| Nowadays the fact that smart phones have changed people's daily lives is widely known.The function of smart phones become increasingly powerful every day,and they have rich sensory capabilities.The sensors deployed on smart phones,such as accelerometers,gyroscopes,magnetometers,GPS and microphones.The functions not only allow us to communicate with each other but also offer the possibilities of sensing the environment,processing and sharing information.When intelligent human beings carry these smart devices,communicate with each other on mobile networks,execute specific sensing tasks,mobile crowdsesning network is established.Mobile CrowdSensing(MCS)has the advantages of task-driven,mobility,scalability,cost effectiveness and spatiotemporality,comparing to the traditional Internet of things.The MCS applications has been widely adopted for environmental monitoring,traffic surveillance,military use,personal health care,urban dynamic mining and smart cities.MCS is different to the traditional Internet of Things in several aspects: 1.participants recruitment and incentive mechanism of sensing tasks;2.sensing data transmission in communication networks;3.privacy protection of participants in MCS applications.In order to solve the challenges in MCS applications,we study the sensing data collection,sensing task execution and participants privacy protection.We adopted economic model and algorithms to conduct intensive simulations.In this paper,we proposed three main works in MCS:(1)Reward-Aided Sensing Task Execution in Mobile Crowdsensing Enabled by Energy HarvestingThe key challenge that degrades the performance of MCS is selfish mobile users who conserve the resources(e.g.,CPU,battery and bandwidth)of their devices.To solve this problem,we adopt computation offloading to offer participants more options of sensing task execution: local execution and cloud execution.It can release the computing burden from the smart devices by this way.Incentive mechanism is the most important issue which needs to be considered in MCS.A proper incentive mechanismcan make sure the participants in MCS to donate their resource to achieve a common interest.To simulate mobile user to become a participant and remain the number of the participants,researchers have designed extensive method to provide the incentive mechanism.They adopt money,reputation and credit as reward to participants to guarantee the sensing quality.In our work,energy harvesting(EH)is envisioned as a promising way to address the challenge of incentive mechanism.In this paper,we propose a game theoretic approach for achieving sustainable and higher-quality sensing task execution in MCS.We analyzed the problem and developed algorithms.Simulation results demonstrate that the proposed algorithm is able to improve the quality of sensing result and the lifetime of participants' devices.(2)Coverage Guaranteed Data and Energy Routing Cooperation in Mobile CrowdsensingIn MCS,the sensing task initiator collects sensing data by recruiting mobile users.The quality of the sensing result is strongly affected by the quality of sensing data that mobile users donate.Moreover,the effective coverage of target sensing area also affects the result of sensing task.The effective coverage means the quantity of participants in target area needs to fulfill the sensing task requirements.However,mobile users are selfish,and the resources of the mobile phone,such as the battery life,are limited.Some of the mobile users have to expend too many resources to upload the sensing data becausethey are located far away from the base station.This situation may cause poor sensing result.We propose a collaborative routing strategy of data and energy that introduces energy harvesting and cooperation into data routing.The collaborative routing strategy aims to encourage more mobile users to participate in MCS.We adopt energy cooperation and energy harvesting to assist sensing data uploading.For this problem,we develop a collaborative routing algorithm to determine the optimal routes in MCS.Simulation results demonstrate that the proposed routing strategy is able to maximize the ratio of coverage and the sensing quality.(3)A Blockchain-based Security-preserved Reward Mechanism of Mobile CrowdsensingA MCS scenario faces several security challenges,including untrustworthy participants,untrustworthy sensing task initiator and untrustworthy reward transaction.An untrustworthy task initiator may publish a sensing task without a reward guarantee,for example single point failure,it may also try to steal the private information from participants.An untrustworthy participant can forge his identity of reputation,or a malicious participant who steals the sensing data by forwarding cooperation.An untrustworthy reward transaction can fail,due to the mobility of participants.Since the architecture of MCS is usually centralized,MCS needs an authority to process all the communications between the sensing task initiator and the participants.This feature of MCS will add extra operational cost to the whole scenario.We proposed a blockchain-based MCS framework,moreover we proposed a novel incentive mechanism for different roles of participants.We designed three-stage Stackelberg game to improve the utility of both task initiator and participants.We simulate the proposed framework in two aspects.First we evaluate the algorithms,which is the performance of the 3-stage game.And then we evaluate the feasibility of blockchain-based MCS architecture by adopting a private testnet blockchain.In summary,we mainly studied the sensing data transmission,sensing task execution and participants' privacy in MCS.We first analyzed the traditional centralized framework,and then proposed a novel decentralized framework and solution of MCS.We studied the energy consumption in sensing task execution and sensing data transmission.By different sensing task execution mechanism and reward allocating mechanism,it gave the participants more choices in participate the sensing tasks.This pcaper enlightened new approaches of MCS scenario in the future. |
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