Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks

Energy conserving of sensor nodes is the most crucial issue in the design of wireless sensor networks (WSNs). In a cluster based routing approach, cluster heads (CHs) cooperate with each other to forward their data to the base station (BS) via multi-hop routing. In this process, CHs closer to the BS are burdened with heavier relay traffic and tend to die prematurely which causes network partition is popularly known as a hot spot problem. To mitigate the hot spot problem, in this paper, we propose unequal clustering and routing algorithms based on novel chemical reaction optimization (nCRO) paradigm, we jointly call these algorithms as novel CRO based unequal clustering and routing algorithms (nCRO-UCRA). In clustering, we partition the network into unequal clusters such that smaller size clusters near to the sink and larger size clusters relatively far away from the sink. For this purpose, we develop the CH selection algorithm based on nCRO paradigm and assign the non-cluster head sensor nodes to the CHs based on derived cost function. Then, a routing algorithm is presented which is also based on nCRO based approach. All these algorithms are developed with the efficient schemes of molecular structure encoding and novel potential energy functions. The nCRO-UCRA is simulated extensively on various scenarios of WSNs and varying number of sensors and the CHs. The results are compared with some existing algorithms and original CRO based algorithm called as CRO-UCRA to show the superiority in terms of various performance metrics like residual energy, network lifetime, number of alive nodes, data packets received by the BS and convergence rate.     

A green cluster‐based routing scheme for large‐scale wireless sensor networks

In wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering‐based routing algorithms that can balance out the trade‐off between load distribution and network lifetime “green cluster‐based routing scheme.” This paper proposes a new energy‐aware green cluster‐based routing algorithm to preventing premature death of large‐scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule‐based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme.     

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.     

Fuzzy chessboard clustering and artificial bee colony routing method for energy‐efficient heterogeneous wireless sensor networks

Energy is an extremely critical resource for battery‐powered wireless sensor networks (WSNs), thus making energy‐efficient protocol design a key challenging problem. However, uneven energy consumption is an inherent problem in WSNs caused by multi‐hop routing and many‐to‐one traffic pattern among sensors. In this paper, we therefore propose a new clustering method called fuzzy chessboard clustering (FFC), which is capable to overcome the bottleneck problem and addressing the uneven energy consumption problem in heterogeneous WSNs. We also propose an energy‐efficient routing method called artificial bee colony routing method (ABCRM) to find the optimal routing path for the heterogeneous WSNs. ABCRM seeks to investigate the problems of balancing energy consumption and maximization of network lifetime. To demonstrate the effectiveness of FCC‐ABCRM in terms of lessening end‐to‐end delay, balancing energy consumption, and maximization of heterogeneous network lifetime, we compare our method with three approaches namely, chessboard clustering approach, PEGASIS, and LEACH. Simulation results show that the network lifetime achieved by FCC‐ABCRM could be increased by nearly 25%, 45%, and 60% more than that obtained by chessboard clustering, PEGASIS, and LEACH, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

Secure coverage-preserving node scheduling scheme using energy prediction for wireless sensor networks

With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.     

An Energy-Efficient Routing Algorithm Based on Relative Identification and Direction for Wireless Sensor Networks

In studies of wireless sensor networks (WSNs), routing protocols in network layer is an important topic. To date, many routing algorithms of WSNs have been developed such as relative direction-based sensor routing (RDSR). The WSNs in such algorithm are divided into many sectors for routing. RDSR could simply reduce the number of routes as compared to the convention routing algorithm, but it has routing loop problem. In this paper, a less complex, more efficient routing algorithm named as relative identification and direction-based sensor routing (RIDSR) algorithm is proposed. RIDSR makes sensor nodes establish more reliable and energy-efficient routing path for data transmission. This algorithm not only solves the routing loop problem within the RDSR algorithm but also facilitates the direct selection of a shorter distance for routing by the sensor node. Furthermore, it saves energy and extends the lifetime of the sensor nodes. We also propose a new energy-efficient algorithm named as enhanced relative identification and direction-based sensor routing (ERIDSR) algorithm. ERISDR combines triangle routing algorithm with RIDSR. Triangle routing algorithm exploits a simple triangle rule to determine a sensor node that can save more energy while relaying data between the transmitter and the receiver. This algorithm could effectively economize the use of energy in near-sensor nodes to further extend the lifetime of the sensor nodes. Simulation results show that ERIDSR get better performance than RDSR, and RIDSR algorithms. In addition, ERIDSR algorithm could save the total energy in near-sensor nodes more effectively.     

