Power Minimization in Wireless Powered Fog Computing Networks with Binary Offloading
This paper investigates the power minimization design for a multi-user wireless powered fog computing (FC) network, where a hybrid access point (HAP) (integrated with a fog server) charges the multiple energy-limited wireless sensor devices (WSDs) via wir
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School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China [email protected] Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China 3 School of Information, Beijing Wuzi University, Beijing 101149, China 4 State Grid Energy Research Institute Co., Ltd., Beijing 102209, China 5 ZTE Corporation, Xi’an 710065, China
Abstract. This paper investigates the power minimization design for a multi-user wireless powered fog computing (FC) network, where a hybrid access point (HAP) (integrated with a fog server) charges the multiple energy-limited wireless sensor devices (WSDs) via wireless power transfer (WPT). With the harvested energy, each WSD accomplishes its computation task by itself or by the fog server with a binary offloading mode. A power minimization problem is formulated by jointly optimizing the time assignment (for WPT and tasks offloading) and the WSDs’ computation mode selection (local computing or FC) under constraints of energy causality and computation rate requirement. Due to the integer and coupling variables, the considered problem is non-convex and difficult to solve. With successive convex approximate (SCA) method, a threshold-based algorithm is designed in terms of the WSDs’ channel gains. Simulation results show that the proposed algorithm is able to achieve the same performance of the enumeration-based algorithm with very low computational complexity. Moreover, it is observed that the channel gains have a great impact on computation mode selection. Specifically, the WSDs with good channel gains prefer local computing while the WSDs with poor channel gains prefer FC, which is much different from the existing sum computation rate maximization designs. Keywords: Binary offloading · Fog computing · Power minimization Successive convex approximate · Wireless power transfer
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This work was supported in part by ZTE Corporation, in part by the Self-developed project of State Grid Energy Research Institute Co., Ltd. (Ubiquitous Power Internet of Things Edge Computing Performance Analysis and Simulation Based on Typical Scenarios, No. SGNY202009014) and also in part by the Beijing Intelligent Logistics System Collaborative Innovation Center (No. BILSCIC-2019KF-07). c Springer Nature Singapore Pte Ltd. 2020 Z. Hao et al. (Eds.): CWSN 2020, CCIS 1321, pp. 126–139, 2020. https://doi.org/10.1007/978-981-33-4214-9_10
Power Minimization
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Introduction
With the development of Internet of Things (IoTs), more and more latencyintensive and computation-intensive applications are appearing, which bring huge computing burden to the wireless sensor devices (WSDs) [1,2]. To improve the computation capabilities of WSDs in IoTs, fog computing (FC) (or mobile edge computing (MEC)) has been regarded as one of the most promising technologies [3,4]. With FC, WSDs are allowed to offload part or all of the computation tasks to their surrounding fog servers acted by access points (APs), base stations (BSs) and personal computers (PCs)
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