Facing to jammers and eavesdroppers, conventional security data transmission is generally based on the cryptographic method. Enormous issues arise when the cryptographic method applied in dynamic wireless scenarios, such as key distribution for symmetric cryptosystems, and high computing complexity of asymmetric cryptosystems. With the rapid growing of wireless traffic and massive device accessing of Internet-of-Things (IoT), the computing complexity and energy consumption aggravate, which leads to security degrade of wireless networks. To address this issue, a secure communication scheme based on physical layer security was proposed for Full-Duplex (FD) wireless-powered IoT networks, which limited the amount of information received at the unauthorized receiver by exploiting the randomness of noise and wireless channel. In this method, secrecy capacity was analyzed based on information theory, and then the Secrecy Outage Probability (SOP) was derived with the analysis model of secrecy capacity. In addition, considering the influence of noise, jammer interference, spatial mutual interference, and residual self-interference on safety capacity, a secure beamforming method was proposed to increase the mutual information between the transmitting and receiving ends and improve the secrecy capacity of the full-duplex wireless-powered IoT network by decreasing the joint interference. Derivation results are verified through Monte Carlo simulation. Simulation results show that the FD wireless-powered IoT with secure beamforming is superior to the conventional wireless-powered IoT in terms of secrecy capacity and SOP.

In view of the friend recommendation in Mobile Social Network (MSN), a new method based on multi-dimensional similarity was proposed. The method is a kind of method based on content, but not confined to single dimension matching information, it judges users' similarity of various dimensions from three aspects of space, time and interest, then gets a comprehensive judgment by "difference distance". The proposed method can recommend other users to target audience when they are consistent in geographical position, online-time and interest. The experimental results show that when the method is used in the friend recommendation of mobile social networks, its precision and efficiency are up to 80% and 60% respectively, the performance is much better than the other friend recommendation methods based on single dimension; at the same time, by adjusting the value of three dimensional weights, the method can be used in a variety of mobile social networks with different characteristics.

Due to the deficiency of ubiquitous charging stations (or stakes) and short driving distances of Electric Vehicle (EV), many people are hesitant to use EV. To reduce users' anxiety about limited battery capacity and lower fees due to frequent charging and making detour to charge, a matching theoretic Traveling Plan-aware Charging Scheduling (TPCS) scheme was proposed. Firstly, preference lists of EV users and charging stations were constructed respectively according to traveling plans of EV and their electricity demand at each charging station. Secondly, a many-to-one matching model was established between EV users and charging stations. Finally, interfaces of charging stations were allocated to optimize the system total utility. Compared with the Random Charging Scheduling (RCS) algorithm and Only utility of Electric Vehicle concerned Scheduling (OEVS) algorithm, the system total utility of TPCS was increased at most by 39.3% and 5% respectively. In addition, TPCS guaranteed the satisfactory ratio of EV users to be above 90% when charging demand of EV users was light, which is higher than that of RCS. The proposed algorithm can effectively improve the system total utility and satisfactory ratio of EV users, and reduce the computational complexity.

In order to improve the energy efficiency of the heterogeneous wireless networks with macro-cells and micro-cells, a Joint Switch scheduling and Resource Allocation (JSRA) algorithm was proposed. Firstly, based on sleeping of micro base stations, Centralized User Switch scheduling Algorithm (CUSA) was adopted to determine the associated base station for each user. The sleeping/waking status of a micro base station was judged according to whether to decrease of network power consumption when users of the micro base station entirely switched to macro base station.Then, the Best Channel quality Subcarrier Adjustment (BCSA) algorithm was used to assign subcarriers and transmission power for users. The network energy efficiency was guaranteed to approximate the optimal solution by adjusting the subcarrier allocation between the user with the maximum energy efficiency and the user with the minimum energy efficiency. The theoretical analysis and simulation experiments show that,compared with three existing algorithms which considered user handoff or resource allocation separately, JSRA has high computational complexity; however, when the number of users is 120, JSRA can reduce network power consumption 44.4% at most, increase the total effective data rate of users by 80% with the slight reduction only than one contrast as well as the energy efficiency of the network by 200% at most. Experimental results show JSRA can improve the energy efficiency of heterogeneous wireless networks effectively.