Article

# EDDEEC: Enhanced Developed Distributed Energy-Efficient Clustering for Heterogeneous Wireless Sensor Networks

Authors:
• COMSATS Institute of Information Technology, Wah Cantt.
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## Abstract

Wireless Sensor Networks (WSNs) consist of large number of randomly deployed energy constrained sensor nodes. Sensor nodes have ability to sense and send sensed data to Base Station (BS). Sensing as well as transmitting data towards BS require high energy. In WSNs, saving energy and extending network lifetime are great challenges. Clustering is a key technique used to optimize energy consumption in WSNs. In this paper, we propose a novel clustering based routing technique: Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous WSNs. Our technique is based on changing dynamically and with more efficiency the Cluster Head (CH) election probability. Simulation results show that our proposed protocol achieves longer lifetime, stability period and more effective messages to BS than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) in heterogeneous environments.

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... It is preferable to have a few nodes with additional energy to extend the network life span in such applications. Numerous researchers have proposed distinctive clustering-based methodologies for increased energy efficiency and improved network lifetime (Heinzelman et al. 2000;Liao and Yang 2012;Qing et al. 2006;Elbhiri et al. 2010;Kumar et al. 2009;Javaid et al. 2013;Naranjo et al. 2017;Mittal et al. 2017aMittal et al. , 2017bNorouzi et al. 2011;Bhasker 2014;Bayraklı and Erdogan 2012;Kumar and Vishwas 2015;Dehghani et al. 2018;Ragavan and Ramasamy 2018;Yuan et al. 2017;Verma et al. 2019). The essential task in these protocols is to pick the CH proficiently. ...
... Instead, it considers the three levels of heterogeneity for deploying the nodes in the networks. Another protocol for the purpose is Enhanced DEEC (EDDEEC) (Javaid et al. 2013), which follows a similar approach as DDEEC (Elbhiri et al. 2010). ...
Article
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Internet of Things (IoT)-enabled Wireless Sensor Networks is not only an encouraging research domain but also a promising industrial trend which permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. The IoT device plays a significant role in various IoT-based networks and working of such network depends upon the battery power. Once the devices have deployed in the hostile environments, replacement of batteries is not feasible. To address this, challenge a plethora of research has been conducted but IoT networks still suffer one or the other way. In this paper, a genetic algorithm is integrated with an efficient clustering for power grid application where monitoring and controlling process is proposed by using movable sinks in IoT-enabled HWSNs (OptiGeA) . The OptiGeA protocol is depicted for cluster heads election by joining the factor of density, distance, energy and heterogeneous node’s capacity for its created fitness function. The investigation analysis of OptiGeA is prepared to work with single sink, multiple static sinks and multiple movable sinks to have an unslanted comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to shorten transmission distance between the sink and CH during system operation and pact with the hotspot issue. It is evident from a simulation study that the proposed OptiGeA protocol outflank state-of-the-art protocols on determinations of particular execution measurements precisely stability period, system's residual energy, network lifetime, throughput, and number of clusters per round execution.
... The more networks are being used are bi-directional. TheWSN [6][7][8][9][10][11] is consisting of several hundred and thousands sensor nodes, these nodes are comprised of large number of low-cost,Multifunctional this processes the data by sensing which is then communicated through its transceiver.The costof sensor nodes is similarly variable and depends upon the complexity of sensor the.A WSN [12][13][14][15][16] consists of spatially distributed autonomoussensors and have the features such as: to monitor physical or ecological conditions, such as temperature, sound, vibration, pressure, motion or pollutants and agreeably send the data with the help of network to the base station [7]. The main characteristics of WSN [9]are :No wired-infrastructure, Ability to manage with node failures, Mobile Nodes, Dynamic network topology, Self-organization , Heterogeneity of nodes, Scalability to large scale of deployment, Performance as router by all nodes, Ease of deployment, Unattended operation, Low-power consumption, Ease of use etc.The wireless sensor network [13] is being used broadly with the applications [14]: Military, Environmental Sensing, Area monitoring, Air pollution monitoring, Health, Forest fire detection, Home, Space Exploration and Chemical Processing. ...
