Robustness Optimization Scheme With Multi-Population Co-Evolution for Scale-Free Wireless Sensor Networks

Qiu, Tie; Liu, Jie; Si, Weisheng; Wu, Dapeng

doi:10.1109/tnet.2019.2907243

Cited by 159 publications

(58 citation statements)

References 32 publications

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“…The two-person game models are often used to define the expected reward of mobile phone users [22]. Hu et al [23] used the iterated prisoner's dilemma to analyze the worker's hostile attack issue and proposed a solution based on zero-determinant strategy [24] to promote cooperation between users. However, the incentive schemes based on traditional game theory often imply the assumption that the players are rational and cannot fully adapt to the environment of crowdsourcing systems.…”

Section: Mechanisms For Solving the Cooperation 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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Section: Mechanisms For Solving the Cooperation 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

Self Cite

View full text Add to dashboard Cite

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“…A proper service migration strategy should make a trade-off between the service latency and energy consumption of PVs, which encounters two challenges [18,19]. One challenge is that the parking duration of PVs greatly affects the overall performance of the service migration strategy [4].…”

Section: Introductionmentioning

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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“…The main causes of the haze pollution are the emission of exhaust gas from industrial production, smoke and dust from coal combustion, waste gas from vehicles, and dust from construction sites [1], [2]. Different data retrieval methods have been used from historical monitoring results to WSN-based collection [3], as well as its optimization [4], [5]. In this case, if the relative humidity is high and the air flow is relatively slow, the air will easily saturate and condense to form haze through the cooling of atmospheric radiation.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values

Chen

Wang

et al. 2020

IEEE Access

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In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value. INDEX TERMS Real-time sensing and predicting, Kalman filter, air quality index, simulation.

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Robustness Optimization Scheme With Multi-Population Co-Evolution for Scale-Free Wireless Sensor Networks

Cited by 159 publications

References 32 publications

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

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

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

An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values

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