An energy-efficient overlapping clustering protocol in WSNs

Yuan, Hu; Niu, Yugang

doi:10.1007/s11276-016-1434-5

Cited by 17 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Energy consumption and sensor coverage are important measures for improving the lifespan and the use of WSNs. Yuan Hu et al 34 suggested an energy-efficient overlapping clustering (EEOC) protocol for WSNs. Based upon the overlapping process of cluster creation, the identified cluster heads (CHs) were distributed uniformly, resulting in low energy consumption throughout the communication phase in intra-cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the increasing importance of route selection for mobile data collectors, much effort has been made to achieve optimal route selection in delay-sensitive applications. [17][18][19]31,34,35,[37][38][39][40][41][42][43][44][45] The mobility approach based on the network coverage mechanism is optimal to manage the transmission loads between sensor nodes. However, an optimal route formation to reduce latency and preserve energy in data gathering is still an unresolved issue.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Identification of optimal location points for efficient data gathering in IoT environment

Wala

Chand

Sharma

2021

Int J Communication

View full text Add to dashboard Cite

Recent development in sensor technologies encourages the adoption of mobile data collectors (MDCs), that is, vehicles or robots, to collect data in the Internet of Things (IoT) environment. However, the slow travel speed of the MDCs leads to significant delays in the transmission of data. This work proposes an optimal region location point based route planning (ORLP-RP) scheme to construct a shorter tour with minimized data delivery latency. Primary goal of the work is to distinguish the overlapped wireless sensor network (WSN) clusters and specify the overlapped regions to find optimal location points within each identified region to ensure one-hop data gathering from each cluster. Thereafter, MDC trajectory is formed by using the nearest-neighbor (NN) heuristic approach that generates a near optimal solution. In performance evaluation, the ORLP-RP scheme has been compared with the existing algorithms such as EAPC, RDP, MOPSO, and NDCMC for data collection in terms of the number of optimal location points, total path length, data gathering latency, network lifespan, energy consumption, and data gathering ratio. Simulation results demonstrate the effectiveness of the proposed approach by improving the number of optimal location points with an average up to 58%, total path length improvement by 17%, energy consumption and network lifespan improvement on average by 18% and 12%, data gathering ratio by the MDC up to 9%, and reducing the delay of the MDC path by 21% compared with considered approaches. Thus, simulation results indicate better performance of the proposed scheme at all the indices listed above.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Identification of optimal location points for efficient data gathering in IoT environment

Wala

Chand

Sharma

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Cluster head election based on Bollinger Band has been proposed in [48]. In [18], an energy-efficient overlapping clustering protocol is proposed where the boundary nodes in the overlapping area are responsible to relay the aggregated data to the static sink. Prasad et.al [39] use hybrid differential evolution along with multi objective bee swarm optimisation for clustering.…”

Section: Related Studymentioning

confidence: 99%

“…if (i, j) ∈ tour done by ant a 0 otherwise (18) Thus, based on the above policy of ACO as given by eqns. (16), (17) and 18, the mobile collector moves throughout the network and collects data from the CHs in a single-hop fashion.…”

Section: Mobile Collector Movement Strategymentioning

confidence: 99%

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

2017

View full text Add to dashboard Cite

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been

show abstract

“…The hierarchical clustering routing protocol in RWSN supports the mobility of sensor nodes and CHs. The whole protocol is divided into two phases: the establishment phase and the steady-state phase [15]. After the sensor nodes are deployed, they are divided into logical clusters.…”

Section: Introductionmentioning

confidence: 99%

New Approach of Energy-Efficient Hierarchical Clustering Based on Neighbor Rotation for RWSN

Chen

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Energy Efficient Hierarchical Clustering (EEHC) method is a distributed random clustering algorithm for residential wireless sensor networks (RWSNs) with the goal of maximizing network lifetime. However, during selection of the cluster head stage, a node declares itself as a cluster head to neighboring nodes with probability p, which does not consider well the topological characteristics of the network. The new energy-efficient hierarchical clustering approach based on neighbor rotation (EEHCN) for RWSN has been proposed in this paper, the new idea of which determines whether a node of RWSN can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. The experimental results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime of RWSN. The innovative research work is very useful for the relative application of the RWSN, such as smart home or smart community.

show abstract

An energy-efficient overlapping clustering protocol in WSNs

Cited by 17 publications

References 32 publications

Identification of optimal location points for efficient data gathering in IoT environment

Identification of optimal location points for efficient data gathering in IoT environment

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

New Approach of Energy-Efficient Hierarchical Clustering Based on Neighbor Rotation for RWSN

Contact Info

Product

Resources

About