... The more networks are being used are bi-directional. TheWSN [6][7][8][9][10][11] is consisting of several hundred and thousands sensor nodes, these nodes are comprised of large number of low-cost,Multifunctional this processes the data by sensing which is then communicated through its transceiver.The costof sensor nodes is similarly variable and depends upon the complexity of sensor the.A WSN [12][13][14][15][16] consists of spatially distributed autonomoussensors and have the features such as: to monitor physical or ecological conditions, such as temperature, sound, vibration, pressure, motion or pollutants and agreeably send the data with the help of network to the base station [7]. The main characteristics of WSN [9]are :No wired-infrastructure, Ability to manage with node failures, Mobile Nodes, Dynamic network topology, Self-organization , Heterogeneity of nodes, Scalability to large scale of deployment, Performance as router by all nodes, Ease of deployment, Unattended operation, Low-power consumption, Ease of use etc.The wireless sensor network [13] is being used broadly with the applications [14]: Military, Environmental Sensing, Area monitoring, Air pollution monitoring, Health, Forest fire detection, Home, Space Exploration and Chemical Processing. ...
Article
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Alot of routing protocols have been developed for Wireless sensor networks on different parameters but few parameters are not considered yet for homogeneous network scenario where each node have same capability in terms of processing power, storage, energy etc. A sensor network worksuntil the nodes do not drain their batteries. Further, it is very difficult to replenish the batteries of the nodes, once they are being deployed to some hostile environment. In this paper, five well accepted WSN routing protocols namely DEEC, EESAA, M-GEAR, M-LEACH and Z-SEP have been benchmarked for their energy pattern in homogeneous scenario. All simulations are done in MATLAB. Different parameters are used for checking the efficiency of the considered routing protocol for WSN. Simulation results shows that there is no clear winner for all situations but most of the time DEEC and M-GEAR protocol provide better results in terms of packets transferred to base station and cluster heads. EESAA and Z-SEP protocols performs well in terms of clusters and cluster heads formation than other protocols, on the other hand all protocols perform well in terms total number of alive and dead nodes.
... This mechanism route the data to transfer as a chain from the node through intermediate nodes to sink is known as PEGASIS. Javaid et al. [8], proposed a distributed energy-efficient clustering model to overcome the energy issues. This technique is based on the dynamic changes in the probability of the cluster head selection. ...
... The matrix used to store the values of each cluster node8 CSSE, 2022 ...
Article
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Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster. Initially, the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance. The proposed scheme performs hierarchal routing and direct routing with some energy thresholds. The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance. Moreover, the simulations will be performed in two scenarios, gateway based and without gateway to achieve more energy-efficient results.
... Then, two algorithms have been presented based on Particle Swarm Optimization (PSO), developed with an efficient particle encoding scheme and multi-objective fitness function [23]. In [24] author proposed an EDDEEC, 1 3 which is an energy-aware protocol. It dynamically changes the possibility of nodes becoming a CH to distribute an equal amount of energy consumption between sensor nodes. ...
... Type of Paper Problem [13] Survey WSN configuration for mining environment [14] Clustering Routing path optimization [21] Clustering Path optimization [22] Path Optimization Path Optimization [16] Clustering Load balancing problem to prolog all the nodes [15] Clustering Network clustering [17] Clustering Load balancing [19] Clustering Regional energy and weight computation [20] Survey Clustering algorithm classification [18] Clustering Handling uncertain level decision [23] Clustering Load balancing [24] Clustering CH selection ...
Article
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Wireless Sensor Network (WSN) is the future of next-generation’s communication and computational technology. Now WSN is being used to fulfill various application requirements like medical, engineering, industries, agriculture, etc. It is a resource-constrained network. Additionally, mobility in WSN causes serious issues related to QoS (Quality of Service) requirements like energy efficiency. In order to deal with this issue, in this paper, an Energy Efficient Weighted Clustering Algorithm (EE-WCA) has been proposed. The main aim of EE-WCA is to create a clustered network, which minimizes energy consumption and creates efficient regional Cluster Heads (CH). For this, three phases in clustering are defined. First, evaluating QoS requirements (i.e., buffer length, node displacement, battery level, connectivity, and SNR (signal to noise ratio)). Second, minimize the computational overhead of nodes to save energy using the weighted computation-based technique. This technique helps to regulate the application’s QoS requirements for the selection of CH. Finally, to distribute the resource consumption uniformly over the entire region of WSN, the CH updation process has been described. The experimental setup is prepared on the NS-2.35 simulator and the results are measured using 10 different sizes of network. The experimental observations on different performance factors, i.e. energy consumption, E2E(End to End) delay, throughput, packet delivery ratio, and packet drop ratio, confirm the enhanced performance of network with respect to a state-of-art WCA algorithm.
