Computational Offloading for Energy Constrained Devices in Multi-Hop Cooperative Networks

Funai, Colin; Tapparello, Cristiano; Heinzelman, Wendi

doi:10.1109/tmc.2019.2892100

Cited by 28 publications

(15 citation statements)

References 17 publications

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“…designed the optimal offloading strategy by the convex optimization method to maximize the task load of the host device under the task delay constraint. In [ 34 ], a heuristic task allocation routing algorithm was proposed to fairly distribute computation tasks among multiple cooperative nodes. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

Guo

Chen

2023

Sensors

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Emergency event monitoring is a hot topic in wireless sensor networks (WSNs). Benefiting from the progress of Micro-Electro-Mechanical System (MEMS) technology, it is possible to process emergency events locally by using the computing capacities of redundant nodes in large-scale WSNs. However, it is challenging to design a resource scheduling and computation offloading strategy for a large number of nodes in an event-driven dynamic environment. In this paper, focusing on cooperative computing with a large number of nodes, we propose a set of solutions, including dynamic clustering, inter-cluster task assignment and intra-cluster one-to-multiple cooperative computing. Firstly, an equal-size K-means clustering algorithm is proposed, which activates the nodes around event location and then divides active nodes into several clusters. Then, through inter-cluster task assignment, every computation task of events is alternately assigned to the cluster heads. Next, in order to make each cluster efficiently complete the computation tasks within the deadline, a Deep Deterministic Policy Gradient (DDPG)-based intra-cluster one-to-multiple cooperative computing algorithm is proposed to obtain a computation offloading strategy. Simulation studies show that the performance of the proposed algorithm is close to that of the exhaustive algorithm and better than other classical algorithms and the Deep Q Network (DQN) algorithm.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

Guo

Chen

2023

Sensors

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“…Device resource availability: especially for scenarios with resource-constrained devices, mainly in terms of battery [11] [12], computing [13], storage [14], and bandwidth [15], efficient resource management is one of the main objectives of TA. Indeed, inefficient use of resources can cause their depletion, leading to faults and failures that may be difficult to cope with.…”

Section: Factors Impacting On Task Allocationmentioning

confidence: 99%

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-ofthe-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities' provisioning.

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“…These may lead to the frequently occurrence of load imbalance between edges. Then the edge with overload can assign some of its offloaded tasks to another edge with light load or no-load to achieve a lower latency than to a cloud [28], which is called multi-hop cooperative offloading [125]. Multi-hop cooperative offloading can be applied in two scenarios of connected devices (hop-d) [98] and connected edges (hop-e) [28].…”

Section: E Multi-hop Cooperative Offloadingmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Computational Offloading for Energy Constrained Devices in Multi-Hop Cooperative Networks

Cited by 28 publications

References 17 publications

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

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