In order to improve the effectiveness of mobile cloud computing in Device-to-Device (D2D) communication network, a computation offloading scheme based on the time switch policy for energy harvesting was proposed. Firstly, the computational tasks needed to be migrated of a traffic-limited smart mobile terminal were sent to an energy-limited smart mobile terminal in the form of Radio-Frequency (RF) signals through D2D communication, and the time switch policy was used by the energy-limited smart mobile terminal for the energy harvesting of received signals. Then, the extra traffic consumption was paid by the energy-limited terminal for the relay tasks of traffic-limited terminal to the cloud server. Finally, the proposed scheme was modeled as a non-convex optimization problem for minimizing terminal energy and traffic consumption, and the optimal scheme was obtained by optimizing the time switch factor and the harvest energy allocation factor of the energy-limited terminal, and the transmission power of the traffic-limited terminal. The simulation results show that, compared with non-cooperative scheme, the proposed scheme can effectively reduce the terminal's limited resource overhead by the computation offloading through reciprocal cooperation.

Aiming at the problem that the network lifetime was reduced because of the high energy consumption of the nodes covered by multiple radiation sources in large scale Wireless Sensor Network (WSN), a new clustering algorithm based on Evolutionary Game Theory (EGT) was proposed. The non-cooperative game theory model was established by mapping the search space of the optimal node sets to the strategy space of the game and using the utility function of the game as objective function respectively; then the optimization objective was achieved by using Nash equilibrium analysis and the perturb-recover process of equilibrium states. Furthermore, a detailed clustering algorithm was presented to group the optimal node sets into clusters for further location. The proposed algorithm was compared with the nearest-neighbor algorithm and the clustering algorithm based on Discrete Particle Swarm Optimization (DPSO) algorithm in the location accuracy and the network lifetime under the RSSI (Received Signal Strength Indication)/TDOA (Time Difference of Arrival) two rounds cooperative location scheme. Simulation results show that the proposed algorithm decreases the energy consumption of the nodes covered by multiple radiation sources, prolongs the network lifetime and guarantees the precise location.

Distributed Hash Table (DHT) network is widely used in secure self-destruction schemes of privacy data in cloud computing environment, but malicious nodes and dishonest nodes in the DHT network easily lead to key shares loss or leakage. To tackle those problems, a trust evaluation mechanism was proposed for the DHT network used in cloud-data secure self-destruction system. In this mechanism, a trust cloud model was established for DHT nodes to describe their trust information qualitatively and quantitatively. By introducing an improved calculation method of direct trust value together with recommended trust value and fully considering the internal and external factors of DHT nodes, the trust value of nodes were first calculated on two dimensions consisted of operating experiment and interactive experience. The result data were used to build trust evaluation sub-cloud for each index. After that, all these trust evaluation sub-clouds were summed up to generate the comprehensive trust cloud according to the weights of different evaluation indexes. Then, the comprehensive trust cloud, by means of cloud generator algorithm, could be described as one-dimensional normal cloud. Finally, the reliable and efficient nodes could be selected using trust decision algorithm. Experimental results show that the proposed mechanism can help original data self-destruction system making comprehensive trust decision and finding reliable DHT network nodes, further enhancing disaster-tolerant capability and reducing computational cost of the system.