Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization

Bi, Jing; Yuan, Haitao; Duanmu, Shuaifei; Zhou, MengChu; Abusorrah, Abdullah

doi:10.1109/jiot.2020.3024223

Cited by 249 publications

(87 citation statements)

References 39 publications

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“…The energy consumption and processing/transmission time of computing tasks are regarded as costs. With the support of a cloud computing center, a distributed algorithm for cost minimization is proposed by optimizing the offloading decision and resource allocation of a mobile edge computing system [ 48 ]. Its experimental results show that compared with other existing algorithms, i.e., Greedy algorithm, and those in [ 49 , 50 ], the cost can be reduced by about 30%.…”

Section: Computing Task Scheduling Schemementioning

confidence: 99%

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

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In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

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Section: Computing Task Scheduling Schemementioning

confidence: 99%

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

Self Cite

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“…The number of salespersons B σ in each source depot σ ∈ is assumed to be provided a priori, and it is such that i∈ B i = |S|. The definition of the initial deployment of the salespersons over the source depots is given by (7). It is necessary to ensure that the same salesperson enters and exits a certain city.…”

Section: Problem Formulation Of the Extended Colored Tspmentioning

confidence: 99%

“…When the model of the mission planning problem is defined, several different choices on how to obtain the mission plan can be made, based on the mission requirements. For example, if the optimization time is limited, the usage of metaheuristic algorithms or a combination of them is shown to be a good choice [7], [8]. On the other hand, if the focus of the mission planning is toward the optimality of the solution, This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

Miloradović¹,

Çürüklü²,

Ekström³

et al. 2022

IEEE Trans. Cybern.

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The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances. Index Terms-Extended colored traveling salesperson problem (ECTSP), genetic algorithm (GA), multirobot mission planning, multirobot systems.

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“…PSO is an evolutionary computation technique that simulates the social behaviour of bird flocking. This computationally efficient method is easy to implement [ 32 , 33 , 34 ]. The proposed localisation method assumes a centralised architecture for the WSNs, so that all the neighbouring anchor nodes can communicate to a central entity where the PSO can be implemented.…”

Section: Introductionmentioning

confidence: 99%

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Kotiyal

Singh

Sharma

et al. 2021

Sensors

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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

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Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization

Cited by 249 publications

References 39 publications

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

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