EEDOS: an energy-efficient and delay-aware offloading scheme based on device to device collaboration in mobile edge computing
TL;DR: This work proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC that outperforms existing studies in term of energy efficiency with an improved delay in task execution and is capable of performing more successful task offloading and requires less edge server resources.
Abstract: Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.
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TL;DR: This article comprehensively surveys the topic area of device-enhanced MEC mechanisms, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices.
Abstract: 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.
151Â citations
Cites methods from "EEDOS: an energy-efficient and dela..."
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TL;DR: The experimental results show that the joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks can effectively reduce the time delay and energy consumption of terminal tasks, which improves the efficiency of task processing and the experience quality of end users.
30Â citations
TL;DR: The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.
Abstract: Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.
11Â citations
TL;DR: The assessment and comparison of these Green IoT solutions are carried out based on their characteristics, technology used, outcomes, usability, and their limitations, and an organized assessment of existing Green IoT techniques is offered.
8Â citations
5Â citations
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TL;DR: In this article, the authors present a cloud centric vision for worldwide implementation of Internet of Things (IoT) and present a Cloud implementation using Aneka, which is based on interaction of private and public Clouds, and conclude their IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
9,593Â citations
17 Aug 2012
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Abstract: Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
4,440Â citations
01 Jan 1965-Journal of Research of the National Bureau of Standards Section B Mathematics and Mathematical Physics
TL;DR: The emphasis in this paper is on relating the matching problem to the theory of continuous linear programming, and the algorithm described does not involve any "blind-alley programming" -which, essentially, amounts to testing a great many combinations.
Abstract: An algorithm is described for optimally pamng a finit e set of objects. That is, given a real numerical weight for each unordered pair of objects in a se t Y, to selec t a family of mutually di sjoint pairs th e sum of whose wei ghts is maximum . The well-known optimum assignment proble m [5)2 is the sp ecial case where Y partitions into two se ts A and B suc h that pairs contained in A and pairs contain ed in Bare not positively weighted and therefo re are superfluous to the problem. For this \"bipartite\" case the algorithm becomes a variant of the Hungarian method [3]. The problem is treated in terms of a graph G whose nodes (vertices) are the objects Y and whose edges are pairs of objects, including at leas t all of th e positively weighted pairs. A matching in G is a subse t of its edges such that no two mee t the same node in in G. The proble m is to find a maximum-weight-sum matching in C. Th e special case where all th e positive weights are one is treated in detail in [2] and [6]. The description here of the more general algorithm uses the terminology set up in [2]. Paper [2] (especially sec . 5) helps also to motivate thi s paper, though it is not r eally a prerequisate till section 7 here . The incr ease in difficulty of the maximum weightsum matc hing algorithm relative to the s ize of the graph is not expone ntial, and only moderately algebraic. The algorithm does not involve any \"blind-alley programming\" -which, essentially, amounts to testing a great many combinations . The emphasis in this paper is on relating the matching problem to the theory of continuous linear
1,712Â citations
TL;DR: An overview of the main design concepts of SCIP and how it can be used to solve constraint integer programs is given and experimental results show that the approach outperforms current state-of-the-art techniques for proving the validity of properties on circuits containing arithmetic.
Abstract: Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), which is free for academic and non-commercial use and can be downloaded in source code. This paper gives an overview of the main design concepts of SCIP and how it can be used to solve constraint integer programs. To illustrate the performance and flexibility of SCIP, we apply it to two different problem classes. First, we consider mixed integer programming and show by computational experiments that SCIP is almost competitive to specialized commercial MIP solvers, even though SCIP supports the more general constraint integer programming paradigm. We develop new ingredients that improve current MIP solving technology. As a second application, we employ SCIP to solve chip design verification problems as they arise in the logic design of integrated circuits. This application goes far beyond traditional MIP solving, as it includes several highly non-linear constraints, which can be handled nicely within the constraint integer programming framework. We show anecdotally how the different solving techniques from MIP, CP, and SAT work together inside SCIP to deal with such constraint classes. Finally, experimental results show that our approach outperforms current state-of-the-art techniques for proving the validity of properties on circuits containing arithmetic.
1,163Â citations
TL;DR: A comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT.
Abstract: The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for machine-type communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like long-term evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. In addition, the third generation partnership project cellular-based low-power wide area solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including extended coverage global system for mobile communications for the Internet of Things, enhanced machine-type communications, and narrowband-Internet of Things. Furthermore, 5G new radio enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of massive to critical IoT applications are also presented in coming up with an efficient context-aware congestion control mechanism.
951Â citations