Energy-Efficient and Security-Aware Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Recently, edge-cloud computing (ECC) has emerged as a new paradigm for alleviating the intensive overhead for mobile IoT applications. However, data security remains a significant concern that has not been adequately addressed. Moreover, the diversity of mobile devices leads to overloaded edge servers and thereby perpetually increasing the latency and limiting the gain of performance. Therefore, this paper proposes a new security, load balancing, and energy-aware task offloading framework for the ECC system environment to bypass potential security threats and the edge servers' balancing challenge. Specifically, a new layer of security based on an advanced encryption standard (AES) cryptographic method and fingerprint combination is introduced in order to protect the data from vulnerabilities during offloading. Moreover, to organize the load on edge servers, a new load-balancing algorithm is being developed. Subsequently, task offloading, data security, and load balancing are jointly formulated as an integer problem whose objective is to reduce the system's energy with latency constraints. Finally, extensive simulation results demonstrated that our model is scalable and can save about 19%, 17.5%, 20.3%, 14.4%, and 13% of system energy with respect to other benchmark solutions.