Network resource allocation is an important issue for designing energy-harvesting wireless sensor networks (EH-WSNs). This article considers the capacity assignment problem in EH-WSNs with the interference channel for fixed data and energy flow topologies. We focus on the optimal data rates, power allocations, and energy transfers, minimizing the total network delay for the network. We first consider a simplified model where the data flow is fixed on each data link and optimizes transmit power at each sensor node for a single energy harvest in a time slot. However, the optimization problem is nonconvex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high signal-to-interference-plus-noise ratio (SINR), this article aims to directly solve the original nonconvex formulation by employing a powerful evolutionary algorithm, i.e., negatively correlated search (NCS). Then, we investigate the joint optimization problem of capacity and flow for the entire EH-WSNs, and develop a novel multiobjective NCS algorithm (MOEA/D-NCS) to deal with the complicated nonlinear constraints and optimize the data rates, power allocations, and energy transfer simultaneously, so as to minimize the total network delay. The numerical results demonstrate that solving the nonconvex problem with approximated approach is a good alternative for solving the approximated convex problem with accurate optimization approaches; the joint optimization of capacity and flow is a good solution for EH-WSNs; and the scheme of partial transmission for data flow is an advantage in respect of decreasing the network delay. The solution of this article could also be beneficial to other complex optimization problems in the wireless network design.

中文翻译：

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通过负相关搜索实现具有干扰信道的能量收集无线传感器网络的最优能量延迟调度


网络资源分配是设计能量收集无线传感器网络（EH-WSN）的一个重要问题。本文考虑了具有固定数据和能量流拓扑的干扰信道的 EH-WSN 中的容量分配问题。我们专注于最佳数据速率、功率分配和能量传输，最大限度地减少网络的总网络延迟。我们首先考虑一个简化的模型，其中数据流固定在每个数据链路上，并优化每个传感器节点的发射功率，以实现一个时隙内的单个能量收集。然而，优化问题是非凸的，因此很难找到最优解。与大多数传统方法通过考虑相对较高的信号干扰加噪声比（SINR）将原始优化问题近似为凸优化问题不同，本文旨在通过采用强大的进化算法直接求解原始非凸公式算法，即负相关搜索（NCS）。然后，我们研究了整个 EH-WSN 的容量和流量联合优化问题，并开发了一种新颖的多目标 NCS 算法（MOEA/D-NCS）来处理复杂的非线性约束并优化数据速率、功率分配和网络性能。同时进行能量传输，从而最大限度地减少网络总延迟。数值结果表明，用近似方法求解非凸问题是用精确优化方法求解近似凸问题的一个很好的替代方法；容量和流量的联合优化是EH-WSNs的一个很好的解决方案；数据流分部分传输的方案在降低网络延迟方面具有优势。 本文的解决方案也可能有益于无线网络设计中的其他复杂优化问题。