scholarly journals A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2312 ◽  
Author(s):  
Antonio-Jesus Yuste-Delgado ◽  
Juan-Carlos Cuevas-Martinez ◽  
Alicia Triviño-Cabrera
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Base Station ◽  
Wireless Sensor ◽  
P Value ◽  
Distributed Clustering

Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the s k i p value of the network. The s k i p , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.

scholarly journals DHCFL: Dual Head Clustering Based on Fuzzy Logic for Wireless Sensor Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 1049-1054
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Data Transmission ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor

Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios.

AN ENERGY-EFFICIENT ADAPTIVE CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS

2009 ◽  
Vol 06 (02) ◽  
pp. 117-126 ◽  
Author(s):  
FENGJUN SHANG ◽  
MEHRAN ABOLHASAN ◽  
TADEUSZ WYSOCKI
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Average Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Reduce Energy Consumption

In this paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterheads). This paper focuses on reducing the power consumption of wireless sensor networks. We first extend LEACH's stochastic clusterhead selection algorithm by an average energy-based (LEACH-AE) deterministic component to reduce energy consumption. And then an unequal clustering idea is introduced to further reduce energy consumption of clusterheads. Simulation results show that our modified scheme can extend the network life by up to 38% before the first node dies in the network. Through both theoretical analysis and numerical results, it is shown that the proposed algorithm achieves better performance than the existing clustering algorithms such as LEACH, DCHS, LEACH-C.

scholarly journals Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3726 ◽  
Author(s):  
Zhang ◽  
Qi ◽  
Li
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Minimum Weight ◽  
Recovery Phase ◽  
Base Station ◽  
Wireless Sensor ◽  
Fuzzy Membership Function ◽  
Dijkstra Algorithm

Monitoring of marine polluted areas is an emergency task, where efficiency and low-power consumption are challenging for the recovery of marine monitoring equipment. Wireless sensor networks (WSNs) offer the potential for low-energy recovery of marine observation beacons. Reducing and balancing network energy consumption are major problems for this solution. This paper presents an energy-saving clustering algorithm for wireless sensor networks based on k-means algorithm and fuzzy logic system (KFNS). The algorithm is divided into three phases according to the different demands of each recovery phase. In the monitoring phase, a distributed method is used to select boundary nodes to reduce network energy consumption. The cluster routing phase solves the extreme imbalance of energy of nodes for clustering. In the recovery phase, the inter-node weights are obtained based on the fuzzy membership function. The Dijkstra algorithm is used to obtain the minimum weight path from the node to the base station, and the optimal recovery order of the nodes is obtained by using depth-first search (DFS). We compare the proposed algorithm with existing representative methods. Experimental results show that the algorithm has a longer life cycle and a more efficient recovery strategy.

scholarly journals Path Optimization of Mobile Sink Node in Wireless Sensor Network Water Monitoring System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Fan Chao ◽  
Zhiqin He ◽  
Aiping Pang ◽  
Hongbo Zhou ◽  
Junjie Ge
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Mobile Sink ◽  
Base Station ◽  
Water Monitoring ◽  
Wireless Sensor ◽  
Sink Node

In the water area monitoring of the traditional wireless sensor networks (WSNs), the monitoring data are mostly transmitted to the base station through multihop. However, there are many problems in multihop transmission in traditional wireless sensor networks, such as energy hole, uneven energy consumption, unreliable data transmission, and so on. Based on the high maneuverability of unmanned aerial vehicles (UAVs), a mobile data collection scheme is proposed, which uses UAV as a mobile sink node in WSN water monitoring and transmits data wirelessly to collect monitoring node data efficiently and flexibly. In order to further reduce the energy consumption of UAV, the terminal nodes are grouped according to the dynamic clustering algorithm and the nodes with high residual energy in the cluster are selected as cluster head nodes. Then, according to the characteristics of sensor nodes with a certain range of wireless signal coverage, the angular bisection method is introduced on the basis of the traditional ant colony algorithm to plan the path of UAV, which further shortens the length of the mobile path. Finally, the effectiveness and correctness of the method are proved by simulation and experimental tests.

scholarly journals A DISTRIBUTED ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS

2018 ◽  
Vol 19 (1) ◽  
pp. 72-90
Author(s):  
Seyed Mohammad Bagher Musavi Shirazi ◽  
Maryam Sabet ◽  
Mohammad Reza Pajoohan
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Clustering Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Relay Nodes ◽  
Cluster Heads ◽  
Energy Efficient Clustering

