scheme [ 7 , 8 , 9 , 10 ] and energy-balanced tracking scheme [ 11 , 12 , 13 ]. However, as long as the sensor nodes are static, this issue cannot be fully tackled.…”

Section: Introductionmentioning

confidence: 99%

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

Qian

Liu

2018

Sensors

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Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.

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Probabilistic coverage based sensor scheduling for target tracking sensor networks

Cited by 42 publications

References 30 publications

A hierarchical adaptive routing algorithm of wireless sensor network based on software-defined network

A hierarchical adaptive routing algorithm of wireless sensor network based on software-defined network

K-coverage prediction optimization for non-uniform motion objects in wireless video sensor networks

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

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