TASC: topology adaptive spatial clustering for sensor networks

Virrankoski, Reino; Savvidees, A.

doi:10.1109/mahss.2005.1542850

Cited by 45 publications

(35 citation statements)

References 12 publications

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“…In probabilistic bunching conventions, a hub turns into a CH with a specific likelihood. The EEHC [21], HEED [27] and EECS [29], fall in the probabilistic class and PEGASIS [15], and TASC [30] are classified in the deterministic class.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Energy Efficiency Protocols in WSNs

Saini¹,

Aaseri²

2017

IJARCSSE

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A wireless sensor network (WSN) is developing over miscellaneous areas to give new opportunities for the services as well as occasions for the networks. The advancement of micro electro-mechanical systems has made sensor networks a very fascinating area for research. The primary issue in sensor networks is energy optimization. Energyoptimization means how wisely we use energy. Energy efficiency programs are plotted tolower the use energy of members participating. Numerous approaches and algorithms havebeen proposed toeradicate the issue of energy optimisationThis paper explores the competing issues of energy consumptionefficiency in wireless sensor network..In this paper, we review the key coverageoptimization protocols and present open research issues related to energy efficient coverage.Keywords-WSN, PEGASIS, Clustering, BS, CH, LEACH. I. INTRODUCTION 1.1 Wireless Sensor NetworksWireless Sensor Networks composed of numerous tiny electronic devices called nodes which gather the data from their environment nearby and then send the collected data to base station where further analysis and forecasting can be done. The analysed data is used to take managerial resolutions or business decisions. In multiple issues and numerous scenarios the sensor networks have become tool for data analysis. This has given the capability of remotely monitoring of a physical surrounding for a wide and distinct scenarios and different problem context. These tiny nodes are selfregulating which provide ability of distributed computing in the network. With distributing computing the network can be made flexible or ductile to adopt several methods for deployment, security enforcement, and routing and data dissemination.Energy efficiency is an important aspect in WSN as nodes have a finite battery power. The various types of limitations make WSN and different protocols challenging and divergent. WSN has emerged as a completely different technology as compared to standard traditional internet architecture. WSN nodes are very tiny and occupy lower space and weight as compared to traditional networked devices. WSN easily scales to the order of thousands of nodes and requires minimal help from outside to build the network. The latest advancements in wireless have made the smaller and less expensive products which enhance communication speed significantly.All nodes in WSNs have a battery with fixed power which makes energy conservation as basic need while creating architecture of WSN [30]. The objectives in the creation of WSNs have the capability of huge rate of data exchange among nodes in the network and base station and ability of minimizing the energy consumption. We need efficient routing protocols in WSNs to achieve these objectives. Therefore, several routing algorithms have been proposed [23].Cluster Based routing protocols composed of two layer architecture. One layer is responsible for section of cluster head and the other handles routing. A cluster head (CH) is one which is performs collection of data from rest nodes, gathering or pr...

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Section: Literature Reviewmentioning

confidence: 99%

A Review on Energy Efficiency Protocols in WSNs

Saini¹,

Aaseri²

2017

IJARCSSE

View full text Add to dashboard Cite

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“…Here, we review some of the protocols proposed in the literature for each class: PEGASIS [18], DWEHC [21], and TASC [22] in the deterministic class, and EEHC [20], EECS [19], and HEED [8] in the probabilistic class.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to the idea in [21], Topology Adaptive Spatial Clustering (TASC) [22] decomposes large non-uniform networks into smaller locally-uniform clusters. TASC assumes nodes are aware of 2-hop neighborhood information and also that they know the distance to their neighborhood.…”

Section: Related Workmentioning

confidence: 99%

CDEEC: A connectivity Degree-based energy efficient clustering protocol for wireless sensor networks

Moad

Azim

Bouabdallah

et al. 2011

2011 IFIP Wireless Days (WD)

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“…Then centralized and distributed algorithms are developed to select the cluster heads. In [18], authors present a distributed Topology Adaptive Spatial Clustering algorithm that partitions the network into a set of logically isotropic, non-overlapping clusters without prior knowledge of the number of clusters, cluster size and node coordinates. This is achieved by devising a set of weights that encode distance measurements, connectivity and density information within the locality of each node.…”

Section: Related Workmentioning

confidence: 99%

Self-organized togetherness in a crowd group

Zia

Madani

Aslam

et al. 2009

Proceedings of the 7th International Conference on Frontiers of Information Technology

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Self-organization is a computing paradigm in which participating entities proceed to execute a global goal strictly based on local information. In population dynamics, the sense of togetherness (due to social bindings or a common confinement) experienced by a group of individuals (i.e. "crowd group") is an interesting phenomenon to explore in the context of self-organization. Given a mechanism supporting spatial awareness, many settings require individuals belonging to a group, not only, to stay together (togetherness), but also to account for personal goals (dispersion). Self-organization can help individuals within such a group to stay together and having dispersed at the same time, togetherness being the primary requirement. In this paper, we discussed the parameters defining togetherness and dispersion within a spatially aware crowd group. In this context, the factors affecting the interplay between togetherness and dispersion were examined and maximum tolerable limits of dispersion were tailored in diverse settings of collaboration range, number of individuals willing to diverge and density of a crowd group. Simulation results provide insight into the interplay between these parameters, hence resolving operational dependencies.

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TASC: topology adaptive spatial clustering for sensor networks

Cited by 45 publications

References 12 publications

A Review on Energy Efficiency Protocols in WSNs

A Review on Energy Efficiency Protocols in WSNs

CDEEC: A connectivity Degree-based energy efficient clustering protocol for wireless sensor networks

Self-organized togetherness in a crowd group

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