... Cluster formation also affects the lifetime of CHs since inappropriate cluster formation may force either CH or NCH to be depleted of energy sooner. A good survey of existing studies on clustering for WSNs can be found in Singh and Sharma [7] , these include energy-efficient algorithms [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] , MAC layer design [25][26][27] , and many more. Most of the clustering algorithms use a rotation of CHs among sensors to reduce the burden of CHs and balance energy consumptions among all the sensors. ...
... Most of the clustering algorithms use a rotation of CHs among sensors to reduce the burden of CHs and balance energy consumptions among all the sensors. Existing work on energy efficient clustering technology for WSNs is typically divided into centralized [16,20,21,23] and distributed approaches [8][9][10][11][12][13][14]22] . Distributed approaches make decisions based on local information exchanged between nearby sensors while centralized approaches try to solve an optimization problem based on global information, serving as a benchmark for the former. ...
Article
Motivated by recent developments in Wireless Sensor Networks(WSNs), we present distributed clustering algorithms for maximizingthe lifetime of WSNs, i.e., the duration till the first node dies. Westudy the joint problem of prolonging network lifetime by introducing clustering techniques and energy-harvesting (EH) nodes. Firstlywe propose distributed clustering algorithm for maximizing the lifetime of clustered WSN, which includes EH nodes, serving as relaynodes for cluster heads (CHs). Secondly graph-based and LP-basedEH-CH matching algorithms are proposed which serve as benchmarkalgorithms. Extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently
... However, low-energy nodes are not protected, which can easily lead to the rapid death of nodes near the BS due to the repeated selection of CHs. Literature [37], [38] also as modified the SEP algorithm to address multi-level energy heterogeneity and adopted a probabilistic calculation method based on energy ratio in the stage of the CH election, which protects low-energy nodes to a certain extent. Aderohumu et al. proposed the E-SEP algorithm [39] which added intermediate nodes and divided the nodes in the network into three categories according to energy. ...
Article
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Field stations and observation systems in cold and arid areas are mostly distributed in harsh natural environments which lead to problems such as the poor real-time data collection, transmission and processing, lower accuracy of data and failure to form integrated network observation, etc. To solve these problems which severely restrict scientific monitoring and research in these areas, a Layered and Heterogeneous Clustering routing algorithm LHC is proposed for field observation instrument networks based on the classical clustering routing algorithm LEACH. First, the LHC algorithm adopts the mechanism of heterogeneous node energy to divide nodes into advanced nodes and normal nodes. The advanced nodes have more initial energy than normal nodes, which increases their probability to be elected as cluster heads (CHs). Then, a hierarchical structure is used to divide the network into several layers, each layer elects a fixed number of CHs, and the distribution of CHs is improved. Finally, by analyzing the influence of the residual energy of the node and the distance between the node and the base station (BS) of the network, the mechanism based on energy and distance factors is introduced into each round of CH election to improve the CH election method. In Matlab experiments, the improved LHC algorithm was compared with LEACH, SEP, and DEEC through analysis and comparison from the aspects of network life cycle, energy consumption and CH number. The experimental results show that the LHC algorithm has the advantages of uniform CH distribution and balanced node energy consumption which effectively improve the energy efficiency and data transmission capacity, and prolong the life cycle of the observing instrument network. The LHC algorithm provides an important routing protocol for observing instrument network and real-time reliable data transmission.
... However, with the increase of deployment scale, the efficiency of the protocol declines dramatically due to the single-hop communication from cluster heads (CHs) to the base station (BS) and the possibility of low-power nodes being repeated as CHs [20,21]. Several dynamic CH role rotation algorithms have been suggested to eliminate the deficiencies of LEACH by multihops and energy awareness, including I-LEACH (improved low-energy adaptive cluster hierarchical) [22], EEUC (energy-efficient uneven clustering) [23], HEED (hybrid energy-efficient distribution) [24], DEEC (distributed energy-efficient clustering) [25], and DDEEC (developed distributed energy-efficient clustering) [26]. ...
