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 A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongchun Qu ◽  
Libiao Lei ◽  
Xiaoming Tang ◽  
Ping Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Base Station ◽  
Simulation Software ◽  
Wireless Sensor ◽  
Sensor Monitoring

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.

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.

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 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.

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 Searching Algorithm of Dormant Node in Wireless Sensor Networks

2017 ◽  
Vol 13 (05) ◽  
pp. 122 ◽  
Author(s):  
Bo Feng ◽  
Wei Tang ◽  
Guofa Guo
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Delaunay Triangulation ◽  
Base Station ◽  
Wireless Sensor ◽  
Effective Mechanism ◽  
Dormant State ◽  
Important Nodes

In wireless sensor networks, the nodes around the base station have higher energy consumption due to the forwarding task of all the detected data. In order to balance the energy consumption of the nodes around the base station, a reasonable and effective mechanism of node rotation dormancy is put forward. In this way, a large number of redundant nodes in the network are in a dormant state, so as to reduce the load of important nodes around the base station. The problems of the redundant nodes in the sensor network are analyzed, and a new method is proposed to distinguish the redundant nodes based on local Delaunay triangulation and multi node election dormancy mechanism. The experimental results showed that this method could effectively distinguish the redundant nodes in the network; at the same time, through the multi round election mechanism, parts of redundant nodes are made dormant. In summary, they can reduce the network energy consumption on the condition of guaranteeing the original coverage.

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.

Performance Analysis of Multi-Hop Routing Protocol With Optimized Grid-Based Clustering for Wireless Sensor Network

2020 ◽  
pp. 254-282
Author(s):  
Saloni Dhiman ◽  
Deepti Kakkar ◽  
Gurjot Kaur
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Sensor Network ◽  
Routing Protocol ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grid Based

Wireless sensor networks (WSNs) consist of several sensor nodes (SNs) that are powered by battery, so their lifetime is limited, which ultimately affects the lifespan and hence performance of the overall networks. Till now many techniques have been developed to solve this problem of WSN. Clustering is among the effective technique used for increasing the network lifespan. In this chapter, analysis of multi-hop routing protocol based on grid clustering with different selection criteria is presented. For analysis, the network is divided into equal-sized grids where each grid corresponds to a cluster and is assigned with a grid head (GH) responsible for collecting data from each SN belonging to respective grid and transferring it to the base station (BS) using multi-hop routing. The performance of the network has been analyzed for different position of BS, different number of grids, and different number of SNs.

scholarly journals A New Centralized Clustering Algorithm for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4391 ◽  
Author(s):  
Juan-Carlos Cuevas-Martinez ◽  
Antonio-Jesus Yuste-Delgado ◽  
Antonio-Jose Leon-Sanchez ◽  
Antonio-Jose Saez-Castillo ◽  
Alicia Triviño-Cabrera
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fuzzy System ◽  
Clustering Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Cluster Heads ◽  
Control Message ◽  
Message Exchange

Clustering is presently one of the main routing techniques employed in randomly deployed wireless sensor networks. This paper describes a novel centralized unequal clustering method for wireless sensor networks. The goals of the algorithm are to prolong the network lifetime and increase the reliability of the network while not compromising the data transmission. In the proposed method, the Base Station decides on the cluster heads according to the best scores obtained from a Type-2 Fuzzy system. The input parameters of the fuzzy system are estimated by the base station or gathered from the network with a careful design that reduces the control message exchange. The whole network is controlled by the base station in a rounds-based schedule that alternates rounds when the base station elects cluster heads, with other rounds in which the cluster heads previously elected, gather data from their contributing nodes and forward them to the base station. The setting of the number of rounds in which the Base Station keeps the same set of cluster heads is another contribution of the present paper. The results show significant improvements achieved by the proposal when compared to other current clustering methods.

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