Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

     

Improved low energy adaptive clustering hierarchy and its optimum cluster head selection

ABSTRACT

Wireless sensor networks (WSNs) play a vital role in present-day world, which are being used in different types of applications and occupy an important part in networking domain. The main objective of WSNs is to sense and collect the information from a given area of interest and provide the gathered data to the sink. WSN comprises of number of sensor nodes with batteries of limited energy for communication and computational activities, which are not possible to recharge the batteries after their deployment in the region of interest. Therefore, saving battery energy and utilising the limited power to the optimum level for extending network lifetime became the main factor of WSN. Hence, optimum cluster head (CH) selection will make the network to support longer lifetime and balanced energy consumption during its lifetime. Our proposed protocol selects the optimum CH and found out to be more efficient than the existing low energy adaptive clustering hierarchy. The simulated output shows better network lifetime and in some other performance metric.     

Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach

Clustering has been accepted as one of the most efficient techniques for conserving energy of wireless sensor networks (WSNs). However, in a two-tiered cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving data from their member sensor nodes, aggregating them and transmitting that data to the base station (BS). Therefore, proper selection of CHs and optimal formation of clusters play a crucial role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient CH selection and energy balanced cluster formation algorithms, which are based on novel chemical reaction optimization technique (nCRO), we jointly called these algorithms as novel CRO based energy efficient clustering algorithms (nCRO-ECA). These algorithms are developed with efficient schemes of molecular structure encoding and potential energy functions. For the energy efficiency, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes in the CH selection phase. In the cluster formation phase, we consider various distance and energy parameters. The algorithm is tested extensively on various scenarios of WSNs by varying number of sensor nodes and CHs. The results are compared with original CRO based algorithm, namely CRO-ECA and some existing algorithms to demonstrate the superiority of the proposed algorithm in terms of energy consumption, network lifetime, packets received by the BS and convergence rate.     

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.     

A novel approach based on bio-inspired efficient clustering algorithm for large-scale heterogeneous wireless sensor networks

In large-scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K-means clustering algorithm and optimised K-means based modified intelligent CH selection based on BFOA for large-scale network (lar-OK-MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK-MICHB algorithm for large-scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar-OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi-level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment.     

A Centralized Balance Clustering Routing Protocol for Wireless Sensor Network

Routing protocol plays a role of great importance in the performance of wireless sensor networks (WSNs). A centralized balance clustering routing protocol based on location is proposed for WSN with random distribution in this paper. In order to keep clustering balanced through the whole lifetime of the network and adapt to the non-uniform distribution of sensor nodes, we design a systemic algorithm for clustering. First, the algorithm determines the cluster number according to condition of the network, and adjusts the hexagonal clustering results to balance the number of nodes of each cluster. Second, it selects cluster heads in each cluster base on the energy and distribution of nodes, and optimizes the clustering results to minimize energy consumption. Finally, it allocates suitable time slots for transmission to avoid collision. Simulation results demonstrate that the proposed protocol can balance the energy consumption and improve the network throughput and lifetime significantly.     

Energy-Efficient Routing Algorithm Based on Unequal Clustering and Connected Graph in Wireless Sensor Networks

Clustering and multi-hop routing algorithms substantially prolong the lifetime of wireless sensor networks (WSNs). However, they also result in the energy hole and network partition problems. In order to balance the load between multiple cluster heads, save the energy consumption of the inter-cluster routing, in this paper, we propose an energy-efficient routing algorithm based on Unequal Clustering Theory and Connected Graph Theory for WSN. The new algorithm optimizes and innovates in two aspects: cluster head election and clusters routing. In cluster head election, we take into consideration the vote-based measure and the transmission power of sensor nodes when to sectionalize these nodes into different unequal clusters. Then we introduce the connected graph theory for inter-cluster data communication in clusters routing. Eventually, a connected graph is constituted by the based station and all cluster heads. Simulation results show that, this new algorithm balances the energy consumption among sensor nodes, relieves the influence of energy-hole problem, improve the link quality, achieves a substantial improvement on reliability and efficiency of data transmission, and significantly prolongs the network lifetime.     

DUCR: Distributed unequal cluster‐based routing algorithm for heterogeneous wireless sensor networks

Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only. Considering these issues, we propose a distributed multiobjective‐based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy‐efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population.     

CH selection and compressive sensing-based data aggregation in WSN using hybrid Golden circle-inspired optimization

The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

Flower pollination algorithm‐based energy‐efficient stable clustering approach for WSNs

The advances in the size, cost of deployment, and user‐friendly interface of wireless sensor devices have given rise to many wireless sensor network (WSN) applications. WSNs need to use protocols for transmitting data samples from event regions to sink through minimum cost links. Clustering is a commonly used method of data aggregation in which nodes are organized into groups to reduce energy consumption. Nonetheless, cluster head (CH) has to bear an additional load in clustering protocols to organize different activities within the cluster. Proper CH selection and load balancing using efficient routing protocol is therefore a critical aspect for WSN's long‐term operation. In this paper, a threshold‐sensitive energy‐efficient cluster‐based routing protocol based on flower pollination algorithm (FPA) is proposed to extend the network's stability period. Using FPA, multihop communication between CHs and base station is used to achieve optimal link costs for load balancing distant CHs and energy minimization. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period, and system lifetime.     

