Topology control for predictable delay-tolerant networks based on probability

Chen, Hongsheng; Shi, Ke

doi:10.1016/j.adhoc.2014.08.014

Cited by 11 publications

(11 citation statements)

References 32 publications

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“…To model the scenarios stated above, one may utilize space‐time graph model−a directed graph that captures both the space and time dimensions of the network topology. This graph model has been employed in Huang et al, Li et al, and Chen and Shi for controlling and designing a reliable topology for time‐evolving and predictable DTNs. In Merugu et al authors studied the routing problem in dynamic networks with the help of this model, whereas in Kolios et al this model has been utilized to account for the evolution of the network in the temporal dimension.…”

Section: Related Workmentioning

confidence: 99%

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On computing the reliability of opportunistic multihop networks with Mobile relays

Khanna

Chaturvedi

Soh

2019

Quality & Reliability Eng

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Opportunistic multihop networks with mobile relays recently have drawn much attention from researchers across the globe due to their wide applications in various challenging environments. However, because of their peculiar intrinsic features like lack of continuous connectivity, network partitioning, highly dynamic behavior, and long delays, it is very arduous to model and effectively capture the temporal variations of such networks with the help of classical graph models. In this work, we utilize an evolving graph to model the dynamic network and propose a matrix‐based algorithm to generate all minimal path sets between every node pair of such network. We show that these time‐stamped‐minimal‐path sets (TS‐MPS) between each given source‐destination node pair can be used, by utilizing the well‐known Sum‐of‐Disjoint Products technique, to generate various reliability metrics of dynamic networks, ie, two‐terminal reliability of dynamic network and its related metrics, ie, two‐terminal reliabilities of the foremost, shortest, and fastest TS‐MPS, and Expected Hop Count. We also introduce and compute a new network performance metric−Expected Slot Count. We use two illustrative examples of dynamic networks, one of four nodes, and the other of five nodes, to show the salient features of our technique to generate TS‐MPS and reliability metrics.

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Section: Related Workmentioning

confidence: 99%

“…In this paper, we use the evolving graph model, described later in section , to represent the node connectivity in dynamic networks. Note that several researchers, eg, in Xuan et al, Huang et al, Li et al, and Chen and Shi, have also utilized the model to deal with routing and topology control related problems of dynamic networks.…”

Section: Introductionmentioning

confidence: 99%

On computing the reliability of opportunistic multihop networks with Mobile relays

Khanna

Chaturvedi

Soh

2019

Quality & Reliability Eng

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“…However, it is not feasible to apply the existing topology control mechanism directly to PDTN because the topology of PDTN changes dynamically over time. As far as we know, only Huang et al [13][14][15][16] and Chen et al [17][18] studied topology control in PDTN. Taking into account temporal characteristics, they modeled PDTN as a directed space-time graph containing spatial and temporal information.…”

Section: Introductionmentioning

confidence: 99%

Contact Ability Based Topology Control for Predictable Delay-tolerant Networks

Chen

2021

Preprint

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In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a Minimum Spanning Tree(MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly.

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“…With every node that participates in routing, the main characteristic of ad-hoc network is the complete lack of pre-existing infrastructure such as routers in wired networks. There are a lot of papers talking about time-evolving and predictable networks [3,4,5], where the positions of nodes and link status are predictable in a long period of time. However, the predictable network has certain limitation due to the preset network structure.…”

Section: Introductionmentioning

confidence: 99%