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There are problems of interference between Devices-to-Devices (D2D) communication users and cellular users, mutu¬al interference between D2D communication users, and environmental interference caused by the existence of environmental obstacles in the D2D communication system assisted by Reconfigurable Intelligent Surface (RIS). In order to solve the problems, an optimization algorithm aiming at maximizing the total rate of the system is proposed to maintain the high quality of services among cellular users and D2D communication. Simulation experiments show that the total system speed under the tabu search- successive convex approximation algorithm is improved by 56% compared with the phase shift optimization algorithm. The to¬tal system speed under the tabu search-successive convex approximation algorithm is improved by 21. 2% compared with the power allocation algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the sensor network lifetime problem (SNLP), a sensor network coverage model and its data structure are proposed, and the problem is equivalent to its dual problem, namely minimum weight sensor coverage problem. Firstly, SNLP is constructed as a package linear programming. After finding different sensor coverage satisfying sensor network constraints, the sensor network life is maximized by allocating time for each sensor coverage. Secondly, for solving SNLP, three centralized solving methods are proposed, which are based on Garg- Konemann algorithm, greedy algorithm considering partial sensor coverage and constant approximation algorithm considering communication cost. At the same time, a distributed solving method based on global reshuffle is proposed. The reshuffle is triggered when the initial energy supply of a sensor drops to a certain predefined threshold value H among active, idle and intermediate vulnerable states, thus the sensor network lifetime is improved by using smart self- organizing monitoring schedules. Simulation results show that the proposed SNLP, which is based on sensor network coverage model and data structure, and its solving method can achieve preferable running time, network life and network overhead. [ABSTRACT FROM AUTHOR]