Geographical region summary service for geographical routing

Hsiao, Pai-Hsiang

doi:10.1145/501416.501455

Cited by 10 publications

(33 citation statements)

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“…Longer paths raises the possibility of packet losses as it raises the network load. Note that our experience is different from the performance reported in [16], where the author of the protocol used very short simulation run and very low data load by way of using short-lived data sources.…”

Section: Performance Comparison With Other Location Dissemination Procontrasting

confidence: 57%

“…In GRSS [16], the geographic area of the network is divided into a grid with small squares (called order-0 squares). Four adjacent order squares form an order · ½ square, thus generating a hierarchy of squares of different orders and sizes.…”

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

“…The author of the protocol has used GRSS to perform geographic forwarding [16]. The available location information is used to make forwarding decisions at each intermediate node, and the packet is forwarded to the neighbor closest to the destination.…”

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

“…While the original GRSS paper [16] The size of the lower most square, (iii) Summary Period (SP): Time interval to send out summaries. More words about parameter setting will come later.…”

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

“…There are several protocols that use more active mechanisms. Broadly, they can be classified into two types -(i) location database systems that use some form of query-reply mechanism, such as SLURP [29] and GLS [23], (ii) location dissemination systems, that actively disseminate location information in the network, such as GRSS [16] and DREAM location service [2,9].…”

Section: Introductionmentioning

confidence: 99%

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Performance of dead reckoning‐based location service for mobile ad hoc networks

Kumar

Das

2004

Wireless Communications

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A predictive model-based mobility tracking method, called dead reckoning, is developed for mobile ad hoc networks. It disseminates both location and movement models of mobile nodes in the network so that every node is able to predict or track the movement of every other node with a very low overhead.The basic technique is optimized to use "distance effect," where distant nodes maintain less accurate tracking information to save overheads. The dead reckoning-based location service mechanism is evaluated against three known location dissemination service protocols: Simple, DREAM and GRSS. The evaluation is done with geographic routing as an application. It is observed that dead reckoning significantly outperforms the other protocols in terms of packet delivery fraction. It also maintains low control overhead. Its packet delivery performance is only marginally impacted by increasing speed or noise in the mobility model, that affects its predictive ability.

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Section: Performance Comparison With Other Location Dissemination Procontrasting

confidence: 57%

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

“…While the original GRSS paper [16] The size of the lower most square, (iii) Summary Period (SP): Time interval to send out summaries. More words about parameter setting will come later.…”

Section: Geographic Region Summary Service (Grss)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance of dead reckoning‐based location service for mobile ad hoc networks

Kumar

Das

2004

Wireless Communications

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Octopus: A fault-tolerant and efficient ad-hoc routing protocol

2007

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Mobile ad-hoc networks (MANETs) are failureprone environments; it is common for mobile wireless nodes to intermittently disconnect from the network, e.g., due to signal blockage. This paper focuses on withstanding such failures in large MANETs: we present Octopus, a faulttolerant and efficient position-based routing protocol. Faulttolerance is achieved by employing redundancy, i.e., storing the location of each node at many other nodes, and by keeping frequently refreshed soft state. At the same time, Octopus achieves a low location update overhead by employing a novel aggregation technique, whereby a single packet updates the location of many nodes at many other nodes. Octopus is highly scalable: for a fixed node density, the number of location update packets sent does not grow with the network size. And when the density increases, the overhead drops. Thorough empirical evaluation using the ns2 simulator with up to 675 mobile nodes shows that Octopus achieves excellent fault-tolerance at a modest overhead: when all nodes intermittently disconnect and reconnect, Octopus achieves the same high reliability as when all nodes are constantly up.

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Receiver-oriented design of Bloom filters for data-centric routing

Guo

Yang

2010

Computer Networks

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Geographical region summary service for geographical routing

Cited by 10 publications

References 0 publications

Performance of dead reckoning‐based location service for mobile ad hoc networks

Performance of dead reckoning‐based location service for mobile ad hoc networks

Octopus: A fault-tolerant and efficient ad-hoc routing protocol

Receiver-oriented design of Bloom filters for data-centric routing

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