Keep your nice friends close, but your rich friends closer — Computation offloading using NFC

Sucipto, Kathleen; Chatzopoulos, Dimitris; Kosta, Sokol; Hui, Pan

doi:10.1109/infocom.2017.8057147

Cited by 20 publications

(13 citation statements)

References 12 publications

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“…It is important to examine novel D2D communication technologies as well as to examine the effectiveness of an offloading algorithm through real implementations. The study [151] addressed these drawbacks by proposing the first task offloading framework with near field communication (NFC) based D2D communication and a real implementation evaluation. NFC has several advantages over the longer-range Bluetooth and WiFi technologies due to its short communication range, including lower interference, lower energy consumption, and intrinsic security.…”

Section: Joint Minimization Of Latency and Energy Consumptionmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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Section: Joint Minimization Of Latency and Energy Consumptionmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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“…Several PaaS systems have been proposed, mainly addressing how to achieve Quality of Service (QoS) awareness performance [6], [7], [9]- [11], through appropriate helper selection. On the contrary, we propose the CAP to provide ubiquitous (high service success ratio) and Quality of Experience (QoE) like (e.g., disruption duration and frequency) service, through extensive effort to explore human mobility and access handling run by the helpers themselves.…”

Section: Related Workmentioning

confidence: 99%

“…Up-to-now, the exploration of use cases with different proximal services is ongoing, including cellular traffic offloading [4], opportunistic crowd computing [5], mobile augmented reality [6], mobile crowdsourcing [7], computation offloading [8], [9], target marketing [10] and even block-chain applications [11].…”

Section: Introductionmentioning

confidence: 99%

Proximity as a Service for the Use Case of Access Enhancement Via Cellular Network-Assisted Mobile Device-to-Device

Guo

Schormans

et al. 2020

IEEE Access

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Device-to-Device (D2D) communication is a way to treat the User Equipments (UEs) not as terminals, but as a part of the network (helpers) for service provisioning. We propose a generic framework, namely Proximity as a Service (PaaS), formulate the helper selection problem, and design and prove a heuristic helper selection policy, ContAct based Proximity (CAP), which increases the service connectivity and continuity. Design Of Experiment (DOE) is a statistical methodology that rigorously designs and conducts an experiment, and maximizes the information obtained from that experiment. We apply DOE to explore the relationship (analytic expression) between four inputs (factors) and four metrics (responses). Since different factors have different regression levels, a unified four level full factorial experiment and cubic multiple regression analysis have been carried out. Multiple regression equations are provided to estimate the different contributions and the interactions between factors. Results show that transmission range and user density are dominant and monotonically increasing, but transmission range should be restricted because of interference and energy-efficiency. After obtaining the explicit close form expressions between factors and responses, optimal values of key factors are derived. A methodology (the-constraint method) to solve the multiple-objective optimization problem has been provided and a Pareto-Optimal set of factors has been found through iteration. The fluctuation of the iterations is small and a specific solution can be chosen based on the particular scenarios (city center or countryside with different user density). The methodology of optimization informs the design rules of the operator, helping to find the optimal networking solution.

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“…Most previous works have been done on computing offloading [17][18][19]. The emergence of computing offloading techniques can be traced back to the concept of Cyber Foraging [20], which reduces the computation, storage, and energy of MDs by offloading tasks of MDs to nearby servers with sufficient resources.…”

Section: Related Workmentioning

confidence: 99%

A Computing Offloading Game for Mobile Devices and Edge Cloud Servers

Zhu

et al. 2018

Wireless Communications and Mobile Computing

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Computing offloading of mobile devices (MDs) through cloud is a greatly effective way to solve the problem of local resource constraints. However, cloud servers are usually located far away from MDs leading to a long response time. To this end, edge cloud servers (ECSs) provide a shorter response time due to being closer to MDs. In this paper, we propose a computing offloading game for MDs and ECSs. We prove the existence of a Stackelberg equilibrium in the game. In addition, we propose two algorithms, F-SGA and C-SGA, for delay-sensitive and compute-intensive applications, respectively. Moreover, the response time is reduced by F-SGA, which makes decisions quickly. An optimal decision is obtained by C-SGA, which achieves the equilibrium. Both algorithms above proposed can adjust the computing resource and utility of system users according to parameters control in computing offloading. The simulation results show that the game significantly saves the computing resources and response time of both the MD and the ECSs during the computing offloading process.

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Keep your nice friends close, but your rich friends closer — Computation offloading using NFC

Cited by 20 publications

References 12 publications

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Proximity as a Service for the Use Case of Access Enhancement Via Cellular Network-Assisted Mobile Device-to-Device

A Computing Offloading Game for Mobile Devices and Edge Cloud Servers

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