EditorialMobile Crowdsourcing and Pervasive Computing for Smart Cities
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Cited by (16)
Enabling civil–military collaboration for disaster relief operations in smart city environments
2023, Future Generation Computer SystemsCitation Excerpt :An interesting work that discusses the importance of adopting location-based services is [21]. Furthermore, location-based services are useful to implement collaborative edge computing and mobile crowdsensing applications [22–24]. In particular, collaborative computing and crowdsourcing can be effective tools to enable collaborative sharing of information [25].
Min–max movement of barrier coverage with sink-based mobile sensors for crowdsensing
2022, Pervasive and Mobile ComputingCitation Excerpt :This brings the min–max Sink-based Line Barrier Coverage (min–max SLBC) problem which aims to collaborate a coverage over a given barrier with sink-based sensors in an energy-efficient way. The min–max SLBC problem has broad applications in mobile crowdsensing networks, among which we shall emphasize the one from [2–4]. With the explosive popularity of wireless communication and the development of sensor technology, users’ mobile phones or tablets integrate sensors and acquire the power of computation and perception.
Route recommendation based on temporal–spatial metric
2022, Computers and Electrical EngineeringCitation Excerpt :Specially, mobile recommendations [11] have attracted much attention from the research community. Extracting valuable and useful information from these location trajectory data [6] can offer powerful support for the intelligent transportation services [12] in the relevant application scenarios. In this paper, we particularly concentrate on the problem of providing route recommendations for a vacant taxi, which is extraordinarily significant since it not only helps drivers save cost by increasing the vehicle occupancy and utilization rates [13,14], but also improves the traffic conditions.
Cooperative monitoring and dissemination of urban events supported by dynamic clustering of vehicles
2020, Pervasive and Mobile ComputingCitation Excerpt :Usually, those strategies employ devices that can collect data (e.g. smartphones, cameras and vehicles) to support other systems in their the decision making processes regarding the treatment of specific events. In the context of Vehicular Ad Hoc Networks (VANETs), vehicles act as mobile crowd sensors [8,9] in urban sensing scenarios to assist the development of smart applications [10]. They intercommunicate with other vehicles (Vehicle-to-Vehicle — V2V) and the city’s network infrastructure (Vehicle-to-Infrastructure — V2I) to exchange and disseminate contextual information [11], which is paramount for a variety of safety and entertainment related goals [5,12].
Multitask-Oriented Collaborative Crowdsensing Based on Reinforcement Learning and Blockchain for Intelligent Transportation System
2023, IEEE Transactions on Industrial Informatics