Providing Task Allocation and Secure De duplication For Mobile Crowd sensing via Fog Computing
Mobile crowd sensing enables a crowd of individuals to cooperatively collect data for special interest customers using their mobile devices. The success of mobile crowd sensing largely depends on the participating mobile users. The broader participation, the more sensing data are collected; nevertheless, the more replicate data may be generated, thereby bringing unnecessary heavy communication overhead. Hence it is critical to eliminate duplicate data to improve communication efficiency, a.k.a., data deduplication. Unfortunately, sensing data is usually protected, making its deduplication challenging. In this paper, we propose a fog-assisted mobile crowdsensing framework, enabling fog nodes to allocate tasks based on user mobility for improving the accuracy of task assignment. Further, a fog-assisted secure data deduplication scheme (Fo-SDD) is introduced to improve communication efficiency while guaranteeing data confidentiality. Specifically, a BLS-oblivious pseudo-random function is designed to enable fog mnodes to detect and remove replicate data in sensing reports without exposing the content of reports. To protect the privacy of mobile users, we further extend the Fo- SDD to hide users’ identities during data collection. In doing so, Chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous mobile users. Finally, we demonstrate that both schemes achieve secure, efficient data deduplication.
Keywords: Mobile crowdsensing, fog computing, task allocation, secure deduplication.
Mobile crowd sensing is a compelling paradigm that allows a large group of individuals to collaboratively sense data and extract information about social events and national
phenomena with common interest using their mobile devices, e.g., smart phones, smart glasses, drones, cameras and smart vehicles. It supports an ever-increasing number of sensing applications, ranging from social recommendation, such as restaurant recommendation, vehicular navigation and parking space discovery, to environment monitoring, such as air quality measurement, noise level measurement and dam water release warning. With the human intelligence and user mobility, it improves the quality of sensing data, extends the scale of sensing applications, and reduces the cost on high-quality data collection. In mobile crowd sensing, one of the main challenges is to find proper mobile users for sensing tasks to achieve efficient and scalable data collection. Firstly, due to the unique requirements of sensing tasks and the user mobility, a crowd sensing server (CS-server) collects various types of information about mobile users, e.g., location, reputation and activity pattern, and thereby customizes a task allocation policy for each sensing task. For example, to measure traffic congestion in downtown Toronto, the CS-server should recruit the mobile users driving on the roads in downtown Toronto. Secondly, it is hard to guarantee that the potential mobile users could receive the assigned sensing tasks and upload sensing reports in time. Thirdly, to perform sensing tasks, mobile users have to travel to specific locations with a certain cost on time and travel. Therefore, there should be an effective framework for the CS-server to allocate sensing tasks to proper mobile users.
To ensure sensing tasks to be fulfilled effectively, how to select mobile users to perform the tasks is critical in mobile crowd sensing. Several reputation-based task allocation mechanisms have been proposed to evaluate the trustworthiness of mobile users and assign tasks to the mobile users with high reputation. Proposed an anonymous reputation management scheme and a data trust assessment scheme. These schemes enforce both positive and negative reputation updates and preserve the identities of mobile users without the involvement of trusted third parties. To achieve better accuracy, defined reputation scores to represent the probability that a mobile user can perform a task correctly, and a confidence level to state that a task is acceptable if its confidence is higher than a given threshold. Moreover, spatial-temporal correlation is widely used to recruit mobile users. Kazemi and focused on spatial task assignment for spatial crowd sourcing, in which the service provider allocates tasks using greedy, least location entropy priority or nearest neighbor priority algorithm based on the location of mobile users
In this paper, we have developed a fog-assisted mobile crowd sensing (Fo-MCS) framework to improve the accuracy of task allocation with the aid of fog nodes. We have also proposed a fog-assisted secure data deduplication scheme (Fo- SDD) to reduce the communication overhead between fog nodes and CS-server. The Fo-SDD enables fog nodes to detect and erase the replicate data in sensing reports, and provides high security guarantee against brute-force attacks and “duplicate-replay” attacks. To resist “duplicate-linking” leakage, we have extended the Fo-SDD to hide the identities of mobile users, such that no attacker can link the identical sensing reports to specific mobile users. In addition, we have leveraged Chameleon hash function to achieve contribution claim and reward retrieval for anonymous mobile users. Finally, we have discussed the security and efficiency of the proposed schemes and demonstrated the advantages of the Fo- MCS framework. For the further work, we will investigate location privacy preservation for mobile users in fog-assisted mobile crowd sensing.
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