Probabilistic flooding coverage analysis for efficient information dissemination in wireless networks

Koufoudakis, George; Οικονόμου, Κωνσταντίνος; Giannakis, Konstantinos; Aı̈ssa, Sonia

doi:10.1016/j.comnet.2018.05.005

Cited by 8 publications

(10 citation statements)

References 19 publications

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“…Routing Algorithms. FLOODING [15] and GOSSIPING [16] are two groups of planar routing algorithms in the WirelessHART networks, where the sensor nodes are equally privileged, equipotent participants in the networks. In the FLOODING based routing algorithms, the sensor nodes send the data to all the neighboring nodes via broadcasting, and the neighboring nodes go on forwarding the data to their neighbors.…”

Section: The Planarmentioning

confidence: 99%

Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks

et al. 2019

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The frequent data convergecasting from the sensor nodes to the gateways may cause imbalanced energy consumption in the Wireless Highway Addressable Remote Transducer (WirelessHART) networks, yielding a short network lifetime and frequent failures in the data acquisition. Existing solutions always tend to tradeoff between the hardware cost, routing complexity, and energy consumption, making the sensor nodes suffer from expensive hardware investment, overloaded network computation, or imbalanced energy consumption. In this paper, an Adaptive Freeshape Clustering (AFC) protocol is developed for saving and balancing the energy consumption in the WirelessHART networks. In AFC, the Region of Interest (RoI) is first divided into several fan-shaped clusters. The sensor nodes in each fan-shaped cluster compete for the positions of Cell Node (CN), and the nodes that have succeeded in the competition adjust the radius of their coverages to cover the fan-shaped clusters adaptively with the minimum overlapped areas. In this way, each fan-shaped cluster can be subdivided into several freeshape zones regarding each CN's coverage, and the CN in each cluster takes charge of convergecasting the data to the CH. The simulations show that AFC can prolong the network lifetime by 37% compared with other related schemes, e.g., HEED, FLOODING, and DIRECT, whereas it can reduce the degree of energy imbalance by 1.29%.

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Section: The Planarmentioning

confidence: 99%

Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks

et al. 2019

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“…Over the last decades, probabilistic broadcasting schemes have been implemented using several techniques such as probabilistic density-based schemes [3]- [8], probabilistic counter-based schemes [9][10] and [11], probabilistic GPSbased schemes [12][13] [14], and probabilistic algebraic-based scheme [15]. Several studies show that probabilistic schemes that depends on a GPS device are the appropriate solutions for several communication scenarios in VANETs.…”

Section: Related Workmentioning

confidence: 99%

An Advanced Emergency Warning Message Scheme based on Vehicles Speed and Traffic Densities

Banikhalaf¹,

Alomari²,

Alzboon³

2019

IJACSA

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In intelligent transportation systems, broadcasting Warning Messages (WMs) by Vehicular Ad hoc Networks (VANETs) communication is a significant task. Designing efficient dissemination schemes for fast and reliable delivery of WMs is still an open research question. In this paper, we propose a novel messaging scheme, Advanced Speed and Density Warning Message (ASDWM). ASDWM is a broadcast-based scheme that meets design objectives and achieves high saved rebroadcast and reachability, as well as low end-to-end latency of WM delivery. The ASDWM uses vehicle speeds and vehicles density degrees to help emergency vehicles to send WM according to a road condition, adaptively. Simulation results demonstrate the effectiveness and superiority of the ASDWM over its counterparts.

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“…In [37], suitable values for the threshold probability considering various topologies are derived and, in [38], asymptotic expressions for the case of random graphs are also derived. In [4], probabilistic flooding is studied using algebraic graph theory elements and an algorithm is introduced to estimate the threshold probability. Probabilistic flooding in generalized random graphs is studied in [39], while the case of peer-to-peer is studied in [40,41].…”

Section: Past Related Workmentioning

confidence: 99%

“…Due to its probabilistic nature, global outreach under PF is guaranteed with high probability and not deterministically. A large amount of studies (e.g., [4,36,37,63]) focuses on the particular value of q allowing for global outreach and at the same time the amount of messages kept minimized i.e., threshold probability,q. The benefits obtained by this policy vary according to the value of the forwarding probability.…”

Section: Probabilistic Information Disseminationmentioning

confidence: 99%

“…Consequently, when the nodes belonging to the bottleneck links of the network are not operational, any conventional information dissemination mechanism is unable to proceed and reach all the operational nodes. Considering that the energy collection process may recharge these nodes at a later time, this motivates the revisiting the information dissemination mechanism and in particular Probabilistic Flooding (PF) [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Koufoudakis

Οικονόμου

Tsoumanis

2018

JSAN

Self Cite

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Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.

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Probabilistic flooding coverage analysis for efficient information dissemination in wireless networks

Cited by 8 publications

References 19 publications

Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks

Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks

An Advanced Emergency Warning Message Scheme based on Vehicles Speed and Traffic Densities

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

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