A fault-tolerant energy-efficient clustering protocol of a wireless sensor network

Karim, Lutful; Nasser, Nidal; Sheltami, Tarek R.

doi:10.1002/wcm.1240

Cited by 35 publications

(23 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use randomly connected Unit Disk Graphs (UDGs) on an area of 100 meters × 100 meters as a network simulation model. We set the number of zones and nodes to 6 and 36, place BS outside and center of all zones at the coordinates (50,115) and (50,53), respectively, and vary the sensor's data collection time between 3 and 12.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…To the best our knowledge, there is no existing work that addresses all these challenges in one framework. Thus, we introduce a routing protocol referred to as the Cluster-based Routing and Topology Control Approach (CRTCA) [47,52,53] for large scale Wireless Sensor Networks (WSNs). We incrementally integrate clustering, routing, and topology control approaches to construct this protocol.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we evaluate the performance of the proposed CRTCA protocol [52,53], and compare with the existing DSC [8], and LEACH [38] protocols through mathematical analysis, and simulation.…”

Section: Performance Evaluation: Crtca Leach and Dscmentioning

confidence: 99%

See 2 more Smart Citations

An integrated framework for wireless sensor network management

Karim

Mahmoud

Khan

2012

Wirel. Commun. Mob. Comput.

Self Cite

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) have significant potential in many application domains, and are poised for growth in many markets ranging from agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large scale commercial adoption of sensor networks are the lack of available network management and control tools for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at exact location, and scheduling data packets so that data are sent based on their priority and fairness. Moreover, due to the limited communication range of sensors, a large geographical area cannot be covered, which limits sensors application domain. Thus, we investigate a scalable and flexible WSN architecture that relies on multi-modal nodes equipped with IEEE 802.15.4 and IEEE 802.11 in order to use a Wi-Fi overlay as a seamless gateway to the Internet through WiMAX networks. We focus on network management approaches such as sensors localization, data scheduling, routing, and data aggregation for the WSN plane of this large scale multimodal network architecture and find that most existing approaches are not scalable, energy efficient, and fault tolerant. Thus, we introduce an efficient approach for each of localization, data scheduling, routing, and data aggregation; and compare the performance of proposed approaches with existing ones in terms of network energy consumptions, localization error, end-to-end data transmission delay and packet delivery ratio. Simulation results, theoretical and statistical analysis show that each of these approaches outperforms the existing approaches. To the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Hence, we efficiently integrate all network management components so that it can be used as a single network management solution for a large scale WSN, perform experimentations to evaluate the performance of the proposed framework, and validate the results through statistical analysis. Experimental results show that our proposed framework outperforms existing approaches in terms of localization energy consumptions, localization accuracy, network energy consumptions and end-to-end data transmission delay.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

An integrated framework for wireless sensor network management

Karim

Mahmoud

Khan

2012

Wirel. Commun. Mob. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The cluster head or CH acts as a bridge between other sensor nodes and base station and sometimes between one cluster head and other cluster head in multi-hop cases [5]. Cluster heads have responsibility of locality preserving, adding or leaving of nodes, scheduling time slots for each node, data aggregation and management of messages between nodes and base station [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

Efficient Fuzzy Logic-Based Clustering Algorithm for Wireless Sensor Networks

Bidaki¹,

Reza

Tabbakh³

et al. 2016

IJGDC

View full text Add to dashboard Cite

show abstract

“…Thereby it is an efficient approach to switch sensor nodes to the sleep state in order to achieve significant energy conservation. As a consequence, how to set up an optimal working/sleep schedule becomes one issue of topology control [11][12][13][14]. In addition, another way to control network topology is to adjust the transmission power of each node in working state, namely power control.…”

Section: Introductionmentioning

confidence: 99%

Transmission Power Control in Wireless Sensor Networks under the Minimum Connected Average Node Degree Constraint

Qiao

Liu

Zheng

2015

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-As a branch of topology control, power control is of great importance to prolong the survival time of the network. A transmission power control algorithm is proposed under the minimum connected average node degree constraint in this paper. Using a Poisson point process in two dimensions, we derive an analytical expression that determines the required transmission range to achieve a specified connectivity probability for a given node density, and then the minimum connected average node degree is obtained. Based on the above results, an algorithm named MCAND is designed to achieve the given connected probability by adjusting the transmission power of each node dynamically, with the node degree equal to the minimum connected average node degree. Simulation results show that the performance of our algorithm is superior to that of the related algorithms based on node degree in terms of coverage and connectivity.

show abstract

A fault-tolerant energy-efficient clustering protocol of a wireless sensor network

Cited by 35 publications

References 14 publications

An integrated framework for wireless sensor network management

An integrated framework for wireless sensor network management

Efficient Fuzzy Logic-Based Clustering Algorithm for Wireless Sensor Networks

Transmission Power Control in Wireless Sensor Networks under the Minimum Connected Average Node Degree Constraint

Contact Info

Product

Resources

About