A New Offloading Technique to Maximize Multi-User Performance in Edge Computing Using Blockchain Technology in the Internet of Things

We face more and more difficulties as blockchain technology combines with Mobile Edge Computing (MEC) and the Internet of Things (IoT), including excessive energy consumption and latency because of the size and complexity of the network. With data security as a top priority, it is imperative to optimize these exchanges and manage computational job offloading properly. MEC enabled by unmanned aerial vehicles (UAVs) is one of the most promising methods that ensures the stability and expansion of the network coverage zone for such applications and provides them with access to computing capabilities. This work investigates the simultaneous exploration of resource distribution and computation offloading for mobile edge-cloud computing structures that accommodate several users and unmanned aerial vehicles. First, for a mobile edge-cloud computing platform with numerous users and UAVs, we propose a paradigm for efficient resource allocation and compute offloading. The purpose of this work is to examine how blockchain and edge computing are incorporated into IoT systems. To the best of our knowledge, this is the initial assessment piece that covers every aspect of blockchain-based edge implementation categories and system architectures. We present a promising scenario for the energy use balance administration issue of multi-mobile devices and multitasking, based on an analytical method based on blockchain technology. Analysis of the associated approximate ratio is done. Numerous simulation experiments have been conducted in comparison to the random algorithm and the overall energy demand optimization approach. When compared to the randomized process, the experiment results indicate that the suggested greedy algorithm may improve average efficiency by 69.59 percentages in terms of energy consumption. Additionally, under the traditional task topology, the greedy algorithm almost always obtains the optimum solution when the mobile device's minimum transmission power is between five and six dBm.