Hybrid Energy Efficient Distributed Research Articles

Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.

A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.

Big data analytics for large‐scale UAV‐MBN in quantum networks using efficient hybrid GKM

SummaryA primary important aspect of wireless network domain is known to be secured communication as the signals are emphatically available and broadcasted through the air. Quantum wireless networks are a hot topic of research in recent years that made researchers to avail new techniques in this field. Big data is one of the major aspects that support large‐scale quantum network applications. As sensor nodes have resource constraints, it is more complex when using the traditional techniques of key establishment that only used for stable communication networks. Group‐related applications are also one of the important aspects in wireless network for sending and receiving messages from multiple users. Unfortunately, maintenance of security for the group‐oriented protocols is major issue due to the frequent changes in membership. In this paper, a hybrid GKM with re‐keying procedure is proposed to obtain a secure communication for the remote system of unmanned autonomous vehicle (UAV) with mobile backbone network (MBN). Here, the group controller is selected based on the simplified hybrid energy efficient distributed (HEED) protocol and honey key encryption algorithm is used by the key management center (KMC) for generating and distribution of key for the group's controller. The secure communication is explored using the key exchange mechanism between the nodes. The joining and leaving of a node from the group are initiated by the re‐keying process. The simulation results explain the improved performance of the proposed hybrid method when compared with other existing techniques in terms of privacy level, energy, memory, and time consumption.

Heed Protocol Using A Cluster Based V2V Communication

Objectives: Wireless sensor Networks employs a set a sensor nodes to monitor the parameters of the environment. The sensory information collected by the sensor nodes can be retrieved with the help of Mobile Nodes fixed upon vehicles. Existing scenarios engage both single-hop data transfer from Sensor nodes that lie within the mobile network range or profound participation of network margin nodes. These nodes are at the peril of quick energy collapse resulting in loss of network connectivity and decrease in network lifetime. The main idea behind this paper is to enhance the network lifetime and security. Methods/Statistical Analysis: The proposed HEED (Hybrid energy efficient Distributed Clustering) protocol reduces the overall network operating cost and energy disbursement associated with the multi hop data recovery process while also ensuring fair energy utilization among Sensor nodes and prolonged network lifetime. In multi hop network, the nodes closer to the sink node are involved in almost all data transmission, hence their energy would be quickly drained out. Since the energy depletion at sink node is more than other nodes, energy holes are created. Therefore other nodes in the VANET are disconnected from the network which reduces the network lifetime. To enhance network lifetime routing protocols are introduced in addition to HEED protocol which provides efficient clustering. To improve the security Intrusion-Tolerant Routing protocol (INSENS) protocol is used, which uses multipath technique in order to make the network resilient to attacks. Findings: The proposed HEED protocol elects the cluster head periodically based on a metric which is based on parameters like residual energy of the node, closeness of its neighbors and the degree of the node. The number of iterations involved in HEED protocol is very less and it achieves literally consistent distribution of cluster head allotment across the network involving a low level of information overhead. With suitable limits on node density, intra and inter cluster transmission ranges, this protocol can assure connectivity of clustered networks. Simulation results prove that this protocol is efficient in enhancing the lifetime of the network and sustaining scalable data aggregation. In case of Distributed clustering algorithms it is the responsibility of the base station to collect the information gathered by the cluster heads. In this work each and every sensor node broadcasts it accessibility to its surrounding. Based on the broadcast information the cluster head belonging to the specific cluster is identified in parallel. INSENS protocol is used to enhance network security. Simulation results prove that this protocol is efficient in enhancing the security of the network. Application/Improvements: This study proposes centralized, HEED_INSENS protocols to solve the coverage problem in VANETs. It is also useful for applications that require data aggregation and those where all data originated from sources should reach the sink without prior aggregation. Keywords: Sensor Nodes, Network Overhead, Network Lifetime, HEED, INSENS

Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks

In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving energy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network lifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to cluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In this paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network. We then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a distributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct extensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient distributed (HEED) clustering algorithm in terms of energy cost and network lifetime.

Clustering-based Optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system

Proficient clustering method has a vital role in organizing sensor nodes in wireless sensor networks (WSNs), utilizing their energy resources efficiently and providing longevity to network. Hybrid energy-efficient distributed (HEED) protocol is one of the prominent clustering protocol in WSNs. However, it has few shortcomings, i.e., cluster heads (CHs) variation in consecutive rounds, more work load on CHs, uneven energy dissipation by sensor nodes, and formation of hot spots in network. By resolving these issues, one can enhance HEED capabilities to a greater extent. We have designed variants of Optimized HEED (OHEED) protocols named as HEED-1 Tier chaining (HEED1TC), HEED-2 Tier chaining (HEED2TC), ICHB-based OHEED-1 Tier chaining (ICOH1TC), ICHB-based OHEED-2 Tier chaining (ICOH2TC), ICHB-FL-based OHEED-1 Tier chaining (ICFLOH1TC), and ICHB-FL-based OHEED-2 Tier chaining (ICFLOH2TC) protocols. In HEED1TC and HEED2TC protocols, we have used chain-based intra-cluster and inter-cluster communication in HEED, respectively, for even load balancing among sensor nodes and to avoid more work load on CHs. Furthermore, for appropriate cluster formation, minimizing CHs variation in consecutive rounds and reducing complex uncertainties, we have used bacterial foraging optimization algorithm (BFOA)-inspired proposed intelligent CH selection based on BFOA (ICHB) algorithm for CH selection in ICOH1TC and ICOH2TC protocols. Likewise, in ICFLOH1TC and ICFLOH2TC protocols, we have used novel fuzzy set of rules additionally for CH selection to resolve the hot spots problem, proper CH selection covering whole network, and maximizing the network lifetime to a great extent. The simulation results showed that proposed OHEED protocols are able to handle above-discussed issues and provided far better results in comparison to HEED.

