Random network coding for wireless ad-hoc networks

Stoian, Rodica; Perişoară, Lucian Andrei; Stoica, Radu

doi:10.1109/isscs.2009.5206142

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…In opportunistic networking, there are no strict routing rules associated with a multihop network and so whoever gets a chance, can forward the packet[6] [7].…”

Section: Figure 7 Cope Examplementioning

confidence: 99%

“…In this case, a node can XOR the m packets, if each of the next hop has m-1 packets. This defines that each next hop has m-1 packets and so each node will be able to decode m th packet [6] [7].…”

Section: Figure 7 Cope Examplementioning

confidence: 99%

“…COPE uses this information to predicate whether a node has a specific packet or not. Delivery rates are calculated and broadcasted frequently to all the nodes [7].…”

Section: Learning Neighboring Statementioning

confidence: 99%

“…Lets take an another example which is based on taking coding decision based on delivery rates rather than of reception report [7]. Figure 8 shows an example where nodes S1, S2 and S3 have next hops d1, d2 and d3 respectively via a relay node r. lets say delivery probability of each of the links is 1.…”

Section: Learning Neighboring Statementioning

confidence: 99%

“…Figure 8 shows an example where nodes S1, S2 and S3 have next hops d1, d2 and d3 respectively via a relay node r. lets say delivery probability of each of the links is 1. But overhearing probability of each of the links is 0.8. in COPE the threshold of overhearing probability is also 0.8 [7].…”

Section: Learning Neighboring Statementioning

confidence: 99%

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Exploration of Various Network Coding Schemes to Improve Performance in Wireless Multihop Networks

Bajeja¹,

Gosai²

2016

IJCA

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Network coding is a collection of techniques in which a node is allowed to combine, encode and send one or more packets as a single encoded packet instead of sending individually. It improves throughput, packet delivery ratio and efficiency of the system. The inherent broadcasting nature of wireless mediums helps us to adopt network coding very easily. At the same time, as it reduces the number of transmissions, it reduces the probability of various losses due to transmission impairments, channel contentions and route failures. This paper discusses various well known network coding schemes.. Network coding is broadly classified into global and local network coding. Network coding is also classified into binary -Xor based and random linear coding based network coding. Further to these, based upon the applications, Network coding schemes are classified for unicast, multicast and broadcast applications. In each of these categories, network coding is explained in the reference of inter-session network coding and intra-session network coding.

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“…In opportunistic networking, there are no strict routing rules associated with a multihop network and so whoever gets a chance, can forward the packet[6] [7].…”

Section: Figure 7 Cope Examplementioning

confidence: 99%

Section: Figure 7 Cope Examplementioning

confidence: 99%

“…COPE uses this information to predicate whether a node has a specific packet or not. Delivery rates are calculated and broadcasted frequently to all the nodes [7].…”

Section: Learning Neighboring Statementioning

confidence: 99%

Section: Learning Neighboring Statementioning

confidence: 99%

Section: Learning Neighboring Statementioning

confidence: 99%

See 3 more Smart Citations

Exploration of Various Network Coding Schemes to Improve Performance in Wireless Multihop Networks

Bajeja¹,

Gosai²

2016

IJCA

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In-silico generation of random bit streams

Caccia

Malinverno

Paolucci

et al. 2020

Nuclear Instruments and Methods in Physics Research Section A:

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Design and Analysis of a Persistent, Efficient, and Self-Contained WSN Data Collection System

et al. 2019

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Wireless sensor networks are usually deployed in harsh and emergency scenarios, such as floods, fires, or earthquakes, where human participation to monitor and collect environmental data may be too dangerous. It can be also used for healthcare in extreme and remote environments. In such an environment, sensor nodes are faced with the risk of failure and the loss of valuable healthcare data. Therefore, fast collection and reliable storage of data becomes the two important basic topics for reliable data collection. Traditional distributed data collection protocols based on the network, such as Growth Codes proposed by Karma et al., have improved the persistence of data and the efficiency of reliable data collection in disaster scenarios. However, there are still some problems that reduce the overall efficiency. In this paper, we analyze the factors that affect the collection efficiency from a new perspective, the ratio of redundant symbols. Random feedback digestion (RFDG) model is proposed to digest the redundant symbols, similiar to our stomach digesting food, to remove redundant symbols and reduce resource consumption by using the feedback information of the already decoded code words sent by the sink node. This model can increase the valid information ratio in the network and finally increase data decoding efficiency. Three protocols are proposed in this paper according to different feedback mechanisms based on RFDG. It is shown that protocols based on RFDG outperform the growth codes protocol in data collection efficiency and reduce the delayed effect. INDEX TERMS Wireless sensor network, network coding, growth codes, data collection, feedback digestion.

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Random network coding for wireless ad-hoc networks

Cited by 6 publications

References 11 publications

Exploration of Various Network Coding Schemes to Improve Performance in Wireless Multihop Networks

Exploration of Various Network Coding Schemes to Improve Performance in Wireless Multihop Networks

In-silico generation of random bit streams

Design and Analysis of a Persistent, Efficient, and Self-Contained WSN Data Collection System

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