Adaptive energy aware cluster-based routing protocol for wireless sensor networks

Wireless sensor networks (WSNs) have grown excessively due to their various applications and low installation cost. In WSN, the main concern is to reduce energy consumption among nodes while maintaining timely and reliable data forwarding. However, most of the existing energy aware routing protocols incur unbalanced energy consumption, which results in inefficient load balancing and compromised network lifetime. Therefore, the main target of this research paper is to present adaptive energy aware cluster-based routing (AECR) protocol for improving energy conservation and data delivery performance. Our proposed AECR protocol differs from other energy efficient routing schemes in some aspects. Firstly, it generates balance sized clusters based on nodes distribution and avoids random clusters formation. Secondly, it optimizes both intra-cluster and inter-cluster routing paths for improving data delivery performance while balancing data traffic on constructed forwarding routes and at the end, in order to reduce the excessive energy consumption and improving load distribution, the role of Cluster Head (CH) is shifted dynamically among nodes by exploit of network conditions. Simulation results demonstrate that AECR protocol outperforms state of the art in terms of various performance metrics.

     

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

Energy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.     

A Distributed Multi-competitive Clustering Approach for Wireless Sensor Networks

Wireless sensor networks (WSNs) need simple and effective approaches to reduce energy consumption because of limited energy. Clustering nodes is an effective approach to make WSNs energy-efficient. In this paper we propose a distributed multi-competitive clustering approach (DMCC) for WSNs. First, the nodes with high residual energy are selected to act as cluster head candidates (CHCs). Second, cluster heads (CHs) are selected from the CHCs based on a hybrid of competition. If the distances to the selected CHs are suitable, a CHC with more neighbor nodes and smaller average distance to its neighbor nodes is more likely to become a CH. If the number of CHs selected from the CHCs is insufficient, more CHs are selected from non-CHCs continually according to residual energy until the CHs number is suitable. DMCC makes the CHs number stable and distribute the CHs evenly. Simulation experiments were performed on to compare DMCC with some related clustering approaches. The experimental results suggest that DMCC balances the load among different clusters and reduces the energy consumption, which improves the network lifetime.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

Fuzzy logic based clustering in wireless sensor networks: a survey

Wireless sensor networks (WSNs) have limited resources, thus extending the lifetime has always been an issue of great interest. Recent developments in WSNs have led to various new fuzzy systems, specifically designed for WSNs where energy awareness is an essential consideration. In several applications, the clustered WSN are known to perform better than flat WSN, if the energy consumption in clustering operation itself could be minimised. Routing in clustered WSN is very efficient, especially when the challenge of finding the optimum number of intermediate cluster heads can be resolved. Fortunately, several fuzzy logic based solutions have been proposed for these jobs. Both single- and two-level fuzzy logic approaches are being used for cluster head election in which several distinguished features of WSN have been considered in making a decision. This article surveys the recent fuzzy applications for cluster head selection in WSNs and presents a comparative study for the various approaches pursued.     

A Secure Routing Protocol with Trust and Energy Awareness for Wireless Sensor Network

Nodes in most of the deployments of Wireless Sensor Networks (WSNs) remain un-administered and exposed to variety of security attacks. Characterized by constrained resources and dynamically changing behavior of sensor nodes, reliable data delivery in WSNs is nontrivial. To counter node misbehavior attacks, traditional cryptographic and authentication based solutions have proved to be inappropriate due to high cost and incapability factors. Recently, trust based solutions have appeared to be viable solutions to address nodes’ misbehavior attacks. However, the existing trust based solutions incur high cost in trust estimation and network-wide dissemination which significantly increases traffic congestion and undermines network lifetime. This paper presents a Trust and Energy aware Secure Routing Protocol (TESRP) for WSN that exploits a distributed trust model for discovering and isolating misbehaving nodes. TESRP employs a multi-facet routing strategy that takes into consideration the trust level, residual energy, and hop-counts of neighboring nodes while making routing decisions. This strategy not only ensures data dissemination via trusted nodes but also balances out energy consumption among trusted nodes while traversing through shorter paths. Demonstrated by simulation results in NS-2, TESRP achieves improved performance in terms of energy consumption, throughput and network lifetime as compared to existing solutions.     

基于繁忙因子的VVSN自适应双簇头分簇算法研究

在WSN中,节点之间不平衡通信消耗大量能量,因此网络生存时间较短。为改善网络整体生存时间,提出一种基于网络繁忙因子的簇头自适应切换算法。首先,算法一次性选举双簇头,再根据网络实际情况自适应切换簇头。然后,在簇头选举完成后,节点通过当前簇头与基站通信,若当前簇头的能量低于门限值,则网络中的当前簇头将被切换到次级簇头,此时节点通过次级簇头与基站通信,从而降低节点能耗,减少节点的死亡率。双簇头切换机制缩短了整体通信距离,缓解簇头节点过早死亡,使网络生存时间增长。仿真结果表明,该算法通过缩短整体通信距离可显著降低整体网络的能量消耗,增加网络的生存时间。     

DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster‐head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well‐known algorithms.