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There is a tradeoff between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, this paper presents a novel Physaruminspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node can choose the proper next hop by using a proposed Physarum-inspired selecting next hop model (P-iSNH), which comprehensively considers the distance, energy residue, and location of the next hop. As a result, the P-iRP can get a rather low algorithm complexity of (√ ), which greatly reduces the processing delay and saves the energy of sensors. Moreover, by theoretical analysis, the P-iSNH always has an equilibrium solution for multiple next hop candidates, which is vital factor to the stability of routing protocol. Finally, simulation results show that P-iRP can perform better in many scenarios and achieve the effective tradeoff between routing efficiency and energy equilibrium compared to other famous algorithms.

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Computing offloading of mobile devices (MDs) through cloud is a greatly effective way to solve the problem of local resource constraints. However, cloud servers are usually located far away from MDs leading to a long response time. To this end, edge cloud servers (ECSs) provide a shorter response time due to being closer to MDs. In this paper, we propose a computing offloading game for MDs and ECSs. We prove the existence of a Stackelberg equilibrium in the game. In addition, we propose two algorithms, F-SGA and C-SGA, for delay-sensitive and compute-intensive applications, respectively. Moreover, the response time is reduced by F-SGA, which makes decisions quickly. An optimal decision is obtained by C-SGA, which achieves the equilibrium. Both algorithms above proposed can adjust the computing resource and utility of system users according to parameters control in computing offloading. The simulation results show that the game significantly saves the computing resources and response time of both the MD and the ECSs during the computing offloading process.