Mobile Crowd Sensing Service Platform for Social Security Incidents in Edge Computing

Social security involving mass incidents has become more and more important. In this paper, we design and develop a mobile crowd sensing service platform for social security incidents in edge computing. Using this platform, information of the emergent social security incidents from different sources and in different types(e.g., videos, audios, pictures and text) can be gathered in the edge cloud server. The police can check the information on the edge cloud server and respond to the incidents in time. To minimize the responding time, we design and develop a task allocation algorithm based on ant colony optimization to allocate the tasks to appropriate policemen efficiently. We take simulations on Matlab. It is observed that the performance of our proposed algorithm is 12% better than the one based on improved generic algorithm in running time.

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