Opportunistic Data Mules for Short Delay Smart City Applications

Chehbour, Fairouz; Doukha, Zouina; Moussaoui, Samira; Gabsi, Mohamed

doi:10.1109/saconet.2018.8585500

Cited by 1 publication

(2 citation statements)

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“…For that, we consider two simulation scenarios. In the first one, the election is performed using the fitness function stated in the primary version of this work . In this case, the coefficients of the fitness function parameters are identical and are all equal to 1 ( a = b = c =1).…”

Section: Simulation and Measurementsmentioning

confidence: 99%

“…To the best of our knowledge, this is the first work that deals with high latency of DM‐based data collection in urban areas. A partial version of this work is stated in Chehbour et al More details, deeper analysis and simulation scenarios are provided in the current work. The main contributions of the later can be summarized as follows: Develop a new scheme that provides an AoI with multiple oblivious DMs in which we try to opportunistically designate a DM to each road segment in order to distribute at best the data collection process and hence, alleviate the increased latency drawback of the mulling approach. Develop an opportunistic DM election algorithm that operates in intersections (called later DMEA for Data Mule Election Area) to designate for each outgoing road segment a DM according to vehicles heading and speed. Develop an optimized query propagation strategy in order to activate the DM election process within a minimum delay in almost all DMEAs so that to simultaneously collect data from all segments. Finally, we identify the most relevant performance metrics related to the proposed scheme through extensive simulations. …”

Section: Introductionmentioning

confidence: 99%

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OMS: Opportunistic mules for short latency data collection in smart cities

Chehbour

Doukha

Moussaoui

et al. 2019

Int J Communication

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In smart city scenarios, data collected by sensors are required to be conveyed to central controllers for processing. Thus, efficient data collection mechanisms considering the urban environment characteristics should take place. To that end, the data mules approach is proved to be efficient. However, being applied to urban environment leads to high data delivery latency due to the characteristics of this environment. Motivated by the need to reduce the collection latency of such an approach, we propose to provide each road segment with a data mule to create a process of distributed data collection. Our scheme leverages opportunistic inter-vehicular communications for assigning the mule role to adequate vehicles. The simulation in realistic mobility settings reveals that our scheme can provide more than 80% of the area of interest with data mules in less than 1 minute. In addition, the data collection latency can be reduced from 24 hours to few minutes. VANETs INTRODUCTIONOne goal of Internet of Things (IoT) paradigm is to provide cities with smart services in order to enhance their sustainability. Smart City projects rely on advanced technologies such as wireless sensing and communication technologies to deal with air pollution, poor waste management, traffic congestion, and energy misuse. 1 Wireless sensor networks (WSNs) are core components for data acquisition in smart city applications. Indeed, due to their low cost and small size, sensing devices can be easily deployed at a large variety of locations allowing coupling the physical infrastructure to information and communication technologies. However, the issue of collecting the data from the huge amount of sensors and actuators spread across streets is still considered as the main challenge for smart city realization. In fact, the complexity of the urban environment can cause network fragmentation due to forcefully sparse node deployments or unpredictable dynamics in wireless link availability. 2 Even if cellular communications have been suggested as an alternative solution, it could be very expensive because of the need of a permanent infrastructure to be built to cover all the nodes. Moreover, it is difficult from economic and administrative points of view to equip each sensor device with a SIM card. 3 Furthermore, the energy consumed for the data collection process could be critical and highly dependent on the quality of the required communication.One effective, economical, and suitable way to deliver sensor data to the central controller is to exploit mobile elements (MEs) that may be robots or vehicles. In fact, while moving, MEs can download sensors data once they are within their communication range. [2][3][4][5][6][7][8] This approach, well known as data mule (DM) approach, is proved to be effective in energy Int J Commun Syst. 2020;33:e4207.wileyonlinelibrary.com/journal/dac

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Section: Simulation and Measurementsmentioning

confidence: 99%