A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

Mostafavi, Seyedakbar; Hakami, Vesal

doi:10.1002/dac.4313

Cited by 21 publications

(13 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These types of protocols require less memory for route discovery and routing and due to their on-demand routing nature, impose less overhead compared to others. While because of discarding their unneeded paths, for any new destination, route discovery has to run which causes more delay [10][11][12][13].…”

Section: A Reactive (On-demand) Routing Protocolsmentioning

confidence: 99%

See 1 more Smart Citation

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

Mostafavi

Hakami

Paydar

2020

JOIV : Int. J. Inform. Visualization

Self Cite

View full text Add to dashboard Cite

In Mobile Ad Hoc Networks (MANETs), lack of a fixed infrastructure, dynamic network topology, device mobility and data communication over wireless channels make the multi-hop routing a very challenging task. Due to mission-critical applications of MANET, dealing with these challenges through the design of a Quality of Service (QoS)-assured protocol is a substantial problem. Mobility in MANETs is commonly considered as a negative factor on quality, although we suggest that the right approach to mobility awareness using wisely selected metrics can lead to a robust and QoS-assured protocol. In this paper, we propose QMAR-AODV, a QoS-assured Mobility-Aware Routing protocol which is an optimized version of AODV protocol. We utilize a combination of stability and quality metrics including Mobility Ratio (MR(C,E)) between nodes in a route, Energy Efficiency and congestion load to choose the most stable and QoS-assured routes. Our simulation results show that QMAR-AODV protocol outperforms E2E-LREEMR and reduces route instability, end-to-end delay, data retransmissions and packet loss by 8.3% 10.9% 10.6% and 5.4 respectively, while increases data reception and network throughput by 5.1% and 4.8% respectively, compared to E2E-LREEMR routing protocol.

show abstract

Section: A Reactive (On-demand) Routing Protocolsmentioning

confidence: 99%

“…In MANETs, it refers to hop counts of a route such that more hops result in the increase of end-to-end delay otherwise, less delay is resulted. End-to-end delay of the j th route is (13) Where HC refers to the hop counts of the j th route and in AODV, it is available for each path.…”

Section: Fig 5 Nodes' Relational Mobility (Mr) Calculationmentioning

confidence: 99%

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

Mostafavi

Hakami

Paydar

2020

JOIV : Int. J. Inform. Visualization

Self Cite

View full text Add to dashboard Cite

show abstract

“…The assumption made on BS is that it has no constraints on energy resources. A hierarchical and distance-based clustering technique is proposed in [9] which utilizes a new rank-order distance measure for agglomerative hierarchical clustering. Here, authors generate a rank order list by sorting all other sensor nodes in near neighbors by absolute distance.…”

Section: Energy Based Clustering Approachmentioning

confidence: 99%

Cluster Centroid-Based Energy Efficient Routing Protocol for WSN-Assisted IoT

Kumar¹,

Bala²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Wireless sensor network is highly resource constrained, where energy efficiency and network lifetime plays a major role for its sustenance. As the sensor nodes are battery operated and deployed in hostile environments, either recharging or replacement of batteries in sensor nodes is not possible after its deployment in inaccessible areas. In such condition, energy is the vital factor for the survival of sensor node in the sensing field. In order to increase the network lifetime and balance the energy consumption, robust routing protocols are required. The proposed network routing has three phases: 1. Network initiation phase to create a zone which enables the communication among local nodes 2. Zone coordinator selection phase algorithm to form zone cluster and its re-election procedure and 3. Zone head selection with its replacement phase based on energy centroid positional information and distance to the basestation to distribute load equally among zone coordinators , local sensor nodes. The data path between zone heads and basestation is distance centric and is optimized at one hop and dual hop levels to avoid data packet loss at zoneheads. Each zone is designed to own atmost ¼ rth of deployed sensor node count through uniform random deployment. Simulations results when basestation is placed inside sensing field indicates that the proposed network algorithm outperforms when benchmarked against similar protocols like conventional LEACH, Traditional PEGASIS, existing PRRP, ES3 protocols in terms of performance metrics like Network energy consumption, Average energy consumed by sensor node, Packet delivery ratio, Packet loss percentage and Network throughput.

show abstract

“…6 Clustering is the predominant and sufficiently simple strategy to perceive energy stability in the network. 7 The strategy of clustering essentially focuses on partitioning the whole collection of deployed sensor nodes into distinct groups for establishing sustainable communication between the BS and the constructed groups termed clusters. The communication between the clusters and BS is attained through the group leader designated as CH.…”

Section: Introductionmentioning

confidence: 99%

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Balamurugan

Janakiraman

Priya

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are capable of offering data dissemination among the nodes such that the exploration of a network's potential could be performed based on the frequency range. It is highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issues of network lifetime, coverage, scheduling and data aggregation. Prolonging network lifetime confirms energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation.Clustering schemes are highly suitable for effectively utilizing the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. This problem of energy-aware optimization included in the clustering process is an NP optimization problem. At this juncture, metaheuristic optimization algorithms are potential candidates for optimizing energy that attributes towards predominant sustenance in network lifetime. In this article, a modified African buffalo and group teaching optimization algorithm (MABGTOA) is proposed for achieving energy stability and maintaining network lifetime by efficient cluster head (CH) selection during the process of clustering. This MABGTOA scheme is developed for sustaining the tradeoff existing between the rate of exploitation and exploration that aids in efficient selection of CHs, thus maintaining lifetime and energy stability in the network. The simulation experiments of the proposed MABGTOA confirm its predominance by offering increased throughput by 18.21% and sustenance in residual energy by 15.48% when compared to the benchmarked schemes taken for comparative investigation.

show abstract

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

Cited by 21 publications

References 54 publications

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

Cluster Centroid-Based Energy Efficient Routing Protocol for WSN-Assisted IoT

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Contact Info

Product

Resources

About