Article
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... Many studies focus clustering alone to optimize the energy in WSN nodes, such as Low Energy Adaptive Hierarchical Clustering (LEACH) [9] and Balanced energy efficient network integrated super heterogeneous (BEENISH) protocols [10], which consider the residual battery of nodes only for CH selection. Authors in [10] compare their results with Distributed Energy-Efficient Clustering (DEEC) algorithm [11], Developed DEEC (DDEEC) [12], and Enhanced DDEEC (EDDEEC) [13] protocols. The results indicate that the BEEN-ISH [10] protocol has the highest data transmission rate with the largest number of nodes alive during the round. ...
Article
Full-text available
Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.
... In Priyadarshi et al. (2020), A review on clustering protocols with energy heterogeneity in wireless sensor networks is presented. Well known probabilistic clustering protocols for homogeneous network being LEACH, Hybrid Energy Efficient Distributed Clustering (HEED) (Younis & Fahmy, 2004) etc. and well known protocols for heterogeneous network are Distributed Energy Efficient Clustering (DEEC) (Qureshi, Javaid, Malik, Qasim, & Khan, 2012), Enhanced DEEC(EDEEC) (Javaid et al., 2013), Energy efficient heterogeneous clustered scheme for wireless sensor networks(EEHC) (Kumar, Aseri, & Patel, 2009). In these protocols, CHs are selected based on a probability value defined by a threshold. ...
Article
Wireless Sensor Networks is one of the most significant area of research where large number of sensor nodes that are distributed in a geographical area operate on limited battery power. As these networks, depending on the application, are sometimes deployed in in-hostile environment, which makes energy conservation one of the major challenge faced in WSN. To reduce energy consumption, clustering is considered to be the most efficient technique. This work proposes a new clustering approach that decreases energy consumption and results in prolonged network lifetime which is an important requirement for networks operating in inaccessible areas. In the proposed approach, heterogeneity is also implemented to increase the stability and energy efficiency of random networks. We have evaluated the efficiency of the proposed protocol through simulations and comparison is performed with well-known existing distributed protocols. Proposed approach shows efficient results in terms of the stability period for different network configurations and Base Station positions. Also, the results are found better in terms of number of alive nodes and network lifetime.
... The nodes with high computation have a better chance to be selected as a CH. Heterogeneous WSNs depends on two or multiple types of sensor nodes according to the levels of energy [54]. In our study, the model was devised as three levels. ...
Technical Report
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The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. Hence, energy constraints have drawn an enormous attention for sensor nodes that harvest energy from different renewable energy sources. Cluster-based routing protocols is deemed a promising approach for the topology management, energy consumption, data transfer and stability in a distributed manner. This technical report aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy level of the nodes to choose cluster heads. Simulation results prove that the proposed method increases network lifetime and the effective throughput.
... [57] EEHS (energy-efficient heterogeneous cluster scheme) used election probability proportional to residual energy [58]. BEENISH Balanced Energy-Efficient Network-Integrated Super Heterogeneous algorithm for WSNs which assigns one of the four energy levels to the node for selecting the cluster heads (CHs).EDDEEC protocol [59] was developed for the heterogeneous network which classifies nodes as normal nodes, advanced nodes, or supernodes and selecting as cluster heads (CHs). PEACH [60] is another heuristic algorithm used for multiple clustering which used location unaware and location-aware in WSNs. ...
Article
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State-of-the-art technologies are upcoming due to innovation and its implications in various domains. Wireless sensor networks playing vital role and energy consumption is considered as an important issues in this domain. All the IoT based applications needs to be multi-objective and optimized for improved performance, we can’t deny the fact that WSN plays an important role in every technologies as sensors were involved for collecting and sensing environment. Many solutions were given regarding the power utilization. Clustering is one such important solutions for efficient energy utilization. The extensive evolution of clustering algorithms leads to increase network lifetime, efficient energy consumption but because of many conflicting attributes that affects the efficiency of clustering that needs to be optimized such as residual energy, BS connectivity, distance of BS-CH’s, etc. and it required proper co-ordination. Our work proposes Multi-Attribute Decision Making (MADM) based Cluster Heads (CH’s) selection for enhancing network lifetime and efficient energy consumptions. We have considered a total 20 attributes, and co-ordination among these attributes has been done by using MADM approaches which selects optimal CHs for increasing network lifetime of WSN. Our results validate that our proposed algorithms performs better than EECH, LEACH-C, and LEACH in rapports of energy efficiency and optimal load-balanced among the sensor nodes. Also enhancement in network lifetime due to efficient energy consumption in proposed work.
