With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance.

由于无线传感器的网络资源和电池能量的局限性，通信过程中的资源竞争将增加网络能耗，降低服务质量（QoS），从而导致多无线电多基站的应用。信道（MRMC）无线传感器网络（WSN）面临许多挑战。在本文中，我们专注于MRMC WSN的联合时隙分配，信道分配和功率控制的资源分配。由于研究目标的多样性和非凸问题的计算复杂性，本文通过分析各种资源的相互依赖关系，提出了一种两阶段的资源分配优化算法。具体来说，为了在所有传感器之间以无冲突的方式交换信息，首先设计了一种用于时隙分配的图形着色算法。然后，在该算法的第一阶段的基础上，研究联合功率控制和信道分配问题，并将其表述为多目标优化问题，以在链路干扰和噪声约束下实现能量效率和网络容量最大化之间的权衡。负载均衡。引入了多目标混合粒子群算法来获得帕累托最优解。仿真结果表明，该算法在实现多性能折衷的基础上具有明显更好的性能。研究联合功率控制和信道分配问题，并将其制定为一个多目标优化问题，以在链路干扰和负载平衡的约束下实现能量效率和网络容量最大化之间的权衡。引入了多目标混合粒子群算法来获得帕累托最优解。仿真结果表明，该算法在实现多性能折衷的基础上具有较好的性能。研究联合功率控制和信道分配问题，并将其制定为一个多目标优化问题，以在链路干扰和负载平衡的约束下实现能量效率和网络容量最大化之间的权衡。引入了多目标混合粒子群算法来获得帕累托最优解。仿真结果表明，该算法在实现多性能折衷的基础上具有明显更好的性能。