A Novel Shortcut Addition Algorithm With Particle Swarm for Multisink Internet of Things

Qiu, Tie; Li, Bolun; Zhou, Xiaobo; Song, Houbing; Lee, Ivan; Lloret, Jaime

doi:10.1109/tii.2019.2925023

Cited by 27 publications

(17 citation statements)

References 32 publications

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“…An important problem in the IoT is to reduce the number of hops from the sensor to the sink. Reference [57] studied the modeling problem of small-world networks with multiple convergence points, and proposed a PSO-based novel shortcut addition algorithm for multi-sink IoT (SAPS). Each cluster is treated as a single aggregate network.…”

Section: Application Of Swarm Intelligence Algorithms In Other Iotsmentioning

confidence: 99%

A Survey of Using Swarm Intelligence Algorithms in IoT

Sun

Tang

Zhang

et al. 2020

Sensors

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With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

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Section: Application Of Swarm Intelligence Algorithms In Other Iotsmentioning

confidence: 99%

A Survey of Using Swarm Intelligence Algorithms in IoT

Sun

Tang

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, the majority of the literature on PVEC have focused on allocating the resources of PVs [7,27]. Gu et al [13] propose a two-tier data center architecture that leverages the excessive storage resources in parking lots.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things

Cheng

et al. 2020

Sensors

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Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.

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“…However, the selection of reward forms in incentive strategies needs to meet the different preferences of each task completers, it is difficult to achieve. Also, some researchers improve the quality of service of task completers by improving the characteristics [31] of crowdsourcing tasks. Guo et al [32] showed that the introduction of occasional breaks, such as playing games or reading comics, can improve task completers' quality of service.…”

Section: Mechanisms For Solving the Quality Of Service Dilemmamentioning

confidence: 99%

Two-Stage Game Design of Payoff Decision-Making Scheme for Crowdsourcing Dilemmas

Xia

Zhang

Cheng

et al. 2020

IEEE/ACM Trans. Networking

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Crowdsourcing uses collective intelligence to finish complicated tasks and is widely applied in many fields. However, the crowdsourcing dilemmas between the task requester and the task completer restrict the efficiency of system severely, e.g., the cooperation dilemma leads to the failure in the interactions and the quality of service dilemma results in the inability of task completer to provide high-quality service. Current research usually focuses on solving only one aforementioned dilemma and fails to integrate perfectly with the service architectural pattern of crowdsourcing systems. In this article, combined with the crowdsourcing interaction phase, we limit the objects that cause dilemma and propose a two-stage game payoff decision-making scheme (TGPD) to overcome these shortcomings. To solve the cooperation dilemma between the requester and the crowdsourcing platform, we first propose a dynamic payment method based on the reputation-quality rules for the task requester, and then develop a cos-evaluation algorithm to estimate platform's cost, last design a co-determine algorithm to determine whether the platform adopts a cooperative strategy. To address the quality of service dilemma between the crowdsourcing platform and the workers, we first present an auction-screening method to estimate the reasonable recruitment range of workers which can be optimized by the result of cos-evaluation algorithm, and then use a reward distribution method to motivate workers to complete tasks with high quality and on time. The experimental results indicate that our new scheme successfully increases the worker's and platforms' payoffs at the same time, improves the accuracy of screening workers, enhances the worker's quality of service, and decreases the platform's cost.

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A Novel Shortcut Addition Algorithm With Particle Swarm for Multisink Internet of Things

Cited by 27 publications

References 32 publications

A Survey of Using Swarm Intelligence Algorithms in IoT

A Survey of Using Swarm Intelligence Algorithms in IoT

A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things

Two-Stage Game Design of Payoff Decision-Making Scheme for Crowdsourcing Dilemmas

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