... EDDEEC is designed as a three-level heterogeneity-based routing protocol [77]. It works in hierarchical networks and defines three types of sensors classified according to their energy levels into normal, advance and super nodes. ...
Article
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This paper surveys the energy-efficient routing protocols in wireless sensor networks (WSNs). It provides a classification and comparison following a new proposed taxonomy distinguishing nine categories of protocols, namely: Latency-aware and energy-efficient routing, next-hop selection, network architecture, initiator of communication, network topology, protocol operation, delivery mode, path establishment and application type. We analyze each class, discuss its representative routing protocols (mechanisms, advantages, disadvantages. . .) and compare them based on different parameters under the appropriate class. Simulation results of LEACH, Mod-LEACH, iLEACH, E-DEEC, multichain-PEGASIS and M-GEAR protocols, conducted under the NS3 simulator, show that the routing task must be based on various intelligent techniques to enhance the network lifespan and guarantee better coverage of the sensing area.
... EEHC 38 presented an algorithm that made use of three types of heterogeneous nodes for the first time, but still, its network suffered from the penalizing the high-energy nodes. Enhanced developed DEEC (EDDEEC) 39 handled this matter of penalization of the high-energy nodes by considering the energy threshold concept which was done at only two levels in case of DDEEC. With the gradual advancements in the development of routing protocols, balanced energy efficient network integrated super heterogeneous (BEENISH) 40 and improved BEENISH 41 exploited four levels of energy heterogeneity in their algorithms. ...
Article
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The proliferation in the sensing technology in wireless sensor network has left an everlasting impact to revolutionize every sector of human's lives. The limited battery of sensor nodes (SNs) has generated a tremendous challenge for the researchers to ameliorate the network's life span. To pacify this concern, in this paper, we have proposed a particle swarm optimization (PSO)‐based dual sink mobility (PSODSM) technique to reduce the energy expenditure of the SNs. PSODSM has the paramount focus on the cluster head (CH) selection based on integration of crucial factors: “ratio of remaining energy to the initial energy,” node degree, node centrality, separation factor between the SN and the sink, CH number, and energy consumption rate. As soon as the CHs are selected, two sinks placed opposite to each other are made to move towards the selected CHs for data collection. The most prominent fact is the movement of sinks which is done outside the periphery of the network to collect data rather than the reported conventional research works that employ sink mobility inside the network. Extensive simulations are performed to examine the empirical evaluation of PSODSM based on the benchmark of different performance metrics. The performance validation of PSODSM is done against the other meta‐heuristic algorithms, and findings show that the PSODSM outperforms the competitive algorithms and is also found to be scalable pertaining to real time implementation. Particle swarm optimization (PSO)‐based clustering is performed that selects cluster head (CH) based on six parameters, (a) residual energy, (b) distance factor, (c) node centrality, (d) node degree, (e) CH count, and (f) energy consumption rate. The dual sinks are made to move opposite to the each other in such a way that they collectively cover four sides of the square‐shaped network. The simulation results demonstrate the proposed protocol outperforms the competitive algorithms.
... The punishment at three levels was additionally kept away from by EDDEEC. 25 To improve the steadiness time frame to the further levels, BEENISH 26 was presented that pre-owned vitality heterogeneity at four levels. Mittal et al 27 proposed threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP) for enhancing the network lifetime of WSN. ...
Article
Full-text available
Wireless sensor network (WSN) suffers from the energy‐limited sensor nodes which consume energy heavily depending upon the magnitude of data which is transmitted or received by the nodes in the network. In this paper, our primary aim is to reduce the quantity of data transmitted to the data‐collecting sink, which helps in the energy preservation and eventually leads to network longevity. To address this concern, in this paper, we propose a novel framework for energy‐efficient compressive data gathering (NFECG) for heterogeneous WSN. NFECG works in four following phases; in the first phase, the cluster head (CH) selection is performed by considering remaining energy, “distance within the nodes and the sink,” and node density; in second phase, sleep scheduling is done among the cluster member nodes; further, in third phase, the compression of the aggregated data is performed at the CH level, and equivalent compressed sparse signals are generated which are transmitted to sink. In the last phase, at the sink, decompression is applied to retrieve the original signals. The simulation of NFECG is performed using MATLAB under two cases of different network area and number of nodes. We examine its performance for various performance metrics and also inspect for its scalable characteristics. The results show that for one of the two cases, it improves stability period and network lifetime by 52.59% and 46.09%, respectively, as compared to energy‐adjusted high‐level data total tree (EHDT) protocol, and also for the other case of network configuration, it acquires supreme performance.
