Adaptive Sensor Scheduling Algorithm for Target Tracking in Wireless Sensor Networks
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摘要: 在无线传感器网络目标跟踪的过程中进行节点调度，可以综合考虑跟踪误差和能量消耗，延长传感器网络的使用寿命。为了综合考虑节点调度的短期和长远损失，该文将问题建模为部分可观测马尔科夫决策过程(POMDP)以得到更优的调度策略，并提出一种近似求解算法C-QMDP。该算法利用马尔科夫链蒙特卡洛方法(MCMC)推导连续状态空间的置信状态的转移，并计算瞬时代价。使用状态离散化方法，基于马尔科夫决策过程(MDP)值迭代求解未来代价的近似值。仿真结果表明，相比现有POMDP近似算法，该文算法既可以降低跟踪过程中的累积损失，又可以将大量运算进行离线计算，减小了在线决策时的计算量。
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关键词:
- 无线传感器网络 /
- 目标跟踪 /
- 节点调度 /
- 部分可观测马尔可夫决策过程
Abstract: In the process of target tracking, the sensor scheduling algorithm can achieve the tradeoff between the tracking error and the energy consumption so as to extend the service life of the sensor network. The issue can be modeled as a Partially Observable Markov Decision Process (POMDP), which takes both short- and long- term losses of sensor scheduling into account and makes a better decision. A C-QMDP approximation algorithm suitable for continuous state space is proposed. The Markov Chain Monte Carlo (MCMC) method is used to derive the transfer function of belief state and calculate the instantaneous cost. The state discretization method is used to solve the approximation of future cost based on Markov Decision Process (MDP) iteration. Simulation results show that compared to the existing POMDP approximation algorithms, the proposed algorithm can reduce the cumulative losses and computation load in the tracking process by offline computation. -
表 1 C-QMDP算法
算法 1 C-QMDP算法
输入：置信状态(包括粒子状态和粒子权重)：
${{{b}}_k} = \left({{S}}_k^{(1)},\tilde w_k^{(1)},{{S}}_k^{(2)},\tilde w_k^{(2)},·\!·\!·,{{S}}_k^{(N)},\tilde w_k^{(N)}\right)$
输出：最优动作A
(1) function C-QMDP( ${{{b}}_k}$)
(2) $V\!\, \raisebox{4pt}{\hat} \leftarrow {\rm{MDP\_discrete\_value\_iteration()}}$
(3) for all control actions A do
(4) ${{{b}}_{k + 1}} \leftarrow {\rm{Particle\_filter(}}{{{b}}_k},{{A}}{\rm{)}}$
(5) for i = 1 : N do
(6) ${\tilde{ S}}_{k + 1}^{(i)} \leftarrow {{S}}_{k + 1}^{(i)}$
(7) end for
(8) $Q({{{b}}_k},{{A}}) = L({{{b}}_k},{{A}}) + \beta \sum\limits_{i = 1}^N {\tilde w_k^{(i)}V\!\, \raisebox{4pt}{\hat}\left({\tilde{ S}}_{k + 1}^{(i)}\right)} $
(9) end for
(10) return $\arg \mathop {\max }\limits_{{A}} Q({{{b}}_k},{{A}})$
(11) end function
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表 2 C-QMDP与其余算法性能比较
算法 跟踪误差 传感器功耗 总代价 全部开启 1.3647 70.0722 97.3662 CPA 2.9132 24.0217 82.2857 CO-Rollout 1.8371 20.3493 57.0913 C-QMDP 1.5542 20.0906 51.1746
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