Abstract
One of the main challenges that mobile crowdsensing systems must solve is reducing data collection costs while still holding high data delivery probability. Compared with cellular networks, opportunistic networks can significantly reduce data transfer costs at the cost of damaging data delivery probability. This paper proposes an optimal data collection scheme for mobile crowdsensing, which utilizes integrated cellular and opportunistic networks to implement data collection. We use data collecting path to describe how the sensing data are collected and sent to the back-end platform, though cellular networks directly or through multi-hop opportunistic networks. An optimal data collection problem is then formulated as choosing specific data collecting paths from candidate path set to minimize the total crowdsensing cost under the data delivery constraints, which can be considered as a minimum set covering problem. To solve this NP-hard problem, we design and implement a greedy heuristic algorithm that constructs the solution in multiple steps by making a locally optimal decision in each step. We conduct extensive simulations based on three real-world traces: Cambridge, Infocom06, and UPB. The results show that, compared with other data collection approaches, our approach achieves a better tradeoff between cost and data delivery.
Highlights
Nowadays, mobile crowdsensing become more and more popular with the development of mobile personal devices such as smartphones or smartwatches with significantly more sensing, computing, communication, and storage resources [1], [2]
RELATED WORK Data collection is an essential part of building a mobile crowdsensing system [18], which includes the following two aspects: (1) which users should be recruited to participate in sensing activities and (2) how to transfer the sensing data to the back-end platform
The second scheme [17] uses an opportunistic network to upload data to the back-end platform, referred to as OMinCost. It translates the statistics of individual user mobility to statistics of space-time path formation and selects the data collection paths set with the minimum cost to meet Points of Interest (PoIs) data delivery constraints
Summary
Mobile crowdsensing become more and more popular with the development of mobile personal devices such as smartphones or smartwatches with significantly more sensing, computing, communication, and storage resources [1], [2]. An essential operation in mobile crowdsensing is to perform data collection to minimize the communication cost while still satisfying sensing coverage and data quality constraints Designing such a data collection strategy usually includes (1) deciding how to recruit the participants among the candidates who visit the sensing area and have the willingness to capture data; (2) deciding how to transfer the captured data to the back-end platform. The participants may still need to pay extra money for data transferring, which may prevent mobile users from participating in sensing activities and lead to more incentive costs This strategy generates additional workload for the cellular network and increases the communication cost. Mobile devices participating in sensing activities can either transmit data to the back-end platform directly through cellular networks or transfer data to other devices and let other devices transmit the data to the backend platform through short-distance radio to balance the cost and successful data delivery probability.
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