Abstract
Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.
Highlights
Due to the advancements in cellular technologies and applications of Internet of things (IoT), our daily life activities, such as smart healthcare, smart homes, and smart driving, are becoming dependent on mobile terminals (MTs) [1, 2]. e aforementioned applications are computation-intensive and energy-hungry and require large storage and faster execution [3]
For the improvement of MT’s battery life and ensured connectivity with hybrid access point (HAP) during partial offloading technique, the time slot for data offloading is further divided into two parts: (a) the time fraction in which the MT will charge its battery through radio frequency (RF) signal and (b) the time fraction in which the MT will offload data to the HAP. is division of time slot, referred to as time allocation policy, is discussed in [15, 16]. e problem of time allocation policy and offloading mode selection is studied in [12], and a total computational offloading technique for all energy harvesting MTs is proposed
The battery life of MT, time allocation policy for energy harvesting and data offloading, and offloading policy based on optimal number of subtasks are investigated for single MT in WP-mobile edge computing (MEC) system. e application for computational offloading is divided into optimal number of subtasks and through offloading policy, the decision to follow either remote execution or local execution is made
Summary
Due to the advancements in cellular technologies and applications of Internet of things (IoT), our daily life activities, such as smart healthcare, smart homes, and smart driving, are becoming dependent on mobile terminals (MTs) [1, 2]. e aforementioned applications are computation-intensive and energy-hungry and require large storage and faster execution [3]. Both the battery life and computation power can be improved through the optimal selection of number of subtasks per application and time allocation policy for energy harvesting and data offloading. (i) We find the time allocation policy for energy harvesting and data offloading, partial offloading policy, and optimal number of subtasks per application, simultaneously, through deep learning approach in WP-MEC system to improve the battery life of MT and execution delay.
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