scholarly journals Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 384 ◽  
Author(s):  
Pankaj Kumar Kashyap ◽  
Sushil Kumar ◽  
Upasana Dohare ◽  
Vinod Kumar ◽  
Rupak Kharel
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Load Balancing ◽  
Clustering Algorithm ◽  
Average Energy ◽  
Residual Energy ◽  
Base Station ◽  
Inference System ◽  
Neuro Fuzzy

Energy is a precious resource in the sensors-enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly, which may interrupt the operations in the network. Further, a single artificial intelligence technique is not enough to solve the problem of load balancing and minimize energy consumption, because of the integration of ubiquitous smart-sensors-enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesized fuzzy logic and a neural network to counterbalance the selection of the optimal number of cluster heads and even distribution of load among the sensors. We developed fuzzy rules, sets, and membership functions of an adaptive neuro fuzzy inference system to decide whether a sensor can play the role of a cluster head based on the parameters of residual energy, node distance to the base station, and node density. The proposed ANFCA outperformed the state-of-the-art algorithms in terms of node death rate percentage, number of remaining functioning nodes, average energy consumption, and standard deviation of residual energy.

Energy Efficient Cluster-based Routing using Hybrid Approach of Fuzzy and Ida-Star for Wireless Sensor Networks

2021 ◽  
Author(s):  
Mohaideen Pitchai K
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Hybrid Approach ◽  
Cluster Head ◽  
Residual Energy ◽  
The Other ◽  
Cluster Head Selection

Abstract Appropriate cluster head selection can significantly reduce energy consumption and enhance the lifetime of the WSN. The choice of cluster heads, which is a pivotal step in the cluster-based algorithm, can seriously influence the performance of the clustering algorithm. Under normal circumstances, whether a node can be a cluster head or not depends not only on its energy level but also on the other factors such as energy consumption, channel lost, neighbor density, etc. In this sense, the selection of the cluster head can be regarded as a multiple criteria decision-making issue. This paper presents an Energy efficient Cluster Head selection using Fuzzy Logic (ECHFL) protocol, which combines the approaches of the fuzzy and IDA-star algorithm. This protocol selects the appropriate cluster head by using fuzzy inference rules. It uses three parametric descriptors such as residual energy, expected residual energy, and node centrality for the cluster formation and cluster head selection processes. These parameters contribute mainly for avoiding over-dissipation of energy in the network by selecting the suitable cluster head for the network. This protocol shows how fuzzy logic can be used in the cluster formation process to distribute the tasks and energy consumption over all the nodes. As a summary, the proposed protocol gives good performance results in comparison with the other protocols.

Trusted energy aware cluster based routing using fuzzy logic for WSN in IoT

2021 ◽  
pp. 1-15
Author(s):  
A.R. Rajeswari ◽  
K. Kulothungan ◽  
Sannasi Ganapathy ◽  
Arputharaj Kannan
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Cluster Formation ◽  
Optimal Path ◽  
Life Time ◽  
Base Station ◽  
Energy Aware ◽  
Inference System ◽  
Cluster Based Routing

WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches.

scholarly journals LIFETIME MAXIMIZATION OF A MOBILE WSN USING ZRP-FUZZY CLUSTERING PROTOCOL BASED ON ANT-LION OPTIMIZER

2021 ◽  
Vol 1 (1) ◽  
pp. 70-82
Author(s):  
Amnah A. Saadi ◽  
Osama A. Awad
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Ant Lion Optimization ◽  
Zone Routing Protocol ◽  
Ant Lion ◽  
Selection Of

Wireless Sensor Networks require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of  WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. This paper prepared a modified ZRP  for mobile WSN  clustering scheme and optimization using ant-lion optimization algorithm and so far named as mobility cluster head fuzzy logic based on the zone routing protocol (ZRP-FMC-ALO). Which proposed fuzzy logic approach based on three descriptors node for the selection of the CH nodes such as, residual energy, the concentration, and the centrality of the node and also exploited the concept of the mobility of the  Base Station (BS) to prolong the life span of a WSN. The performance of the proposed protocol compared with the famous protocol such as LEACH. Using the MATLAB simulator and the result shows that it outperforms in terms of the WSN network lifetime, the average remaining-consuming energy, and the number of a live node.  

