Resource Discovery Using Spatio-temporal Information in Mobile Ad-Hoc Networks

Wolfson, Ouri; Xu, Bo; Yin, Huabei; Rishe, Naphtali

doi:10.1007/11599289_12

Cited by 13 publications

(12 citation statements)

References 9 publications

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“…Total simulated time was 20 hours for each of the 29 tests. Results were compared to ranking by age or distance alone, and to the parking availability formula (IGS) given in [5]. The IGS method represents the optimal combination of age and distance.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the use of vehicular ad-hoc networks (VANETs) has gained attention for purposes of information dissemination in a variety of applications, such as parking [1,2,3,4,5] or traffic information dissemination [7]. A common problem in all of such applications is evaluating the relevance of a piece of information for specific vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Existing methods for parking information systems either relied on heuristic approaches for relevance estimation [1,2] or assumed vehicles know which information is irrelevant [3,4]. In [5], relevance function is based on a formula for parking space availability probability, but parameters of this formula would not be known to vehicles. In this paper, a method is proposed to learn a model that can be used to calculate relevance of a piece of information.…”

Section: Introductionmentioning

confidence: 99%

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Learning the relevance of parking information in VANETs

Szczurek

Wolfson

et al. 2010

Proceedings of the Seventh ACM International Workshop on VehiculAr InterNETworking

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The use of Vehicular Ad-Hoc Network (VANET) has been applied to many applications involving information dissemination. Many of such applications are limited by the communication limitations of a VANET, such as limited transmission range and bandwidth. This imposes a necessity for evaluating the relevance of information. This paper proposes the use of machine learning for finding relevance of information for a parking information dissemination system. The proposed method uses the learned relevance for aiding vehicles in decision making by finding the probability that a given parking location will be available at the time of arrival. The method was evaluated through simulations and the results show that the proposed method is successful at learning the relevance of parking reports, which resulted in lower parking discovery times for vehicles.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning the relevance of parking information in VANETs

Szczurek

Wolfson

et al. 2010

Proceedings of the Seventh ACM International Workshop on VehiculAr InterNETworking

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unlike global approaches, decentralized algorithms facilitate fast local updating and do not require hard-to-maintain global consistency [22]. Other researchers including [19,23,24,25] have all investigated decentralized knowledge maintenance and creation in spatial applications to cope with the problems posed by mobility in mWSN. Such decentralized approaches can also help to increase energy efficiency [16], increase scalability and robustness [26], and reduce the potential for information overload [27,28].…”

Section: Mobile Wireless Sensor Networkmentioning

confidence: 99%

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Laube

Duckham

Wolle

2008

Geographic Information Science

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Abstract. Movement patterns, like flocking and converging, leading and following, are examples of high-level process knowledge derived from lowlevel trajectory data. Conventional techniques for the detection of movement patterns rely on centralized "omniscient" computing systems that have global access to the trajectories of mobile entities. However, in decentralized spatial information processing systems, exemplified by wireless sensor networks, individual processing units may only have access to local information about other individuals in their immediate spatial vicinity. Where the individuals in such decentralized systems are mobile, there is a need to be able to detect movement patterns using collaboration between individuals, each of which possess only partial knowledge of the global system state. This paper presents an algorithm for decentralized detection of the movement pattern flock, with applications to mobile wireless sensor networks. The algorithm's reliability is evaluated through testing on simulated trajectories emerging from unconstrained random movement and correlated random walk.

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“…This question has already begun to be answered by [20,21]. The information dissemination strategy explored in this paper, where agents communicate with one another whenever they happen to be in close spatiotemporal proximity, is similar to the opportunistic exchange described in [21,22].…”

Section: Mobile Ad-hoc Geosensor Networkmentioning

confidence: 99%

Information Dissemination in Mobile Ad-Hoc Geosensor Networks

Nittel

Duckham

Kulik

2004

Geographic Information Science

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Abstract. This paper addresses the issue of how to disseminate relevant information to mobile agents within a geosensor network. Conventional mobile and location-aware systems are founded on a centralized model of information systems, typified by the client-server model used for most location-based services. However, in this paper we argue that a decentralized approach offers several key advantages over a centralized model, including robustness and scalability. We present an environment for simulating information dissemination strategies in mobile ad-hoc geosensor networks. We propose several strategies for scalable, peer-to-peer information exchange, and evaluate their performance with regard to their ability to distribute relevant information to agents and minimize redundancy.

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Resource Discovery Using Spatio-temporal Information in Mobile Ad-Hoc Networks

Cited by 13 publications

References 9 publications

Learning the relevance of parking information in VANETs

Learning the relevance of parking information in VANETs

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Information Dissemination in Mobile Ad-Hoc Geosensor Networks

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