Spatial crowdsourcing with mobile agents in vehicular networks

Urra, Óscar; Ilarri, Sergio

doi:10.1016/j.vehcom.2019.03.004

Cited by 15 publications

(10 citation statements)

References 67 publications

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“…The wireless communication range between vehicles is 250 meters [27] with a bandwidth of 54 Mbps (nominal transfer rate of IEEE 802.11g). Moreover, buildings are The simulator used to perform the experiments [39,41] has been developed to test easily different configurations and data management approaches based on mobile agents. For example, we can easily change the query processing algorithms used by the agents, their hopping strategies, the mobility models used by the vehicles, the underlying road network infrastructure, etc., and obtain statistics about different performance parameters (such as the number of communications performed, or the partial times invested in each step of the query processing).…”

Section: Experimental Settingsmentioning

confidence: 99%

An approach driven by mobile agents for data management in vehicular networks

Urra

Ilarri

Trillo-Lado

2017

Information Sciences

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In the last years, and thanks to the improvements on computing and communications technologies, wireless networks formed by vehicles (called vehicular networks) have risen as a key topic of interest. In these networks, the vehicles can exchange data with others by using short-range radio signals in order to get useful information related to traffic conditions, road safety, and others. The availability of different types of sensors can be exploited by the vehicles to measure many parameters from their surroundings. These data can then be shared with other drivers who, on the other side, could also explicitly submit queries to retrieve information available in the network. This can be a challenging task, since the data is scattered among the vehicles belonging to the network and the communication links among them have usually a short life due to their constant movement.In this paper, we use the technology of mobile software agents to help to accomplish these tasks, since they have a number of features that are very well suited for their use in mobile environments, such as their autonomy, mobility, and intelligence. Specifically, we analyze the benefits that mobile agents can bring to vehicular networks and the potential difficulties for their adoption. Moreover, we describe a query processing approach based on the use of mobile agent technology. We focus on range queries that retrieve interesting information from the vehicles located within a geographic area and present an extensive experimental evaluation that shows the feasibility and the interest of the proposal.

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Section: Experimental Settingsmentioning

confidence: 99%

An approach driven by mobile agents for data management in vehicular networks

Urra

Ilarri

Trillo-Lado

2017

Information Sciences

Self Cite

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“…However, some recent projects integrate sensors in public transportation systems, overcoming some of the issues derived from fixed sensors [13]. Moreover, there are research works that go further proposing crowdsourcing schemes, in which the sensors are installed in private vehicles and mobile devices [14]. The main contribution of the SwarmCity project is the possibility to move the sensors through the city, which allows measuring in the requested locations and times using fewer sensors.…”

Section: Related Workmentioning

confidence: 99%

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

Roldán‐Gómez

Garcia-Aunon

Mazariegos

et al. 2020

Pers Ubiquit Comput

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Smart cities have emerged as a strategy to solve problems that current cities face, such as traffic, security, resource management, waste, and pollution. Most of the current approaches are based on deploying large numbers of sensors throughout the city and have some limitations to get relevant and updated data. In this paper, as an extension of our previous investigations, we propose a robotic swarm to collect the data of traffic, pedestrians, climate, and pollution. This data is sent to a base station, where it is treated to generate maps and presented in an immersive interface. To validate these developments, we use a virtual city called SwarmCity with models of traffic, pedestrians, climate, and pollution based on real data. The whole system has been tested with several subjects to assess whether the information collected by the drones, processed in the base station, and represented in the virtual reality interface is appropriate. Results show that the complete solution, i.e., fleet control, data fusion, and operator interface, allows monitoring the relevant variables in the simulated city.

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“…Besides leveraging user-provided data, it is also imperative to incorporate agents with MCS campaigns [74]. Normally, mobile agents would be deployed in a crowdsensing setting to collect and/or query data [75] or perform other tasks as overviewed below.…”

Section: Agent-based Strategiesmentioning

confidence: 99%

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Dasari

Kantarcı

Pouryazdan³

et al. 2020

Sensors

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Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

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Spatial crowdsourcing with mobile agents in vehicular networks

Cited by 15 publications

References 67 publications

An approach driven by mobile agents for data management in vehicular networks

An approach driven by mobile agents for data management in vehicular networks

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

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