scholarly journals Mobile Base Station and Clustering to Maximize Network Lifetime in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Oday Jerew ◽  
Kim Blackmore ◽  
Weifa Liang
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Packet Loss ◽  
Network Size ◽  
Base Station ◽  
Sensor Nodes ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Mobile Base Station ◽  
Mobile Base

Using a mobile base station (BS) in a wireless sensor network can alleviate nonuniform energy consumption among sensor nodes and accommodate partitioned networks. In the work of Jerew and Liang (2009) we have proposed a novel clustering-based heuristic algorithm for finding a trajectory of the mobile BS that strikes a nontrivial tradeoff between the traffic load among sensor nodes and the tour time constraint of the mobile BS. In this paper, we first show how to choose the number of clusters to ensure there is no packet loss as the BS moves between clusters. We then provide an analytical solution to the problem in terms of the speed of the mobile BS. We also provide analytical estimates of the unavoidable packet loss as the network size increases. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm. The results show that the use of clustering in conjunction with a mobile BS for data gathering can significantly prolong network lifetime and balance energy consumption of sensor nodes.

scholarly journals Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)

2022 ◽  
Vol 12 (1) ◽  
pp. 1011
Author(s):  
Mohammed Réda El Ouadi ◽  
Abderrahim Hasbi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Rapid Development ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Main Element

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.

scholarly journals Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network

2020 ◽  
Vol 10 (4) ◽  
pp. 4176
Author(s):  
Sridhar R. ◽  
N. Guruprasad
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Packet Loss ◽  
Energy Efficient ◽  
Data Gathering ◽  
Data Packet ◽  
Sensor Nodes ◽  
Wireless Sensor

A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods.

Optimized Lifetime of Wireless Sensor Networks Through Metaheuristic Firefly Based Cluster Head Selection Algorithm

2021 ◽  
Author(s):  
Anusha Chintam ◽  
Madhusudhana Rao T.v ◽  
Rajendra Kumar G
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Ad Hoc ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Fitness Value

Abstract A wireless sensor network is a type of wireless ad-hoc networks, which is a collection of individual sensor nodes that are battery-operated devices and connected through ad-hoc and self-configuring connectivity. Therefore, the energy-saving of sensor node is a challenging design issue. Hence, the lifetime of a node is decreased. To enhance the network lifetime and optimal energy consumption, clustering is one of the best methods in WSN. While message transmission there is more distance between the cluster head and base station then more energy drained by the cluster head compare to the remaining sensor nodes in a particular cluster and if the energy consumption is more then automatically the network lifetime decreased. Therefore, this paper proposed an optimal metaheuristic firefly based cluster head selection protocol (FCH) by finding fitness value for selecting the best cluster head. This best-elected cluster head drains less energy as well as increase the network lifetime. In addition to the proposed FCH compared with two basic sensor networks algorithms low energy adaptive clustering hierarchy (LEACH) and Data transmission (DT). The FCH algorithm achieved better results than compared algorithms in terms of dead nodes, remaining energy, and alive nodes of the network.

scholarly journals System Lifetime and Performance Improvement in Wireless Sensor Networks by having Improved Energy Distributed Unequal Clustering Protocol

2021 ◽  
Author(s):  
Ashok T ◽  
Prabakaran R
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Clustering Algorithms ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Cluster Heads

Abstract Wireless Sensor Network (WSN) is becoming a very important area of research in today’s world and contributes a lot in the field of technology. Reducing energy consumption and improving the network lifetime is the key factor to be considered.Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. Also, it comprises various sensor nodes to detect different parameters. Among those non-replaceable batteries plays a greater part. Hence the system with such networks is essential that the sensor nodes consume as little energy as possible.To address the problem, we propose anovel model namely enhanced energy distributed unequal clustering which is mainly utilized for tackling energy consumption issues in multi-hop remote sensor systems. In the proposed method with an area of base station and energy are given significance as clustering parameters. Because of these parameters, diverse nodes are assigned. Here, another methodology has been proposed to enhance the working of EDUC, by electing cluster heads considering several nodes in the neighborhood. The incorporation of the area data for calculation of the opposition radii gives better adjusting of energy in correlation with the current methodology. The technique utilized is of holding similar bunches for a couple of rounds and is successful in decreasing the clustering overhead. The execution of the proposed convention has been assessed under three distinct scenarios and contrasted and existing conventions through reenactments. The outcomes demonstrate that the proposed plan beats the current conventions regarding system lifetime and performances in all the scenarios in terms of delay, energy consumption, packet loss ratio, and packet received ratio.

scholarly journals CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Yali Yuan ◽  
Caihong Li ◽  
Yi Yang ◽  
Xiangliang Zhang ◽  
Lian Li
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fuzzy Approach

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria.

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