Gossiping using the energy map in wireless sensor networks

Machado, Max do Val; Mini, Raquel A. F.; Loureiro, Antônio A. F.; Guidoni, Daniel L.; Melo, Pedro O. S. Vaz de

doi:10.1145/1298126.1298189

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In this way, the constraint is defined as:

γ = \frac{1}{false| Ω_{i t}^{a} false|},

where

false| {normalΩ}_{i t}^{a} false|

counts the number of nodes i in a given solution

{normalΩ}_{i t}^{a}

. Energy constraint:This restriction attempts to prioritize solutions with lower energy consumption. In order to measure energy consumption, the nodes' energy coefficient, as defined in the study of Val Machado et al, is considered. This is expressed as

C o e = e_{i} + \frac{1}{n e i + 1} true \sum_{j = i}^{n e i} e_{j},

where e is the energy level of the node, and nei is the number of neighbors of the i th node.…”

Section: The Proposed Automatic Provisioning Approachmentioning

confidence: 99%

“…This restriction attempts to prioritize solutions with lower energy consumption. In order to measure energy consumption, the nodes' energy coefficient, as defined in the study of Val Machado et al, is considered. This is expressed as

C o e = e_{i} + \frac{1}{n e i + 1} true \sum_{j = i}^{n e i} e_{j},

where e is the energy level of the node, and nei is the number of neighbors of the i th node.…”

Section: The Proposed Automatic Provisioning Approachmentioning

confidence: 99%

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An approach for provisioning virtual sensors in sensor clouds

et al. 2019

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In sensor clouds environments, the provisioning process is a crucial task since it is responsible for selecting physical sensors that will be used to create virtual sensors. However, most works consider the allocation of all sensors within the region of interest, causing serious problems such as the wasting of energy consumption. The objective of this paper is to present ACxSIM, an automatic approach to the provisioning of virtual sensors. ACxSIM includes two algorithms: adaptive clustering algorithm based on similarity (ACASIM) and ant colony optimization for sensor selection based on similarity (ACOSIM).ACASIM first clusters the sensor nodes based on the similarity of its measurements (exploiting the temporal and spatial correlations between them), which may create clusters with nodes not physically close to each other. Therefore, in ACASIM, a cluster represents different geographical areas whose nodes have correlated measurements (according to a defined error threshold). Later, ACOSIM, based on ant colony optimization algorithm, creates virtual sensors by selecting only a subset of nodes from each cluster. In this way, the overall energy consumption of sensor nodes is reduced, prolonging the lifetime of the sensor cloud. Results from experiments in Intel Lab dataset show that the ACxSIM reduces energy consumption by 73.97%, providing a solution to be considered in sensor cloud scenarios.Int J Network Mgmt. 2019;29:e2062.wileyonlinelibrary.com/journal/nem

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“…In this way, the constraint is defined as:

γ = \frac{1}{false| Ω_{i t}^{a} false|},

where

false| {normalΩ}_{i t}^{a} false|

counts the number of nodes i in a given solution

{normalΩ}_{i t}^{a}

C o e = e_{i} + \frac{1}{n e i + 1} true \sum_{j = i}^{n e i} e_{j},

where e is the energy level of the node, and nei is the number of neighbors of the i th node.…”

Section: The Proposed Automatic Provisioning Approachmentioning

confidence: 99%

C o e = e_{i} + \frac{1}{n e i + 1} true \sum_{j = i}^{n e i} e_{j},

where e is the energy level of the node, and nei is the number of neighbors of the i th node.…”

Section: The Proposed Automatic Provisioning Approachmentioning

confidence: 99%

An approach for provisioning virtual sensors in sensor clouds

et al. 2019

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“…However, the use of the same network wide rebroadcast probability for all nodes is not efficient, and the rebroadcast redundancy is high. Some improvements have been proposed to determine the rebroadcast probability based on the density of local neighbors 7–9 or based on the number of received message copies 3, 10 to reduce rebroadcast redundancy and increase broadcast coverage; and some other improvements include using energy‐aware gossiping probability for network lifetime maximization 11 or employing local reception confirmation message for high broadcast reliability 12, 13. These variations normally require the exchange of neighbor information.…”

Section: Related Workmentioning

confidence: 99%

Broadcast based on layered diffusion in wireless ad hoc and sensor networks

Wang¹,

Lim²,

Ma³

2011

Wireless Communications

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Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors.

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