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 FLECH: Fuzzy Logic Based Energy Efficient Clustering Hierarchy for Nonuniform Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Baranidharan Balakrishnan ◽  
Santhi Balachandran
Keyword(s):  
Fuzzy Logic ◽  
Data Collection ◽  
Data Transmission ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Redundant Data ◽  
Energy Efficient Clustering

Lifetime of Wireless Sensor Network (WSN) is an important issue which affects its implementation in various real time applications. The major factor behind the energy consumption in WSN is its data collection mechanism. The direct data transmission from each sensor node to the Base Station (BS) consumes more energy than other alternatives. Also it is unnecessary, due to redundant data transmission because of geographically closer nodes. Clustering is the best data collection architectural model for WSN since it takes care of in-network processing which handles redundant data within the network. The techniques used for the network having uniform node distribution are not suitable for the networks which have nonuniformly distributed nodes. This paper contributes a novel clustering algorithm: Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH) for nonuniform WSN. The clusters in FLECH are created using proper parameters which increases the lifetime of the WSN. Fuzzy logic in FLECH is wisely used to combine important parameters like residual energy, node centrality, and distance to BS for electing best suitable nodes as CH and increases the network lifetime. FLECH performance is verified in different scenarios and the results are compared with LEACH, CHEF, ECPF, EAUCF, and MOFCA. The simulation results clearly indicate the lifetime increase by FLECH over other algorithms and its energy conservation per round of data collection in the network.

Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Rui ◽  
Junying Wu ◽  
Jianbin Zhao ◽  
Maryam Sadat Khamesinia
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Load Balancing ◽  
Integrated Circuits ◽  
Transportation Systems ◽  
Future Research ◽  
Optimization Approach ◽  
Content Type ◽  
And Control

Purpose Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them. Design/methodology/approach Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency. Findings The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm. Research limitations/implications There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment. Practical implications The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics. Originality/value This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

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 A Dynamic K-means Based Clustering Algorithm Using Fuzzy Logic for CH Selection and Machine Learning Based Data Transmission

2021 ◽  
Author(s):  
Anupam Choudhary ◽  
Abhishek Badholia ◽  
Anurag Sharma ◽  
Brijesh Patel ◽  
Sapna Jain
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Cluster Center ◽  
Similar Data ◽  
Inference System ◽  
Fuzzy Input

Abstract Clustering is effective method to increase network lifetime, energy efficiency, and connectivity of Sensor nodes in wireless sensor network. An energy efficient clustering algorithm has been proposed in this paper. Sensor nodes are clustered using K-means algorithm which dynamically forms number of clusters in accordance with number of alive nodes. Selection of suitable CH is done by fuzzy inference system by choosing three fuzzy input variable such as residual energy of Sensor node, its distance from cluster center and base station. Amount of data transmitted by member nodes to CH is reduced by machine learning that classify similar data at regular interval. The simulation results show that proposed algorithm outperforms other cluster based algorithms in terms of data received by base station, number of alive node per round, time of first node, middle node and last node to die for various density of sensor nodes and scalable conditions.

scholarly journals Enhanced Clustering Algorithm based on Fuzzy Logic (E-CAFL) for WSN

2019 ◽  
Vol 20 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Pawan Singh Mehra ◽  
Mohammad Najmud Doja ◽  
Bashir Alam
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Cluster Formation ◽  
Cluster Head ◽  
Residual Energy ◽  
Inference System ◽  
Premature Failure ◽  
System A ◽  
Balanced Clustering

Since longer lifetime of the network is utmost requirement of WSN, cluster formation can serve this purpose efficiently. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to sink. With imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role. In this paper, an enhanced clustering algorithm based on fuzzy logic E-CAFL is propound which is an improvement over CAFL protocol. E-CAFL takes account of the residual energy, node density in its locality and distance from sink and feed into fuzzy inference system. A rank of each node is computed for candidature of cluster coordinator. Experiments are performed for the designed protocol to validate its performance in adverse scenarios along with LEACH and CAFL protocol. The results illustrate better performance in stability period and protracted lifetime.

scholarly journals Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 3154-3161
Keyword(s):  
Network Lifetime ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Cluster Head ◽  
Data Communication ◽  
Residual Energy ◽  
Base Station ◽  
Clustering Method ◽  
Inference System ◽  
Projected Clustering

These-days Wireless Sensor Networks (WSNs) has become integral part of many applications include tracking, monitoring and so on. Nodes are limited in battery, memory and processing capacity. Tracking and monitoring applications continue to work for longer hours; energy is the major constraint for network to transmit sensed data. State of the art specifies that by using clustering method energy-efficiency, scalability, and efficient-data-communication is achieved. Sensors deployed in the network be partitioned to clusters then one of the nodes is designated to become a Cluster Head (CH) that accumulate sensed information and sends to Sink/Base Station (BS). Normally CH is elected by considering nodes remaining energy and topological attributes related to the node in network. In this projected clustering method a centrality-metric “Cluster-Optimal-Degree-Centrality (CODC)”, is defined and also considered other parameters residual energy, distance between CHs, plus number of nodes belonging to a cluster guarantees better cluster configuration and CH selection. Fuzzy-Inference-System takes Expected-Residual-Energy (ERE) and CODC as inputs. Experiments are carried using ns-2; the proposed clustering method improves QoS, and efficiently prolongs network lifetime.

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