WSN Architectures for Intelligent Transportation Systems

Khanafer, Mounib; Guennoun, Mouhcine; Mouftah, Hussein T.

doi:10.1109/ntms.2009.5384685

Cited by 43 publications

(20 citation statements)

References 24 publications

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“…Realizing their limitation of lacking real-time traffic information, recent research work focus on employing new architectures such as neural network [9], Wireless Sensor Network (WSN) [10] to enhance the information accuracy and routing efficiency. For vehicular wireless network, the infrastructureless route guidance system [11] have been studied in [12] [13], which mainly rely on the inter-communication between vehicles to share information.…”

Section: Introductionmentioning

confidence: 99%

Efficient route guidance in vehicular wireless networks

Sun

Xie

Chen

et al. 2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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With the rapid proliferation of Wi-Fi technologies in recent years, it has become possible to utilize the vehicular wireless network to assist the route guidance for drivers in a cooperative approach, aiming to mitigating heavy traffic congestion. In this paper, we investigate into the route guidance problem in vehicular wireless network, and then propose two efficient routing algorithms, i.e., centralized route guidance and distributed route guidance, according to different situations. A hybrid framework is then proposed to provide optimized routing decisions in a uniform way. Simulation results in Simulation of Urban MObility (SUMO) indicate that, our route guidance schemes achieve much better performance than traditional GPS-based navigation and randomized routing.

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Section: Introductionmentioning

confidence: 99%

Efficient route guidance in vehicular wireless networks

Sun

Xie

Chen

et al. 2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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“…For example, WSNs for highway monitoring [1,9,21] are often deployed along the highway direction and thus form a one dimensional deployment topology, as sketched in Fig. 3.…”

Section: Introductionmentioning

confidence: 99%

Distributed Range-Free Localization of Wireless Sensor Networks via Nonlinear Dynamics

Naseer¹

2012

Wireless Sensor Networks - Technology and Protocols

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“…Mobile and pervasive computing have recently garnered significant attention because of their potential to enable novel and attractive solutions in areas such as environmental monitoring [7], transportation enterprises [16], and health care [8]. The development of mobile and pervasive computing has benefitted from many different views of the communication and computing universe such as layered design, client-server, distributed networks, cloud-based computing, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Helal

Thai

et al. 2011

Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in-and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving.

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WSN Architectures for Intelligent Transportation Systems

Cited by 43 publications

References 24 publications

Efficient route guidance in vehicular wireless networks

Efficient route guidance in vehicular wireless networks

Distributed Range-Free Localization of Wireless Sensor Networks via Nonlinear Dynamics

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

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