Aiming at the data gathering optimization problem in energy harvesting Wireless Sensor Network (WSN), a scheme based on dynamic sensor node sampling rate and data compression was proposed, where the spatial-temporal characteristics of energy harvested by individual sensor node was considered. To maximize the total amount of sampling data in the network, first, according to the neighbor information of the nodes, a local compression algorithm was proposed to determine the optimal compression strategy. Considering the data receiving and forwarding energy consumption of the node based on its topological position in the data aggregation tree, its sampling rate was gradually increased until its total energy consumption reached the collection energy consumption threshold. After that, a global optimization problem of network performance was constructed, and a heuristic algorithm was proposed. By iteratively solving linear programming problems, the optimal sampling rate and compression scheme were obtained. The experimental results show that compared with the existing adaptive sensing and compression rate selection scheme, the proposed two data collection optimization algorithms can maintain more stable sensor node battery levels and achieve higher network performance.

Aiming at energy-balanced routing problem in rechargeable Wireless Sensor Network (WSN), a new multi-path routing algorithm and an opportunistic routing algorithm were proposed in the scenario of wireless charging with stable power and monitoring data collection network, so as to achieve the energy balance of the network. Firstly, the relationship model between the charging power and the receiving power of wireless sensor nodes was constructed by the theory of electromagnetic propagation. Then, considering the sending and receiving energy consumptions of wireless sensor nodes in the network, the energy-balanced routing problem was transformed into the max-min optimization lifetime problem of the network nodes. The link traffic obtained by the linear programming was used to guide the data flow allocation in the routing. Finally, considering a more realistic scenario of low power WSN, an energy-balanced routing algorithm based on opportunistic routing was proposed. The experimental results show that, compared with the Shortest Path Routing (SPR) and Expected Duty-Cycled wakeups minimal routing (EDC) algorithms, the proposed two routing algorithms can effectively improve the utilization ratio of the energy collection and the network lifetime in the working period.