Optimal Data Collection for Mobile Crowdsensing Over Integrated Cellular and Opportunistic Networks

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.INDEX TERMS Data collection, mobile crowdsensing, opportunistic networks, cellular networks.

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm

“…In [28], the researchers aimed to collect sensed data from workers that have high coverage properties of MCS fulfilled. The authors in [29] proposed a data-collection path-planning scheme that employed either cellular transmission or an opportunistic network.S. Akter et al [30] investigated task allocation in time-sensitive MCS based on A-DQN.…”