NEECP: Novel energy‐efficient clustering protocol for prolonging lifetime of WSNs

In this paper, a novel energy-efficient clustering protocol (NEECP) for increasing the network lifetime in wireless sensor networks is proposed. This technique selects the cluster heads in an effective way with an adjustable sensing range and performs data aggregation using chaining approach. It also avoids transmission of redundant data by using a redundancy check function for improving the network lifetime. It is implemented by considering the data with aggregation and without aggregation. The NEECP without aggregation increases the network lifetime by 59.76 and 7.17% as compared with the hybrid energy-efficient distributed (HEED) and intra-balanced low-energy adaptive clustering hierarchy (IBLEACH), respectively. It increases the network lifetime by 122.92 and 49.53% over the HEED and IBLEACH, respectively, while considering data aggregation.

Multi-hop Routing Algorithm Using Steiner Points for Reducing Energy Consumption in Wireless Sensor Networks

Energy efficiency in wireless sensor networks (WSNs) is one of the most important factors influencing the networks' performance. Energy efficiency is recognized as a critical problem in WSNs so it is important to minimize the total energy consumed by the nodes in a round. Many routing protocols have been proposed to extend networks lifetime through finding energy efficient routing paths. In his paper, we propose a multi-hop routing energy efficient scheme that considers residual energy and distance factors to develop optimal clusters and cluster heads for achieving efficiency and prolonging network lifetime. By MRRCE, rounds in which the first node dies and the last node dies are greatly increased. Moreover, the simulation results prove low energy consumption and lifetime increase. As another contribution of the paper, some algorithms like Low Energy Adaptive Clustering Hierarchy, Hybrid Energy Efficient Distributed (HEED), Extended HEED, DIRECT, Two-Dimensional Technique Based on Center of Gravity and Energy Center, Energy-Aware Evolutionary Routing Protocol and Energy-Efficient Unequal Clustering Mechanism have been analyzed.

Radially Optimized Zone-Divided Energy-Aware Wireless Sensor Networks (WSN) Protocol Using BA (Bat Algorithm)

ABSTRACTThe problems of hot spot and energy consumption of nodes in wireless sensor networks (WSNs) have been tackled by the adoption of hybrid energy efficient distributed (HEED) and unequal clustering algorithm to prolong the network lifetime (UCAPN) protocols. These have involved the implementation of unequally sized clusters that are based on the distance of cluster head (CH) from the base station (BS). The BS partitions the network area into several radially divided zones depending on the distance from centre of the field and CHs are independently selected in each zone. Clustering, on the other hand, controls how data are transmitted and allows for aggregation in the clusters. However, whereas these topologies have been able to improve network life time, the problem of network void still persists. To address this, a radially optimized zone-divided energy-aware WSN protocol using bat algorithm is proposed. This protocol considers not only the distance from the BS, but also the angle at which the WSN develops. It gives a unique and equal opportunity for each sensor network to have ambience area for communicating and sensing necessary for its routing operations. Besides, it divides the sensing area into radial zones and employs the unequal clustering method in these zones to resolve the hot-spot problem. Based on the merits of zone-divided networks, CHs with great residual energy present in the high-energy regions are selected as the relay node in the inter-cluster multi-hop routing stage. Simulation results show that the proposed technique outperforms both HEED and UCAPN protocols. Bat algorithm is capable of predicting most critical nodes and generates a scalable and feasible energy-efficient routing technique for radially optimized zone-divided energy-aware WSN. By exploiting the angle at which the WSN develops, this protocol avails equal opportunity for each sensor network to have enough fields for communicating and sensing necessary for its routing operations. Using bat algorithm not only gives a superior packet delivery ratio, hence reducing voids, but also saves on energy consumption at the nodes. Sensor nodes are grouped into clusters to improve network performance.

An HEED-Based Study of Cell-Clustered Algorithm in Wireless Sensor Network for Energy Efficiency

Unbalanced energy consumption of sensor nodes in wireless sensor network (WSN) leads to the formation of hotspots that can shorten network lifetime. Based on the study of hybrid energy-efficient distributed (HEED) clustering, a cell-clustered algorithm for energy efficiency (CC-HEED) is proposed to save the limited network energy in WSN. According to the power consumption model, the inner cluster regions in this hierarchy network are further divided into several cell-shaped areas, in which cell nodes are brought out to assemble the data in each cell area. In addition, comparisons are performed on network lifetime with other traditional routing algorithms to verify the validity of the routing protocol. Simulation results show that the proposed CC-HEED can significantly reduce energy consumption in WSN, and network lifetime is prolonged notably with high energy efficiency.

