In a wireless sensor network (WSN), locations of a few sensor nodes are assumed to be known at the time of deployment (known as anchor nodes) and localization techniques are used to estimate the locations of the other sensor nodes (known as target nodes). We consider a received signal strength (RSS) based localization problem for which the maximum likelihood (ML) formulation is non-convex, non-linear, and discontinuous. We propose a novel technique to convexify the ML problem by constructing a function that underestimates the ML cost function and solve the resulting localization problem using the gradient descent technique. We also derive the Lipschitz constant for gradient of the convexified ML function and convergence rate of the proposed algorithm. Performance evaluation of the proposed method and comparison with state of the art methods, in terms of localization accuracy, execution time, and convergence rate, are presented. Results show superiority of the proposed technique.

中文翻译：

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使用凸松弛的基于信号强度的协作传感器网络定位


在无线传感器网络 (WSN) 中，假设在部署时已知一些传感器节点（称为锚节点）的位置，并使用定位技术来估计其他传感器节点（称为目标节点）的位置）。我们考虑基于接收信号强度 (RSS) 的定位问题，其最大似然 (ML) 公式是非凸、非线性和不连续的。我们提出了一种新颖的技术，通过构建一个低估 ML 成本函数的函数来凸化 ML 问题，并使用梯度下降技术解决由此产生的定位问题。我们还推导了凸化 ML 函数梯度的 Lipschitz 常数和所提出算法的收敛速度。提出了对所提出方法的性能评估，并在定位精度、执行时间和收敛速度方面与最先进的方法进行了比较。结果显示了所提出技术的优越性。