Device-to-Device Task Offloading in a Stochastic Invalid-Device Scenario with Social Awareness

Abstract

Direct communication with D2D (device-to-device) between resource devices can reduce the communication burden, and D2D resource devices closer to users have high computing power. Therefore, offloading tasks to D2D devices can calculate tasks faster and reduce delays to improve the user experience. Firstly, since D2D devices are usually held by users and there are certain social attributes between users, we consider the impact of social attributes on task offloading and resource allocation in the real offloading system and allocate the responsive computing resources according to the social attributes. Secondly, when D2D devices are vulnerable to attack, damage, and other uncertain factors, it will affect the strategy of task offloading. We introduce the offloading mechanism under the invalid scenario of random invalid probability to convert the uncertain offloading scenario into the offloading situation of multiple deterministic scenarios, so as to enhance the robustness of the whole offloading system. Finally, considering the conditions of social awareness, resource allocation, invalid scenario, and energy constraints, we express it as a nonlinear integer programming problem with a minimum expected time. We use the MLS(maximum-likelihood sampling) algorithm to estimate the sample space of the invalid scenarios and the meta heuristic Discrete Whale Optimization Algorithm (DWOA) to solve the optimization problem to obtain the offloading scheme and resource allocation strategy.