Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

Lyu, Xinchen; Ni, Wei; Tian, Hui; Liu, Ren Ping; Wang, Xin; Giannakis, Georgios B.; Paulraj, A.

doi:10.1109/jsac.2017.2760186

Cited by 242 publications

(109 citation statements)

References 25 publications

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“…Offline algorithm for the joint optimization would require complete non-causal information of networks and may suffer from the curse of dimensionality when the system is in large scale. To address these issues, a perturbed Lyapunov technique was employed to decouple the spatial-and time-dependency of multiple resources in [13] and [14]. However, these analyses may not be suitable for IIoT, since their energy harvesting processes were modeled as stochastic energy packet arrivals.…”

Section: A Related Workmentioning

confidence: 99%

“…It is generally impractical due to systems' stochasticity and unpredictability, partial feedback, and non-negligible transmission delay. In [13] and [25], new analytic frameworks were proposed and applied to accommodate outdated NSI, which were able to diminish the optimality loss asymptotically. Chenshan et al in [26] later extended those frameworks to the scenario of Internet of Things with finite device buffers.…”

Section: A Related Workmentioning

confidence: 99%

“…It is further assumed that all channels are independent and identically distributed (i.i.d.) flat block fading, i.e., channels remain static within each time slot but may vary across different slots [13], [14], [28]. By exploiting the channel reciprocity in time-division mode system [15] [21], the AP can acquire both the uplink and downlink channel states at the beginning of each time slot with little signaling cost.…”

Section: A System Overviewmentioning

confidence: 99%

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

Lyu

Tian

2019

IEEE Internet Things J.

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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.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A System Overviewmentioning

confidence: 99%

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

Lyu

Tian

2019

IEEE Internet Things J.

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“…By integrating the network function virtualization (NFV) techniques, MEC provides flexibility on the resource scheduling and service deployment [201], [202]. Initial investigations on EH powered MEC systems have been conducted in [203]- [211], focusing on EHbased MEC servers [203], [204] and EH mobile users [205]- [210]. In [203], [210], the system operator learns online the task amounts to be offloaded from the MEC server to the central cloud and the CPU frequency of the MEC server, based on the states of core network congestion and RES.…”

Section: A Mobile Edge Computing (Mec)mentioning

confidence: 99%

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

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110

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The advancements in smart power grid and the advocation of "green communications" have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.Index Terms-5G/B5G communication networks, energy har-). The first two authors contributed equally to this work. vesting, smart grid, energy redistribution and trading, optimization techniques. arXiv:1912.13203v1 [eess.SY] 31 Dec 2019 EH and smart gridpowered wireless networks Types and models for RES (Sec. II) Smart grid-powered wireless networks (Sec. VIII) Wireless power and information transfer (Sec. VII) 5G/B5G applications of EH and smart gridpowered wireless networks (Sec. XI) Energy redistribution Energy planning under dynamic pricing (Sec. IX) Two-way energy trading and cooperation (Sec. X) EH-based wireless networks Point-to-point link (Sec. III) Multipoint-to-point system (Sec. IV) Multipoint-to-multipoint system (Sec. V) Multi-hop link (Sec. VI) Lessons learnt and future directions (Sec. XII) Fig. 1. The main structure of this article. from the aspects of network deployment, energy/spectrum efficiency, and delay/bandwidth versus power. Featuring energyharvesting wireless networks, beamforming techniques are reviewed in [22]. Energy scheduling, optimization, and application are presented in [23]. Circuit design, hardware implementation, EH techniques, and communication protocols are reviewed in [24]-[27] with specific emphases on radio frequen...

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“…Fog computing, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of local computation, storage, and communication [18][19][20]. We use a fog server at the BS to concentrate data, data processing, and applications.…”

Section: Introductionmentioning

confidence: 99%

Fog Computing‐Assisted Energy‐Efficient Resource Allocation for High‐Mobility MIMO‐OFDMA Networks

Wang

et al. 2018

Wireless Communications and Mobile Computing

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This paper presents a suboptimal approach for resource allocation of massive MIMO-OFDMA systems for high-speed train (HST) applications. An optimization problem is formulated to alleviate the severe Doppler effect and maximize the energy efficiency (EE) of the system. We propose to decouple the problem between the allocations of antennas, subcarriers, and transmit powers and solve the problem by carrying out the allocations separately and iteratively in an alternating manner. Fast convergence can be achieved for the proposed approach within only several iterations. Simulation results show that the proposed algorithm is superior to existing techniques in terms of system EE and throughput in different system configurations of HST applications.

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Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

Cited by 242 publications

References 25 publications

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

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

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Fog Computing‐Assisted Energy‐Efficient Resource Allocation for High‐Mobility MIMO‐OFDMA Networks

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