Heterogeneous HEED Protocol for Wireless Sensor Networks

One of the important protocols for increasing the network lifetime in wireless sensor networks (WSNs) is hybrid energy efficient distributed (HEED) protocol. This protocol considers two parameters for deciding the cluster heads, i.e., residual energy and node density and has been designed for the homogeneous WSNs. In this paper, we consider the implementation of HEED for a heterogeneous network. Depending upon the type of nodes, it defines one-level, two-level, and three-level heterogeneity and accordingly the implementation of HEED is referred to as hetHEED-1, hetHEED-2, and hetHEED-3, respectively. We also consider one more parameter, i.e., distance and apply fuzzy logic to determine the cluster heads and accordingly the hetHEED-1, hetHEED-2, and hetHEED-3 are named as HEED-FL, hetHEED-FL-2, hetHEED-FL-3, respectively. The simulation results show that as the level of heterogeneity increases in the network, the nodes remain alive for longer time and the rate of energy dissipation decreases. And also, increasing the heterogeneity level helps sending more packets to the base station and increases the network lifetime. The increase in the network energy increases the network lifetime manifold. In fact, using fuzzy logic, the network lifetime increases by 114.85 % that of the original HEED without any increase in the network energy. Thus, the hetHEED-FL-3 provides the longest lifetime (387.94 % increase) in lifetime at the cost of 19 % increase in network energy), sends maximum number of packets to the base station, and has minimum rate of energy dissipation.

Smart secure HEED for WSN

Clustering in wireless sensor networks (WSNs) attracted academia and industry alike due to the wide range of their applications. For the limited resources of WSNs, many algorithms have been proposed for efficient clustering. One of the most famous clustering algorithms is hybrid energy efficient distributed (HEED) protocol. At the same time, security is another challenge due to the critical information transferred. In this paper, we propose a modified HEED, entitled S2HEED, in which a fuzzy logic is used to enhance the cluster head selection criteria for efficient distribution in the field. In addition, we propose a security framework that can be used along with HEED. Based on extensive experiments, our proposals in this paper show that fuzzy logic approach prolongs the network lifetime and the proposed framework defend the network from different attacks like Sinkhole, Sybil attack, Hello flooding attack and selective forwarding in addition to the replay attack.

An Energy-Efficient Cluster-Head Selection Algorithm for Wireless Sensor Networks

Wireless sensor netwo rk has a wide range of applications in vario us fields, extending rapidly its area due to the development of technologies both in sensor and IT. However, technology of efficient battery energy utilization cannot catch up with the rapidly developing technology in releva nt fields. Therefore, energy efficiency becomes the critical factor in wireless sensor networks that requires more attention. Clustering technology is emerging as a promising method to solve efficient energy utilization problem and various cluster-head selection techniques are now under research. This paper suggests an energy efficient cluster-head selection algorithm considering the optimal number of cluster-heads and transmission energy. Our proposed scheme selects cluster-head by computing communi cation cost in probability by adopting distance between base station and n odes to assure an o ptimal number of clusters for a given wireless sensor network. With rapidly increasing amount of information produced by the Internet users along with the constant information technology development, clustering methods are gaining more importance. Clustering technology classifies a given data into significant subgroups, minimizes energy consumption, and maximizes the life span of a network by using limited resources efficiently in various fields such as data analysis, visualization, compression, and data processing. The important goal of clustering technology research is t o extend the l ife span o f a network by distributing energy consumption fairly among all nodes. Among many clustering approaches to extend the life span of a network there are two typical methods, now under research, a cluster-head selection and optimization of the number of cluster-heads or clusters. The typical head selection methods include LEACH-C (Low-Energy Adaptive Clustering Hierarchy-Centralized) (1), EACHS (Energy Adaptive Cluster-head Selection) (2), HEED (Hybrid Energy Efficient Distributed Clustering) (3). But, LEACH- C requires a big initial cost for hardware and EACHS bears a trans mission cost to acknowledge a quantity of energy, while HEED has no guarantee on the number of cluster-heads. This research suggests the energy efficient cluster-head selection technique (EECHS), an efficient header selection algorithm that takes into account the optimal number of cluster-heads and the transmission energy. E ECHS adopts distance in formation between a n ode and base station (BS) as communication cost to ensure the optimal number of clusters. A proposed cluster-head selection method operates by computing communication cost as internal information probability to save overall communication or transmission energy. The rest of this paper consists of following chapters. Chapter 2 d escribes related cluster header selection techniques and examines HEED, the basis of selection method for EECHS, in more detail. Chapter 3 explains the cluster-head selection technique of EECHS, Chapter 4 identifies performance of EECHS whose technique suggested through analysis on the test result using a simulator, and Chapter 5 deduces a conclusion.