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 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 Reducing Energy Consumption of the SEECH Algorithm in Wireless Sensor Networks Using a Mobile Sink and Honey Bee Colony Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 185-200
Author(s):  
Mohammad Sedighimanesh ◽  
Ali Sedighimanesh
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Data Dissemination ◽  
Mobile Sink ◽  
Wireless Sensor ◽  
Bee Colony ◽  
Honeybee Colony ◽  
Reduce Energy Consumption

Purpose – Clustering, routing, and data dissemination are an important issue in wireless sensor networks. The basic functions of wireless sensor networks are phenomena controlling in the physical environment, and the reporting of sensed data to the central node called sink, in which more operations can be done on the data. The most important limitation of wireless sensor networks is energy consumption. There are several ways to increase the lifetime of these networks, that one of the most important is the using proper clustering method. The aim of this study is to reduce energy consumption using an effective clustering algorithm and for this purpose, the honeybee colony metaheuristic method was used for cluster heads selection. Methodology/approach/design – The simulation in this paper was done using MATLAB software and the proposed method is compared with the LEACH and SEED approach. Findings – The results of simulations in this research indicate that the research has significantly reduced the energy consumption in the network than LEACH and SEED algorithms. Originality/value – Given the energy constraints in the wireless sensor network, providing such solutions and using metaheuristic algorithms can dramatically reduce energy consumption and, consequently increase network lifetime.

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 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.

Energy saving algorithm and simulation test in wireless sensor networks

2021 ◽  
pp. 1-12
Author(s):  
Lv Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Saving ◽  
Mobile Sink ◽  
Base Station ◽  
Wireless Sensor ◽  
Data Forwarding ◽  
Multi Objective Programming ◽  
Saving Algorithm

In recent years, scientists have begun to introduce dynamic elements into wireless networks. With the introduction of mobile sink node, the phenomenon of “hot node” and “energy hole” can be effectively avoided, so as to realize more network connection and improve network flexibility. Therefore, it is imperative to design energy-saving algorithm with popular sink code. In this paper, a multi hop data forwarding algorithm is proposed for solar powered wireless sensor networks. The algorithm divides the monitoring area of the network and the communication area of the node. Through the sensor node, the next hop node is selected from the appropriate area, thus forming the path from the data source point to the base station. At the same time, in order to reduce the energy consumption and delay in the network, a multi-objective programming model of the next hop data forwarding node is established. The reasonable area of static and dynamic area is calculated by mathematical analysis. Finally, the paper calculates the network’s life cycle, energy consumption and transmission time, and compares the static sink with the network using only mobile sink.

scholarly journals Multi-Criteria Decision-based Path Planning for Data Collection in Fuzzy-Cluster based Large Sensor Networks

2021 ◽  
Author(s):  
Sunil Kumar Singh ◽  
Prabhat Kumar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Information Transmission ◽  
Sensor Node ◽  
Clustering Algorithm ◽  
Mobile Sink ◽  
Base Station ◽  
Fuzzy Cluster ◽  
Wireless Sensor

Abstract Wireless sensor networks (WSN) are widely used to gather information using wireless communication, but due to the confined power of sensor nodes, it is a predominant task to make WSN energy-efficient. Generally, a sensor node finds routes towards the base station (BS) to transmit the data. The sensor node transmits information at once or via neighbor nodes in a multi-hop manner. The nodes close to the BS transmit more data than other nodes. The nodes close to the BS tend to deplete their power quicker than different nodes in the network. This issue is known as the hotspot problem, leading to network separation and reducing the lifetime of the sensor network. The mobile sink as a better strategy to dispose of the hotspot problem in the course of information transmission, but the most critical challenges are finding sojourn points and path planning. This paper makes use of a cluster head (CH) as a sojourn point and multi-criteria decision strategy for path planning by the mobile sink at some stage in information collection. This paper contributes a fuzzy-based clustering algorithm that uses fuzzy logic to decide on cluster heads (CHs). The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach used to figure out the mobile sink's route. Based on this route, the mobile sink will move to collect data from every CH in the network. This scheme receives an order of CHs to visit and collect the data with the mobile sink. The simulation outcomes exhibit that this strategy performs better than the other protocols, which uses multi-hop information transmission for extending the lifetime of wireless sensor networks and one parameter for route planning.

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.

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.

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.

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