... All nodes had different levels of energy due to random allocation. But this two-level heterogeneous network model has a limitation that each node has different energy level, and therefore, the deployed sensor nodes with higher energy levels may not be practically feasible [41,50,51]. Therefore, an advanced algorithm i.e. effective data gathering algorithm (EDGA) proposed for heterogeneous WSNs [41] which considers three levels of heterogeneity viz. ...
Article
The wireless sensor networks (WSNs) with high sensing, computing and communication capabilities had wide applications in different fields. Till date, the WSNs are characterized by several resource constraints viz. energy, processing power, storage and transmission range etc. Therefore, the present research work adopted the hybrid protocol integrating homogeneous and heterogeneous clustering protocols viz. Low Energy Adaptive Clustering Hierarchy (LEACH)-Distributed Energy Efficient Clustering (DEEC) (LEACH-DEEC protocol) in comparison to LEACH-Centralized (LEACH-C) and LEACH-Centralized Sleeping (LEACH-CS) protocols to increase the energy efficiency and network lifetime of WSNs. For LEACH-C protocol, the network lifetime varied between 1058-1454 rounds, as compared with 1202-1987 rounds for LEACH-CS protocol, and 1389-2472 rounds for LEACH-DEEC protocol. Average across the number of alive nodes, the network lifetime for LEACH-CS and LEACH-DEEC protocol was improved by ~27.0 and 52.3%, respectively. The hybrid protocol resulted in ~158.1 and 121.7% increase in networks' lifetime, compared with LEACH-C and LEACH-CS protocols, respectively. The total initial energy was 63.7 Joules (J) for LEACH-C, 75.7 J for LEACH-CS and 97.9 J for LEACH-DEEC protocol. The number of data packets transferred to the BS with respect to the number of rounds by LEACH-C, LEACH-CS and LEACH-DEEC protocols was 2.44 x 10 4 , 3.24 x 10 4 and 4.24 x 10 4 packets, respectively. The reliability of LEACH-DEEC protocol was considerably higher by ~28.8 and 14.6% as compared to LEACH-C and LEACH-CS protocols, respectively. These results revealed that the number of rounds at half of the active nodes was ~14.9 and 29.6% higher with hybrid protocol, compared with LEACH-C and LEACH-CS protocols, respectively. These results highlight that newly proposed hybrid protocol was more energy efficient and had prolonged network lifetime.
... Qing et al. [36] and Javaid et al. [37] developed the DEEC (Developed Distributive Energy-Efficient Clustering) and EDDEEC (Enhanced Developed Distributive Energy-Efficient Clustering) protocols, respectively. In DEEC, the average network energy and node initial energy-based probability function is formulated. ...
Article
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... Versions of DEEC like DDEEC [18], E-DEEC (Enhanced Distributed Energy Efficient Clustering) [19], ip-EDEEC [20], EDDEEC [21] and hetDEEC [22]: heterogeneous distributed energy efficient clustering protocol diversified the approach further and implements the network with three-level heterogeneity using weighted election probability. The authors claim to enhance the energy levels and the network lifetime. ...
... However, concerning energy (EC), the evaluation and optimization of the network often present difficulties as a comprehensive model, which hardly takes the EC into account. Figure 3 shows some energy consumption factors of WSN [14]. ...
Article
This research article investigates effective energy protocols for wireless sensor networks (WSN). The newly proposed taxonomic classification and comparison provides the following protocol categories: latency and efficient routing based on energy and hop selection in network and its architecture, communication sensor network, networking structure, procedure functioning, sending and receiving round mode, and route setting. This research work has examined each class to discuss and compare the different parameters of its representative routing protocols (mechanisms, advantages, disadvantages) based on the energy efficient rate along with delivery delay and network time. The simulation results on the NS-simulator of various protocols show that, the routing task has to be built upon different intelligent technologies to improve the network life and ensures better sensory area coverage.
