Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Abstract

A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality and productivity of manufacturing. Scheduling in such a scenario is challenging due to congested wireless channels, time-dependent energy constraints, complicated device heterogeneity, and prohibitive signaling overheads. In this paper, we first propose an online algorithm, called energy-aware resource scheduling (ERS), to maximize the system utility comprising throughput and fairness, with consideration on both system sustainability and stability. Based on Lyapunov optimization and convex optimization techniques, the proposed algorithm achieves asymptotic optimality for heterogeneous IIoT systems without prior knowledge of network state information (NSI). Subsequently, we extend the ERS algorithm to a more realistic scenario where the overhead and delay of NSI feedbacks are nonnegligible. The optimal scheduling decisions of the scenario are provided, and the optimality loss on system utility under outdated NSI is analyzed. Simulations verify our theoretical claims and demonstrate the gains of our proposed ERS algorithm over alternative benchmark schemes.

Hardware Trojans in Chips: A Survey for Detection and Prevention

“…RF ICs are widely used in many wireless network environments, and some algorithms [ 64 , 65 ] to optimize throughput have promoted the performance of wireless devices, which also provides opportunities for HTs. For example, the forward error correction (FEC) encoder in the wireless routing of the transmitter is implanted with HT.…”

Wireless Powered Mobile Edge Computing for Industrial Internet of Things Systems

“…Therefore, in each small cell, the edge node takes the role to jointly coordinate the time portion allocation for uplink and downlink. For fairness concerns, there are typically three schemes to tackle the doubly near-far problem in WPCN: 1) deploying devices physical location delicately [8]; 2) leveraging device cooperations [18]; and 3) designing fairness embedded system utility functions [20], [29]. Unlike WPCN, which only considers the doubly near-far problem, wireless powered MEC in IIoT also needs to put device heterogeneity and available computing resources in mind during its scheduling mechanism design.…”

“…The parameters used in the simulation are taken from 3GPP specifications [33], [34] and existing synthetic data set [18], [20], to capture the features of practical dynamic environments. Specifically, the channel is modeled after the Rayleigh fading model in [20] with 0.2MHz bandwidth and 10 −9 W receiver noise power. The transmission power of the edge node is 2W and the energy harvesting efficiency of devices is set as 0.8.…”

“…The integration of MEC and WPT has fostered a new paradigm called wireless powered MEC [13], [17]- [20], where MEC server and WPT entity are embedded into one edge node to provide self-sustainable computing. Apart from those challenges facing separate MEC/WPT scenarios, including limited bandwidth, intermittent connectivity, doubly near-far problem, and energy causality, scheduling in wireless powered MEC is also confronted with more tricky multiple resource dependency problems.…”

“…Besides, all these schemes require the full knowledge of up-to-date network states, whereas, in practice, only partial and outdated state information is available due to delay or scalability considerations. Different from previous works, [20] aims to improve the system utility comprising of throughput and fairness by exploiting heterogeneity in IIoT. A practical energy-aware resource allocation algorithm was proposed considering the overhead for state feedback.…”