Geographic random forwarding with hybrid-ARQ for ad hoc networks with rapid sleep cycles

Zhao, Bin; Seshadri, Rohit Iyer; Valenti, Matthew C.

doi:10.1109/glocom.2004.1378912

Cited by 17 publications

(3 citation statements)

References 5 publications

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“…As another forwarder selection approach, locationbased opportunistic routing protocols [20]- [23], which Copyright c 2019 The Institute of Electronics, Information and Communication Engineers forward a packet based on geographical information, have been proposed. However, they require every terminal to have the global positioning system (GPS) function for acquiring positions of the terminals.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Local Scaling Factor Control for Backoff-Based Opportunistic Routing

Yamazaki

Yamamoto

Hosokawa

et al. 2019

IEICE Trans. Inf. & Syst.

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In wireless multi-hop networks such as ad hoc networks and sensor networks, backoff-based opportunistic routing protocols, which make a forwarding decision based on backoff time, have been proposed. In the protocols, each potential forwarder calculates the backoff time based on the product of a weight and global scaling factor. The weight prioritizes potential forwarders and is calculated based on hop counts to the destination of a sender and receiver. The global scaling factor is a predetermined value to map the weight to the actual backoff time. However, there are three common issues derived from the global scaling factor. First, it is necessary to share the predetermined global scaling factor with a centralized manner among all terminals properly for the backoff time calculation. Second, it is almost impossible to change the global scaling factor during the networks are being used. Third, it is difficult to set the global scaling factor to an appropriate value since the value differs among each local surrounding of forwarders. To address the aforementioned issues, this paper proposes a novel decentralized local scaling factor control without relying on a predetermined global scaling factor. The proposed method consists of the following three mechanisms: (1) sender-centric local scaling factor setting mechanism in a decentralized manner instead of the global scaling factor, (2) adaptive scaling factor control mechanism which adapts the local scaling factor to each local surrounding of forwarders, and (3) mitigation mechanism for excessive local scaling factor increases for the local scaling factor convergence. Finally, this paper evaluates the backoff-based opportunistic routing protocol with and without the proposed method using computer simulations.

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Section: Introductionmentioning

confidence: 99%

Decentralized Local Scaling Factor Control for Backoff-Based Opportunistic Routing

Yamazaki

Yamamoto

Hosokawa

et al. 2019

IEICE Trans. Inf. & Syst.

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“…Each packet carries the location of the sender and the destination, so that the prioritization of the candidates nodes is based on location information. Hybrid ARQ-Based Intercluster Geographic Relaying (HARBINGER) [7] combines GeRaF with a hybrid automatic repeat request(ARQ). Coding-Aware Opportunistic Routing Mechanism (CORE) [8] is an integration of localized interflow network coding and opportunistic routing.…”

Section: Introductionmentioning

confidence: 99%

Comparison of traditional and opportunistic multihop routing in wireless networking scalability

Spachos

Song

Hatzinakos

2012

2012 26th Biennial Symposium on Communications (QBSC)

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The effect of the network scalability on the performance of a wireless multihop network is investigated. A discrete event simulator is applied to examine the performance of the network with two classes of routing protocols: traditional vs. opportunistic. It is shown that the opportunistic routing can better utilize network redundancies, as compared to an upper bound of traditional routing based on global route optimization.

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“…Candidates in OR can be prioritized based on hop count [87,78,36], geographic-distance [97,93] (Geo-Distance), Expected Transmission Count (ETX) [29], Expected Any-path Transmission (EAX) [96] and so on. Utilization of hop count, ETX or EAX needs an underlying routing protocol (either reactive or proactive) to gather such information.…”

Section: Routing Metricsmentioning

confidence: 99%

Opportunistic routing in wireless mesh networks

Darehshoorzadeh¹

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Advances in communication and networking technologies are rapidly making ubiquitous network connectivity a reality. In recent years, Wireless Mesh Networks (WMNs) have already become very popular and been receiving an increasing amount of attention by the research community. Basically, a WMN consists of simple mesh routers and mesh clients, where mesh routers form the backbone of WMN. Due to the limited transmission range of the radio, many pairs of nodes in WMN may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such networks is an important issue and it poses great challenges. Opportunistic Routing (OR) has been investigated in recent years as a way to increase the performance of WMNs by exploiting its broadcast nature. In OR, in contrast to traditional routing, instead of pre-selecting a single specic node to be the next-hop as a forwarder for a packet, an ordered set of nodes (referred to as candidates) is selected as the potential next-hop forwarders. Thus, the source can use multiple potential paths to deliver the packets to the destination. More specically, when the current node transmits a packet, all the candidates that successfully receive it will coordinate with each other to determine which one will actually forward it, while the others will simply discard the packet. This dissertation studies the properties, performance, maximum gain, candidate selection algorithms and multicast delivery issues about Opportunistic Routing in WMNs. Firstly, we focus on the performance analysis of OR by proposing a Discrete Time Markov Chain (DTMC). This model can be used to evaluate OR in terms of expected number of transmissions from the source to the destination. Secondly, we apply our Markov model to compare relevant candidate selection algorithms that have been proposed in the literature. They range from non-optimum, but simple, to optimum, but with a high computational cost. Thirdly, the set of candidates which a node uses and priority order of them have a signicant impact on the performance of OR. Therefore, using a good metric and algorithm to select and order the candidates are key factors in designing an OR protocol. As the next contribution we propose a new metric that measures the expected distance progress of sending a packet using a set of candidates. Based on this metric we propose a candidate selection algorithm which its performance is very close to the optimum algorithm although our algorithm runs much faster. Fourthly, we have investigated the maximum gain that can be obtained using OR. We have obtained some equations that yield the distances of the candidates in OR such that the per transmission progress towards the destination is maximized. Based on these equations we have proposed a novel candidate selection algorithm. Our new algorithm only needs the geographical location of nodes. The performance of our proposal is very close to the optimum candidate selection algorithm although our algorithm runs much faster. Finally, using OR to support multicast is an other issue that we have investigated in this thesis. We do so by proposing a new multicast protocol which uses OR. Unlike traditional multicast protocols, there is no designated next-hop forwarder for each destination in our protocol, thus the delivery ratio is maximized by taking advantage of spatial diversity.

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Geographic random forwarding with hybrid-ARQ for ad hoc networks with rapid sleep cycles

Cited by 17 publications

References 5 publications

Decentralized Local Scaling Factor Control for Backoff-Based Opportunistic Routing

Decentralized Local Scaling Factor Control for Backoff-Based Opportunistic Routing

Comparison of traditional and opportunistic multihop routing in wireless networking scalability

Opportunistic routing in wireless mesh networks

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