Wireless sensor networks (WSNs) are a new generation of networks typically consisting of a large number of inexpensive nodes with wireless communications. The main purpose of these networks is to collect information from the environment for further processing. Nodes in the network have been equipped with limited battery lifetime, so energy saving is one of the major issues in WSNs. If we balance the load among cluster heads and prevent having an extra load on just a few nodes in the network, we can reach longer network lifetime. One solution to control energy consumption and balance the load among nodes is to use clustering techniques. In this paper, we propose a new distributed energy-efficient clustering algorithm for data aggregation in wireless sensor networks, called Distributed Clustering for Data Aggregation (DCDA). In our new approach, an optimal transmission tree is constructed among sensor nodes with a new greedy method. Base station (BS) is the root, cluster heads (CHs) and relay nodes are intermediate nodes, and other nodes (cluster member nodes) are the leaves of this transmission tree. DCDA balances load among CHs in intra-cluster and inter-cluster data communications using different cluster sizes. For efficient inter-cluster communications, some relay nodes will transfer data between CHs. Energy consumption, distance to the base station, and cluster heads’ centric metric are three main adjustment parameters for the cluster heads election. Simulation results show that the proposed protocol leads to the reduction of individual sensor nodes’ energy consumption and prolongs network lifetime, in comparison with other known methods. ABSTRAK: Rangkaian sensor wayarles (WSN) adalah rangkaian generasi baru yang terdiri daripada nod-nod murah komunikasi wayarles. Tujuan rangkaian-rangkaian ini adalah bagi mengumpul maklumat sekeliling untuk proses seterusnya. Nod dalam rangkaian ini dilengkapi bateri kurang jangka hayat, jadi simpanan tenaga adalah satu isu besar dalam WSN. Jika beban diimbang antara induk kelompok dan lebihan beban dihalang pada setiap rangkaian iaitu hanya sebilangan kecil nod pada tiap-tiap kelompok,  jangka hayat dapat dipanjangkan pada sesebuah rangkaian. Satu penyelesaian adalah dengan mengawal penggunaan tenaga dan mengimbangi beban antara nod menggunakan teknik berkelompok. Kajian ini mencadangkan kaedah baru pembahagian tenaga berkesan secara algoritma berkelompok bagi pembahagian data dalam WSN, dikenali sebagai Pembahagian Kelompok Kumpulan Data (DCDA). Melalui pendekatan baru ini, pokok transmisi optimum dibina antara nod sensor melalui kaedah baru. Stesen utama (BS) ialah akar, induk kelompok-kelompok (CHs) dan nod penyiar ialah nod perantara, dan nod-nod lain (nod-nod ahli kelompok) ialah daun bagi pokok trasmisi. DCDA mengimbangi beban CHs antara-kelompok dan dalam-kelompok komunikasi data daripada kelompok berbeza saiz. Bagi komunikasi berkesan dalam-kelompok, sebahagian nod penyampai akan memindahkan data antara CHs. Penggunaan tenaga, jarak ke stesen utama dan induk kelompok metrik sentrik adalah tiga parameter pelaras bagi pemilihan induk kelompok. Keputusan simulasi protokol yang dicadang menunjukkan pengurangan penggunaan tenaga pada nod-nod sensor individu dan memanjangkan jangka hayat rangkaian, berbanding kaedah-kaedah lain yang diketahui.

Distance Based Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

2014 ◽  
Vol 626 ◽  
pp. 20-25
Author(s):  
K. Kalaiselvi ◽  
G.R. Suresh
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Base Station ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Energy Efficient Routing

In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection

scholarly journals An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy-Efficient Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Oluwasegun Julius Aroba ◽  
Nalindren Naicker ◽  
Timothy Adeliyi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Gaussian Elimination ◽  
Clustering Algorithms ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fast Time

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

scholarly journals Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772090877
Author(s):  
Israel Edem Agbehadji ◽  
Samuel Ofori Frimpong ◽  
Richard C Millham ◽  
Simon James Fong ◽  
Jason J Jung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Energy Optimization ◽  
Base Station ◽  
Wireless Sensor ◽  
Optimal Time ◽  
Local Optimum

The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

scholarly journals NEW CLUSTERING SCHEMES FOR WIRELESS SENSOR NETWORKS

2010 ◽  
Vol 11 (1) ◽  
pp. 51-69
Author(s):  
S. M. Mazinani ◽  
J. Chitizadeh ◽  
M. H. Yaghmaee ◽  
M. T. Honary ◽  
F. Tashtarian
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Clustering Protocol

In this paper, two clustering algorithms are proposed. In the first one, we investigate a clustering protocol for single hop wireless sensor networks that employs a competitive scheme for cluster head selection. The proposed algorithm is named EECS-M that is a modified version to the well known protocol EECS where some of the nodes become volunteers to be cluster heads with an equal probability.  In the competition phase in contrast to EECS using a fixed competition range for any volunteer node, we assign a variable competition range to it that is related to its distance to base station. The volunteer nodes compete in their competition ranges and every one with more residual energy would become cluster head. In the second one, we develop a clustering protocol for single hop wireless sensor networks. In the proposed algorithm some of the nodes become volunteers to be cluster heads. We develop a time based competitive clustering algorithm that the advertising time is based on the volunteer node’s residual energy. We assign to every volunteer node a competition range that may be fixed or variable as a function of distance to BS. The volunteer nodes compete in their competition ranges and every one with more energy would become cluster head. In both proposed algorithms, our objective is to balance the energy consumption of the cluster heads all over the network. Simulation results show the more balanced energy consumption and longer lifetime.

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