Performance analysis of multi-hop ad-hoc network using multi-flow traffic for indoor scenarios

Hiyama, Masahiro; Ikeda, Makoto; Barolli, Leonard; Takizawa, Makoto

doi:10.1007/s12652-010-0021-3

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The basic idea, inspired by traditional P2P systems (Androutsellis-Theotokis and Spinellis 2004), consists of diffusing the queries to different data sources either directly or using multi-hop relaying techniques such as the ones in Biswas and Morris (2005), Lochert et al (2003) and Hiyama et al (2010). Then, each node can compute a partial query result based on its local data and then deliver it to the destination node.…”

Section: Query Dissemination: Pull Modelmentioning

confidence: 99%

Multi-scale query processing in vehicular networks

Delot

Ilarri

Thilliez

et al. 2011

J Ambient Intell Human Comput

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In the last decade, a number of wireless and small-sized devices (e.g., PDAs, smartphones, sensors, laptops, etc.) with increasing computing capabilities have appeared in the market at very affordable costs. These devices have started to be embedded in modern cars in the form of on-board computers, GPS navigators, or even multimedia centers. Thus, the vehicles can carry useful information, acting as data sources for other vehicles. Recently, some works have addressed the problem of processing queries in such highly dynamic vehicular networks in order to share information between drivers. The proposed query processing techniques usually rely on a push model. Hence, each vehicle receives data from its neighbors and decides whether they are relevant enough to be stored in a local data cache. Then, the data may be used by a query processor to retrieve relevant data for the driver. In this paper, we look at the problem from a broader perspective and discuss the interest of multi-scale query processing techniques in such context. The goal of such techniques is to exploit, at the mobile device's level, different access modes (e.g., push, pull) and various data sources (e.g., data cached locally, data stored by vehicles nearby, remote Web services, etc.) to provide the users with results for their queries. We highlight the most important challenges and outline some possible approaches. We also present a prototype of a first query evaluator developed using the Microsoft LINQ API.

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Section: Query Dissemination: Pull Modelmentioning

confidence: 99%

Multi-scale query processing in vehicular networks

Delot

Ilarri

Thilliez

et al. 2011

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Moreover, the energy consumed in transmitting over large distance is proportional to distance (Heinzelman et al 2000). Thus, in order to avoid direct transmission to BS, many energy efficient routing algorithms based on multi-hop routing paradigm have been proposed (Uchida et al 2010;Hiyama et al 2010;Nose et al 2010). Furthermore, for reducing the in-network sensory traffic, several data reduction techniques such as packet merging, data aggregation and compression have been proposed for WSNs.…”

Section: Introductionmentioning

confidence: 99%

A methodology to optimize query in wireless sensor networks using historical data

Malik

Roy

2011

J Ambient Intell Human Comput

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In wireless sensor networks (WSN), a query is commonly used for collecting periodical data from the objects under monitoring. Amount of sensory data drawn across WSNs by a query can significantly impact WSN's power consumption and its lifetime, since WSNs are battery operated. We present a novel methodology to construct an optimal query containing fewer sensory attributes as compared to a standard query, thereby reducing the sensory traffic in WSN. Our methodology employees a statistical technique, principal component analysis on historical traces of sensory data to automatically identify important attributes among the correlated ones. The optimal query containing reduced set of sensory attributes, guarantees at least 25% reduction in energy consumption of WSN with respect to a standard query. Furthermore, from reduced set of data reported by optimal query, the methodology synthesizes complete set of sensory data at a base station (reporting unit with surplus power supply). We validated the effectiveness of our methodology with real world sensor data. The result shows that our methodology can synthesize complete set of sensory data analogues to standard query with 93% accuracy.

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Bi-level programming model of truck congestion pricing at container terminals

Zhang

Chen

2017

J Ambient Intell Human Comput

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Performance analysis of multi-hop ad-hoc network using multi-flow traffic for indoor scenarios

Cited by 6 publications

References 19 publications

Multi-scale query processing in vehicular networks

Multi-scale query processing in vehicular networks

A methodology to optimize query in wireless sensor networks using historical data

Bi-level programming model of truck congestion pricing at container terminals

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