An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network

This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node’s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node’s chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC.     

A Review on Hierarchical Routing Protocols for Wireless Sensor Networks

The routing protocol for Wireless Sensor Networks (WSNs) is defined as the manner of data dissemination from the network field (source) to the base station (destination). Based on the network topology, there are two types of routing protocols in WSNs, they are namely flat routing protocols and hierarchical routing protocols. Hierarchical routing protocols (HRPs) are more energy efficient and scalable compared to flat routing protocols. This paper discusses how topology management and network application influence the performance of cluster-based and chain-based hierarchical networks. It reviews the basic features of sensor connectivity issues such as power control in topology set-up, sleep/idle pairing and data transmission control that are used in five common HRPs, and it also examines their impact on the protocol performance. A good picture of their respective performances give an indication how network applications, i.e whether reactive or proactive, and topology management i.e. whether centralized or distributed would determine the network performance. Finally, from the ensuring discussion, it is shown that the chain-based HRPs guarantee a longer network lifetime compared to cluster-based HRPs by three to five times.     

基于TinyOS的无线传感器网络LEACH算法的改进

LEACH协议是无线传感器网络中最流行的分簇路由协议之一.针对LEACH算法簇分布不均匀以及网络能耗不均衡等问题提出了一种高效节能多跳路由算法.在簇建立阶段,新算法根据网络模型计算出最优簇头间距值,调整节点通信半径以控制簇的大小,形成合理网络拓扑结构;在数据传输阶段,簇头与基站之间采用多跳的通信方式,降低了节点能耗.在TinyOS操作系统下,使用nesC语言设计实现了LEACH-EEMH算法.基于TOSSIM平台的仿真结果表明,新算法较LEACH算法在均衡网络能耗、延长网络寿命方面具有显著优势.     

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%。     

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.

     

Load balancing clustering and routing for IoT-enabled wireless sensor networks

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy-efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c-means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO-based clustering (GWO-C) and Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.     

Sink Node Placement Strategies for Wireless Sensor Networks

In wireless sensor networks (WSNs), all the data collected by the sensor nodes are forwarded to a sink node. Therefore, the placement of the sink node has a great impact on the energy consumption and lifetime of WSNs. This paper investigates the energy-oriented and lifetime-oriented sink node placement strategies in the single-hop and multiple-hop WSNs, respectively. The energy-oriented strategy considers only the minimizing of the total energy consumption in the networks, while the lifetime-oriented strategy focuses much more on the lifetime of the nodes which consume energy fastest. Using a routing-cost based ant routing algorithm, we evaluate the performances of different placement strategies in the networks. Simulation results show that the networks with lifetime-oriented strategy achieve a significant improvement on network lifetime.     

Deterministic K‐means secure coverage clustering with periodic authentication for wireless sensor networks

Innovative and emerging developments in sensor networks are proven to be the backbone for real‐time applications such as satellite communications, military and border area surveillance systems, health care systems, traffic monitoring systems, seismic and underwater monitoring systems, and agriculture and habitat environment systems. Coverage and clustering techniques enable the sensor network to operate in group‐based and region‐based communication and thus save the node energy. Energy‐efficient protocols save the node energy and increase the network life cycle in a resource‐constrained sensor network. Cluster head (CH) node manages and controls the operations such as network topology, coverage area, and routing paths (multi‐paths and fault‐tolerant paths) of the network. In this paper, we present deterministic K‐means secure coverage clustering (K‐SCC) with periodic authentication. The proposed protocol uses coverage clustering technique with periodic authentication between the CH node and sensor nodes to establish the secure channel in the network. Maximum cover of K nodes is maintained in the secure coverage cluster to achieve authenticated communication between the sensor nodes in the network. The proposed K‐SCC protocol is compared with the existing protocols such as deterministic‐SCC and random‐SCC protocols. Simulation results indicate that the proposed K‐SCC protocol achieves an average of 84% coverage ratio (cluster/sensor node ratio) as compared with 62% coverage ratio in the existing SCC protocols. Simulations also indicate that the proposed K‐SCC protocol consumes 20% less energy as compared with the existing SCC protocol. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

An efficient UE cluster head selection algorithm in wireless sensor networks and cellular networks

Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.     

