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Solar harvesting, as the most mature environmental energy harvesting technology, can provide a convenient, low-carbon, and off-grid solution for energy-constrained WSN in remote environmental monitoring. However, the stochastic and intermittent nature of solar energy seriously affects network energy efficiency. In this paper, we jointly consider solar harvest deployment and scheduling for clustered WSN collaborating with SWIPT, where the solar-powered cluster (SC) coordinates SWIPT to periodically broadcast energy to a set of wireless charging nodes in full-duplex mode, each member node exhausts the recharged energy for data sensing and forwarding. To maximize network energy efficiency, we formulate a Solar harvesting collaborative SWIPT Energy Scheduling(SS-CoES) optimization problem, which jointly optimizes the deployment of SCs, the energy broadcast power with time allocation, and data sensing size under the constraint of the self-sustaining demand for network energy. Given the combinational nature of the energy deployment with its strong coupling with energy scheduling, we propose a multi-layer iteration decoupling optimization algorithm based on the Dinkelbach method, which decouples the problem into three subproblems and proposes the Hybrid discrete Firefly algorithm, Lagrangian dual subgradient algorithm, and bisection search method to obtain near-optimal feasible solutions. Simulation results show that the proposed SS-CoES can effectively maximize network energy efficiency and eliminate the dependence on the grid and move chargers, achieving rechargeable wireless sensor networks (RWSN) energy self-sustainability in remote areas. [ABSTRACT FROM AUTHOR]

In order to enhance the sustainability of rechargeable wireless sensor networks (RWSN), a deep-q-networks (DQN)-based adaptive dual-mode energy-efficient routing is proposed in this paper. Firstly, the life expectancy of each node is calculated based on multiple related factors, and a multi-hop routing based on forward transmission principle is proposed by using the indicator. Then according to the relationship between the life expectancy of a single node and the average life expectancy of the whole network, an adaptive dual-mode energy-efficient routing is proposed, which combines the multi-hop routing and the direct upload routing. Finally, for reducing the requirement of the single node for the network state information in the process of routing mode selection, a reinforcement learning framework based on DQN is designed, enabling the nodes to learn to judge the above relationship of life expectancy based on partial state information of its local network. Simulation results show that dynamic adjustment of the routing mode enables our algorithm to effectively optimize energy efficiency, so that the network lifetime increases obviously. Based on the limited information, the correct rate of routing mode selection can reach 95%, which ensures the applicability of our algorithm. [ABSTRACT FROM AUTHOR]

The rechargeable wireless sensor networks provide efficient data services for many applications in the Internet of Things (IoT), in which the sensors can be recharged continuously. Wireless recharging technology has been widely studied and applied. Reasonable energy allocation and identification of the nodes which need recharging in each round is key to the technology. According to dynamic recharging planning, it is crucial to improve energy efficiency in networks with excellent Quality of Service (QoS). Hence, a lifespan-balance-based energy-efficient routing is proposed. The optimization problem of maximizing recharging payoff is first designed and it is solved by our heuristic algorithm, which enables dynamic adjustment of the ratio of moving time to recharging time according to energy states of nodes. Then the QoS routing based on multi-hop strategy which integrates multiple related factors is designed. Finally, the indicator which contains energy supplement to quantify lifespan is designed. Combined with our QoS routing, the method of lifespan balance based on the global relay range regulation is proposed to maximize the minimum lifespan of all nodes intelligently. The simulation result shows that our algorithm can improve the energy efficiency with excellent QoS in the studied networks and prolong the network lifetime. [ABSTRACT FROM AUTHOR]