Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensi...Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.展开更多
Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.Howeve...Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.展开更多
In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit...In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.展开更多
The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle parti...The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.展开更多
With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders...With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.展开更多
基金supported by the Inner Mongolia Natural Science Foundation(Grant No.2023MS06022)the University Youth Science and Technology Talent Development Project(Innovation Group Development Plan)of Inner Mongolia A.R.of China(Grant No.NMGIRT2318)+1 种基金the“Inner Mongolia Science and Technology Achievement Transfer and Transformation Demonstration Zone,University Collaborative Innovation Base,and University Entrepreneurship Training Base”Construction Project(Supercomputing Power Project)(Grant No.21300-231510)the Engineering Research Center of Ecological Big Data,Ministry of Education.
文摘Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.
基金sponsored by Qinglan Project of Jiangsu Province,and Jiangsu Provincial Key Research and Development Program(No.BE2020084-1).
文摘Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.
文摘In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.
基金supported in part by the Natural Science Foundation of Shandong Province of China(ZR202103040180)the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-004the Fundamental Research Funds for the Central Universities under Grant 20CX05019A.
文摘The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.
基金supported in part by the National Natural Science Foundation of China under Grant U1905211,Grant 61872088,Grant 62072109,Grant 61872090,and Grant U1804263in part by the Guangxi Key Laboratory of Trusted Software under Grant KX202042+3 种基金in part by the Science and Technology Major Support Program of Guizhou Province under Grant 20183001in part by the Science and Technology Program of Guizhou Province under Grant 20191098in part by the Project of High-level Innovative Talents of Guizhou Province under Grant 20206008in part by the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province under Grant ZCL21015.
文摘With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.
