scholarly journals An Efficient Mobile Data Gathering Method with Tree Clustering Algorithm in Wireless Sensor Networks Balanced and Unbalanced Topologies

Author(s):  
Meriem MEDDAH ◽  
Rim HADDAD ◽  
Tahar EZZEDDINE

Abstract Mobile Data Collector device (MDC) is adopted to reduce the energy consumption in Wireless Sensor Networks. This device travels the network in order to gather the collected data from sensor nodes. This paper presents a new Tree Clustering algorithm with Mobile Data Collector in Wireless Sensor Networks, which establishes the shortest travelling path passing throw a subset of Cluster Heads (CH). To select CHs, we adopt a competitive scheme, and the best sensor nodes are elected according to the number of packets forwarded between sensor nodes, the number of hops to the tree’s root, the residual energy, and the distance between the node and the closest CH. In simulation results, we adopt the balanced and unbalanced topologies and prove the efficiently of our proposed algorithm considering the network lifetime, the fairness index and the energy consumption in comparison with the existing mobile data collection algorithms.

2015 ◽  
Vol 785 ◽  
pp. 744-750
Author(s):  
Lei Gao ◽  
Qun Chen

In order to solve the energy limited problem of sensor nodes in the wireless sensor networks (WSN), a fast clustering algorithm based on energy efficiency for wire1ess sensor networks is presented in this paper. In the system initialization phase, the deployment region is divided into several clusters rapidly. The energy consumption ratio and degree of the node are chosen as the selection criterion for the cluster head. Re-election of the cluster head node at this time became a local trigger behavior. Because of the range of the re-election is within the cluster, which greatly reduces the complexity and computational load to re-elect the cluster head node. Theoretical analysis indicates that the timing complexity of the clustering algorithm is O(1), which shows that the algorithm overhead is small and has nothing to do with the network size n. Simulation results show that clustering algorithm based on energy efficiency can provide better load balancing of cluster heads and less protocol overhead. Clustering algorithm based on energy efficiency can reduce energy consumption and prolong the network lifetime compared with LEACH protocol.


Author(s):  
Femi A. Aderohunmu ◽  
Jeremiah D. Deng ◽  
Martin Purvis

While wireless sensor networks (WSN) are increasingly equipped to handle more complex functions, in-network processing still requires the battery-powered sensors to judiciously use their constrained energy so as to prolong the elective network life time. There are a few protocols using sensor clusters to coordinate the energy consumption in a WSN, but how to deal with energy heterogeneity remains a research question. The authors propose a modified clustering algorithm with a three-tier energy setting, where energy consumption among sensor nodes is adaptive to their energy levels. A theoretical analysis shows that the proposed modifications result in an extended network stability period. Simulation has been conducted to evaluate the new clustering algorithm against some existing algorithms under different energy heterogeneity settings, and favourable results are obtained especially when the energy levels are significantly imbalanced.


2011 ◽  
Vol 474-476 ◽  
pp. 1221-1227
Author(s):  
Ying Liao ◽  
Wei Xu Hao

Wireless sensor networks (WSNs) detect and monitor the outside physical state by the sensor nodes organizing automatically. Utilizing clustering algorithm to form hierarchical network topology is the common method which implements managing network and aggregating data in WSNs. Different from the previous clustering algorithms, this article proposes a clustering algorithm for WSNs based on distance and distribution to generate clusters considering residual energy of nods in WSNs with inhomogeneous distribution. The simulation result indicates that the algorithm can establish more balanceable clustering structure effectively and enhance the network life cycle obviously.<b></b>


2012 ◽  
Vol 6-7 ◽  
pp. 831-835
Author(s):  
Chang Lin Ma ◽  
Yuan Ruan

In order to improve the lifetime and throughput of wireless sensor networks under the limited power, an improved clustering algorithm is proposed in this paper on the basis of LEACH protocol. The energy factor is considered in this algorithm. The residual energy of all sensor nodes is referred to select cluster-heads of wireless sensor networks. The new clustering algorithm effectively improves the energy efficiency, throughput and lifetime of wireless sensor networks. The results are proved by simulations.


2018 ◽  
Vol 17 (1) ◽  
pp. 7111-7119
Author(s):  
Harpinderjeet Kaur Sekhon ◽  
Dr. Mohita

Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. One of the major issues in wireless sensor networks is developing a routing protocol which has a significant impact on the overall lifetime of the sensor network. The network area is divided into same sized small–small regions. Sensor nodes are randomly deployed in each predefined sub-area. Each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes.


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