... The new threshold of TDEEC guarantees that only nodes with higher residual energy should be chosen as cluster heads. Javaid et al. [39] introduced an Enhanced Developed Distributed Energy-Efficient Clustering (EDDEEC) scheme where the cluster head election probability is dynamically changed according to an absolute energy level threshold. to avoid selecting only the advanced and super sensors as cluster heads in all rounds, which depletes their energy quickly, the energy level threshold guarantees that all nodes have the same CH selection probability when they own almost the same residual energy. ...
Article
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... Many studies employ clustering alone to optimize the energy in WSN nodes, such as the LEACH and BEENISH protocols [35], which consider the residual battery of nodes only for CH selection. Authors in [35] compare their results with istributed energy-efficient clustering algorithm (DEEC) [36], developed DEEC (DDEEC) [37], and enhanced DDEEC (EDDEEC) [38] protocols. The results indicate that the BEENISH [35] protocol has the highest data transmission rate with the largest number of nodes alive during the round. ...
Preprint
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Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multihop communication and a shorter transmission range. Researchers propose different but isolated clustering and routing solutions that are inefficient in terms of energy efficiency and network connectivity in IoT-based WSNs. In this work, we emphasize the importance of considering the context of IoT applications that have further requirements for dedicated data collection per node. We address two interlinked issues, clustering and routing, in a large-scale IoT-based WSN. We propose an improved clustering and routing (ICR) protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. This clustering also develops a strong network backbone that provides fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols, Joint Clustering and Routing (JCR), Low Energy Adaptive Hierarchical Clustering (LEACH) and other recent protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.
... However, this dynamic nature and dependency on E lead to higher epoch for than that lead to an early decay of . EDDEEC (Javaid et al., 2013) introduced threshold residual energy, ℎ = 7 10 , that set usage-limit on energy for all nodes until switching back to . MED-BS clustering algorithm (Guiloufi et al., 2013) introduced energy-aware mobility model for WSN sensors that successfully build a stable model to support scalability without overusing the cluster rebuilding phase. ...
Article
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Energy-efficient resource-optimization for multiple application scenarios requires appropriate clustering-based network-fractioning that skillfully removes unbalanced network connectivity, which is a stimulating bottleneck in WSN-performance. This paper proposes a new Uniform Connectivity-based clustering Protocol, called Lines-of-Uniformity based Enhanced-Threshold (LUET) to provide energy-efficient coverage in three-tier heterogeneous WSN. This protocol considers the node’s remnant-energy and its proximity from either of the lines of uniformity for lowering down average isolated node count in WSN. This paper also proposes a rotation epoch-based LUET variant, (LUET|R) that incorporates static epoch for initial clustering rounds until First-Node-Death to overcome the rapid fall after the death of the first node. The simulation model demonstrates the superiority of LUET and its variants over other established clustering protocols in terms of network-lifetime, power-efficiency, net death-rate, average isolated-nodes, throughput. Index Terms Clustering, Energy-efficiency, Heterogeneous-networks, Lifetime-estimation, WSN
... This mechanism route the data to transfer as a chain from the node through intermediate nodes to sink is known as PEGASIS. Javaid et al. [8], proposed a distributed energy-efficient clustering model to overcome the energy issues. This technique is based on the dynamic changes in the probability of the cluster head selection. ...
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... The sensor node energy is not taken into account by EEHC in the CH selection approach but during the placement of nodes in networks, 3-levels of heterogeneity are considered. Enhanced DEEC (EDDEEC) [8], which uses another protocol for this purpose followed by the same approach as DDEEC [6]. In terms of energy heterogeneity, Qureshi et al. proposed another procedure termed BEENISH [9], which has four levels of heterogeneity. ...
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... EDDEEC [11] method is used for heterogeneous WSNs. It is three level heterogeneous WSNs. ...
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... Table 1 summarizes the issue of QoS requirements and the parameters considered in different clustering schemes. 38,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] In HCR, 24 a set of associates is used to manage each cluster, resulting in longer lifetimes of the clusters; the clusters are selected by using the GA-based approach. In Bhatia et al., 26 GADA-LEACH is proposed, which is the enhancement of the LEACH protocol. ...
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... A similar Th res parameter was used for three-level heterogeneity network in EDDEEC protocol [31]. In order to extend dynamicity, an n-level heterogeneity based hetDEEC protocol [32] was proposed that could be applied on 1/2/3-level heterogeneous network model just by varying a Θ parameter. ...
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PEGASIS: “power efficient gathering in sensor information systems
• S Lindsey
• C S Raghavenda