A time-efficient data offloading method with privacy preservation for intelligent sensors in edge computing

Over the past years, with the development of hardware and software, the intelligent sensors, which are deployed in the wearable devices, smart phones, and etc., are leveraged to collect the data around us. The data collected by the sensors is analyzed, and the corresponding measures will be implemented. However, due to the limited computing resources of the sensors, the overload resource usage may occur. In order to satisfy the requirements for strong computing power, edge computing, which emerges as a novel paradigm, provides computing resources at the edge of networks. In edge computing, the computing tasks could be offloaded from the sensors to the other sensors for processing. Despite the advantages of edge computing, during the offloading process of computing tasks between sensors, private data, including identity information and address, may be leaked, which threatens personal security. Hence, it is important to avoid privacy leakage in edge computing. In addition, the time consumption of offloading computing tasks affects the using experience of customers, and low time consumption makes contributions to the development of applications which are strict with time. To satisfy the above requirements, a time-efficient offloading method (TEO) with privacy preservation for intelligent sensors in edge computing is proposed. Technically, the time consumption and the offloading of privacy data are analyzed in a formalized way. Then, an improved of Strength Pareto Evolutionary Algorithm (SPEA2) is leveraged to optimize the average time consumption and average privacy entropy jointly. At last, abundant experimental evaluations are conducted to verify efficiency and reliability of our method.