DEED: Dynamic Energy-Efficient Data offloading for IoT applications under unstable channel conditions

Yan, Hui; Zhang, Xiongtao; Chen, Huangke; Zhou, Yun; Bao, Weidong; Yang, Laurence T.

doi:10.1016/j.future.2019.01.014

Cited by 20 publications

(12 citation statements)

References 15 publications

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“…The previous work of the EMEC problem can be divided into four categories. First of all, many previous works focused on minimizing the energy consumption with the assumption that devices equipped with a battery have limited power [2,[4][5][6][7][8]. Authors in [4] proposed the evolutionary algorithm to minimize the energy consumption for data offloading.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [4] proposed the evolutionary algorithm to minimize the energy consumption for data offloading. In [2], Yan et al considered an energy efficient offloading method under unstable channels. The energy consumptions for frequency configuration, transmission power allocation, channel rate scheduling, and offloading strategies were considered in [5][6].…”

Section: Related Workmentioning

confidence: 99%

“…∎ Through Theorem 2, the lower bound can be calculated through Eqs. (2)(3)(4)(5)(6). Furthermore, Eqs.…”

Section: Lower Bound Of the Finishing Timementioning

confidence: 99%

“…In recent years, a significant growth of network connected devices such as smart phones, tablets, laptops, automobiles and drones have been witnessed from the development of Internet of Thing (IoT) [1]. This growth results in the use of tens of billions of devices, which require a massive quantity of computational processing and storage resources for sensory data of the environment in autonomous driving, graphics rendering for online gaming, video streaming and more [2]. Across all these applications, data is considered as one of the most significant resources in many applied personal, industrial or academic settings [3].…”

Section: Introductionmentioning

confidence: 99%

“…An efficient solution to the rising computational requirements is Mobile Edge Computing (MEC) [2]. This allows mobile devices to offload computing tasks to an edge server, which contains higher processing abilities to decrease the computing time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Chevalier

Wang

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NPcompleteness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomialtime approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…∎ Through Theorem 2, the lower bound can be calculated through Eqs. (2)(3)(4)(5)(6). Furthermore, Eqs.…”

Section: Lower Bound Of the Finishing Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Chevalier

Wang

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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EASE: Energy‐efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

Yan

Zhu

et al. 2019

Concurrency and Computation

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Continuously growing network traffic has become a major technical bottleneck of the cloud service to develop the Internet of Things (IoTs) and mobile applications. Edge computing as a promising computing pattern deployed close to service users is expected to improve the quality of service (QoS). To fully utilize the capabilities of edge devices, a Device-to-Device (D2D)-based computing resource sharing and aggregation framework is proposed. Under this framework, this paper exploits the Beta distributions to characterize the uncertain communication rate and processing capability of the edge environment. The reliability and energy consumption of local computing and shared computing under uncertain conditions are, respectively, studied. We model the task scheduling as an Integer Programming problem, whose objective is to minimize the energy consumption while ensuring the reliability. Based on that, a heuristic task scheduling algorithm named EASE is proposed. Through a lot of simulation experiments, the performance of EASE is effectively evaluated under the static and dynamic environments. Compared with three comparison algorithms, EASE shows many advantages in terms of reliability, adaptability, and energy saving.

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Internet of Things data management: A systematic literature review, vision, and future trends

Shirvanian

Shams

Rahmani

2022

Int J Communication

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The Internet of Things (IoT) is expected to connect devices with unique identifiers over a network to create an equilibrium system with high speeds and volumes of data while presenting an interoperability challenge. The IoT data management system is indispensable for attaining effective and efficient performance because IoT sensors generate and collect large amounts of data used to express large data sets. IoT data management has been analyzed from various perspectives in numerous studies. In this study, a Systematic Literature Review (SLR) method was used to investigate the various topics and key areas that have recently emerged in IoT data management. This study aims to classify and evaluate studies published between 2015 and 2021 in IoT data management. Therefore, the classification of studies includes five categories, data processing, data smartness application, data collection, data security, and data storage. Then, studies in each field are compared based on the proposed classification. Each study investigates novel findings, simulation/implementation, data set, application domain, experimental results, advantages, and disadvantages. In addition, the criteria for evaluating selected articles for each domain of IoT data management are examined. Big data accounts for the highest percentage of data processing fields in IoT data management, at 34%. In addition, fast data processing, distributed data, artificial intelligence data with 22%, and data uncertainty analysis account for 11% of the data processing field. Finally, studies highlight the challenges of IoT data management and its future directions.

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DEED: Dynamic Energy-Efficient Data offloading for IoT applications under unstable channel conditions

Cited by 20 publications

References 15 publications

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

EASE: Energy‐efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

Internet of Things data management: A systematic literature review, vision, and future trends

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