Abstract
In recent years, service isolation and service miniaturization have become very popular. The large services are dismantled into multiple low-cost and simple small services to improve the scalability and disaster tolerance of the entire services. A service network composed of unmanned aerial vehicles (UAVs) and MEC servers is proposed in this paper, which aims at decoupling multiple services of the SWIPT-MEC network. In this network, UAVs take charge of energy transmission and calculation task scheduling and MEC servers are focused on task calculation. To meet the resource requirements of the ground nodes (GNs) in the network, we designed a distributed iterative algorithm to solve the resource allocation decision problem of GNs and used the modified expert bat algorithm to complete the UAV’s trajectory planning in a two-dimensional space. The results show that the algorithm can formulate a more fair resource allocation strategy, and its performance is improved by 7% compared with the traditional bat algorithm. In addition, the algorithm in this paper can also adapt to changes in task length and has a certain degree of stability.
Highlights
In the past few decades, the Internet of things (IoT) technology has attracted widespread attention from the academic and commercial circles [1, 2]
mobile edge computing (MEC) uses the orthogonal frequency-division multiple access (OFDMA) technology to allocate the spectrum to the unmanned aerial vehicles (UAVs), allowing it to communicate with ground nodes (GNs) in the time-division multiple access (TDMA) mode through this spectrum (ii) Since user information is frequently changed, if the networks apply a centralized algorithm, it will inevitably spend a lot of time to update the data
This paper proposes a simultaneous wireless information and power transfer- (SWIPT-)based MEC network composed of multiple UAVs and one MEC server
Summary
In the past few decades, the Internet of things (IoT) technology has attracted widespread attention from the academic and commercial circles [1, 2]. It is worth mentioning that the communication technology in IoT is one of the current research hotspots [3]. Devices in IoT have low computing ability and energy storage. Most of them cannot perform high-intensity calculations without external auxiliary equipment [4]. These devices do not have enough energy to undertake the massive data interaction requirements in the intelligent age [5]. Cisco estimates that in 2023, at least 8.7 billion communication devices are connected to the 5G networks [6]. To support all devices to enjoy real-time and high-throughput services, researchers and engineers proposed a simultaneous wireless information and power transfer- (SWIPT-) based mobile edge computing (MEC) networks
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