Double Cluster Based Energy Efficient Routing Protocol for Wireless Sensor Network

Reducing the energy consumption of sensor nodes and prolonging the life of the network is the central topic in the research of wireless sensor network (WSN) protocol. The low-energy adaptive clustering hierarchy (LEACH) is one of the hierarchical routing protocols designed for communication in WSNs. LEACH is clustering based protocol that utilizes randomized rotation of local cluster-heads to evenly distribute the energy load among the sensors in the network. But LEACH is based on the assumption that each sensor nodes contain equal amount of energy which is not valid in real scenarios. A developed routing protocol named as DL-LEACH is proposed. The DL-LEACH protocol cluster head election considers residual energy of nodes, distance from node to the base station and neighbor nodes, which makes cluster head election reasonable and node energy consumption balance. The simulation results of proposed protocols are compared for its network life time in MATLAB with LEACH protocol. The DL-LEACH is prolong the network life cycle by 75 % than LEACH.     

无线传感器网络分簇路由协议研究

在无线传感器网络中,分簇型路由在路由协议中占据重要的地位,该协议方便拓扑结构管理,能源利用率高,数据融合简单。文章从簇头生成、簇形成和簇路由3个角度对典型的分簇路由算法LEACH,HEED,EEUC,PEGASIS进行了系统描述,从网络生命周期和节点存活数量等方面,对比了其优缺点,结合该领域的研究现状,指出了未来研究的方向。     

Stable-Aware Evolutionary Routing Protocol for Wireless Sensor Networks

In real life scenario for wireless sensor networks (WSNs), energy heterogeneity among the sensor nodes due to uneven terrain, connectivity failure, and packet dropping is a crucial factor that triggered the race for developing robust and reliable routing protocols. Prolonging the time interval before the death of the first sensor node, viz. the stability period, is critical for many applications where the feedback from the WSN must be reliable. Although Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-like protocols are fundamental and popular clustering protocols to manage the system’s energy and thus to prolong the lifespan of the network, they assume a near to a perfect energy homogeneous system where a node failure, drainage and re-energizing are typically not considered. More recent protocols like Stable Election Protocol (SEP) considers the reverse, i.e., energy heterogeneity, and properly utilizes the extra energy to guarantee a stable and reliable performance of the network system. While paradigms of computational intelligence such as evolutionary algorithms (EAs) have attracted significant attention in recent years to address various WSN’s challenges such as nodes deployment and localization, data fusion and aggregation, security and routing, they did not (to the best of our knowledge) explore the possibility of maintaining heterogeneous-aware energy consumption to guarantee a reliable and robust routing protocol design. By this, a new protocol named stable-aware evolutionary routing protocol (SAERP), is proposed in this paper to ensure maximum stability and minimum instability periods for both homogeneous/heterogeneous WSNs. SAERP introduces an evolutionary modeling, where the cluster head election probability becomes more efficient, to well maintain balanced energy consumption in both energy homogeneous and heterogeneous settings. The performance of SAERP over simulation for 90 WSNs is evaluated and compared to well known LEACH and SEP protocols. We found that SAERP is more robust and always ensures longer stability period and shorter instability period.     

Increasing network lifetime by balancing node energy consumption in heterogeneous sensor networks

Sensor nodes are powered by battery and have severe energy constraints. The typical many‐to‐one traffic pattern causes uneven energy consumption among sensor nodes, that is, sensor nodes near the base station or a cluster head have much heavier traffic burden and run out of power much faster than other nodes. The uneven node energy dissipation dramatically reduces sensor network lifetime. In a previous work, we presented the chessboard clustering scheme to increase network lifetime by balancing node energy consumption. To achieve good performance and scalability, we propose to form a heterogeneous sensor network by deploying a few powerful high‐end sensors in addition to a large number of low‐end sensors. In this paper, we design an efficient routing protocol based on the chessboard clustering scheme, and we compute the minimum node density for satisfying a given lifetime constraint. Simulation experiments show that the chessboard clustering‐based routing protocol balances node energy consumption very well and dramatically increases network lifetime, and it performs much better than two other clustering‐based schemes. Copyright © 2006 John Wiley & Sons, Ltd.