Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme resist attack without considering channel state information. scheme, MEC sensor is chos...

With the development of intelligent transportation and rapid growth application data, tasks offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve service efficiency computing platform, energy-efficient low-latency mobile-edge-computing (MEC) methods urgently needed, which can solve insufficient capacity vehicle terminals. Based on an ...

Smart grid has integrated an increasing number of distributed energy resources to improve efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid (e.g., households) equipped with distributed energy resources can be considered as “microgrids” that both generate and consume electricity. To facilitate energy...

Abstract Hydrogen has a great potential as an alternative energy and produces zero emissions, but most of hydrogen is produced from non-renewable fossil fuels via reforming. Thus, biomass promising replacement to where can be sourced from. In this project, sago waste chosen raw material in microbial electrolysis cell (MEC) produce fuel. A mathematical model with the integration MEC photo-fermen...

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices nearby mobile servers, thereby lowering energy consumption and response time for ground users or IoT devices. Integration Unmanned Aerial Vehicles (UAVs) the computing (MEC) server will significantly benefit small, battery-powered, energy-constrained in 5G future wireless netw...

In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) modified k-mean clustering (MKM) method MEC. We utilised CNN determ...

Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2colbased convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-...