Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory

Abstract

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial-temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.

Internet of medical things for smart D3S to enable road safety

“…EEG is a non-invasive technique that records the electric potential of the cortical neurons with the help of electrodes placed on the scalp. EEG signals measure the potential fluctuation of cortical neurons, and based on frequency bands, the signals are classified as alpha signals (8)(9)(10)(11)(12)(13), beta signals (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), gamma signals , and delta signals (1-4 Hz). We are interested in the alpha wave band as it designates drowsiness.…”

“…Although vehicular-based methods are cost-effective, they are not considered as reliable methods because of driver's skills, road traffic, and the condition of the road. 9,10 The third category consists of the driver's behavior. It includes eye blinking, yawning, eye closure, and head positioning and other facial expression analyses.…”

Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective

“…The recruiter recruits participants to monitor the surrounding environment and contribute to mobile crowdsensing tasks. Meanwhile, the participants are rewarded according to their contribution and the incentive mechanisms of the MCS system [6], [7]. MCS was The associate editor coordinating the review of this manuscript and approving it for publication was Zhenyu Zhou.…”

“…In [4], He et al presented a participant recruitment strategy for vehicle based crowdsensing, considering both current location and future estimated trajectories of the vehicles, and they consider the spatial coverage and temporal coverage respectively. A combination of spatial and temporal coverage was studied in [6] based on public transport with predictable trajectory. In [22], Liu et al proposed a vehicle selection algorithm which can collect comprehensive spatial temporal sensing data.…”

“…Generally, the methods to maximize the sensing data or sensing coverage both belong to NP-complete problem which is proved in [4], [6], [21], and [22], and choosing participants is always modeled as an optimization problem. Nevertheless, to find the optimal or sub-optimal solution in effective time, the performance of the proposed algorithms has to be trade off in the above literatures.…”