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 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.     

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.     

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.     

A fault‐tolerant energy‐efficient clustering protocol of a wireless sensor network

Energy efficiency in specific clustering protocols is highly desired in wireless sensor networks. Most existing clustering protocols periodically form clusters and statically assign cluster heads (CHs) and thus are not energy efficient. Every non‐CH node of these protocols sends data to the CH in every time slot of a frame allocated to them using the time division multiple access scheme, which is an energy‐consuming process. Moreover, these protocols do not provide any fault tolerance mechanism. Considering these limitations, we have proposed an efficient fault‐tolerant and energy‐efficient clustering protocol for a wireless sensor network. The performance of the proposed protocol was tested by means of a simulation and compared against the low energy adaptive clustering hierarchy and dynamic static clustering protocols. Simulation results showed that the fault‐tolerant and energy‐efficient clustering protocol has better performance than both the low energy adaptive clustering hierarchy and dynamic static clustering protocols in terms of energy efficiency and reliability. Copyright © 2012 John Wiley & Sons, Ltd.     

A Genetic‐Algorithm‐Based Optimized Clustering for Energy‐Efficient Routing in MWSN

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy‐efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near‐optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near‐optimal energy‐efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy‐efficient routing technique produces a longer network lifetime and achieves better energy efficiency.     

Rendezvous-based data dissemination for supporting mobile sinks in multi-hop clustered wireless sensor networks

In wireless sensor networks, a clustering-based technique is considered as an efficient approach for supporting mobile sinks without using position information. It exploits a Backbone-based Virtual Infrastructure (BVI) which uses only cluster heads (CHs) to construct routing structures. Since sensor nodes have constrained energy and are failure-prone, the effective design of both a clustering structure to construct a BVI and a routing protocol in the BVI is an important issue to achieve energy-efficient and reliable data delivery. However, since previous studies use one-hop clustering for a BVI, they are not robust against node and link failures and thus leading low data delivery ratio. They also use flooding-based routing protocols in a BVI and thus leading high energy consumption. Thus, in this paper, we propose a rendezvous-based data dissemination protocol based on multi-hop clustering (RDDM). Since RDDM uses a multi-hop clustering to provide enough backup sensor nodes to substitute a CH and enough backup paths between neighbor CHs, it can provide high robustness against node and link failures. By using a rendezvous CH, RDDM constructs routing paths from source nodes to mobile sinks without flooding in our BVI and thus can save energy of sensor nodes. By considering movement types of sinks, RDDM finds out a shorter path between a source node and a mobile sink through signaling only between neighbor CHs and thus can reduce the energy consumption. Analysis and simulation results show that RDDM provides better performance than previous protocols in terms of energy consumption and data delivery ratio.     

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.     

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.     

ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

     

DACOR: A distributed ACO-based routing protocol for mitigating the hot spot problem in fog-enabled WSN architecture

Ant colony optimization (ACO) and unequal clustering algorithms in wireless sensor networks (WSNs) prove their efficiency in protracting the network lifetime. However, the existing ACO and unequal clustering algorithms, respectively, do not consider jointly energy efficiency and reliability and focus only on some normal parameters to adjust the cluster radius, then neglecting the cluster head (CH) neighborhood information as it is wise to reduce the cluster radius when there are more neighbor CHs in order to balance the load and energy consumption. To resolve these problems, we propose a fault-tolerant distributed ACO-based routing (DACOR) protocol for mitigating the hot spot problem in fog-enabled WSN architecture. To improve the performance of the network, we propose a multiple fog nodes (FNs) and unequal clustering-based network model. The proposed model is energy efficient as it avoids repetitive clustering and affects CHs to FNs based on distance. Also, unlike the existing works which use either single FN/sink-based unequal clustering or multiple FNs/sinks to mitigate hot spot problem, we propose to distribute unequal clustering to multiple FNs (partitions). Additionally, we formulate a different rule to calculate the cluster radius based on significant parameters ensuring energy efficiency and balancing. To route data from source to destination, we devise a new probabilistic formula which considers not only energy efficiency but also reliability. The performance of the proposed DACOR protocol has been investigated under different scenarios through simulations. The results show that the proposed DACOR protocol outperforms the existing protocols in terms of various main metrics.     

A modified whale-dragonfly algorithm and self-adaptive cuckoo search-based clustering strategy for augmenting network lifetime in wireless sensor networks

Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale-dragonfly optimization algorithm and self-adaptive cuckoo search-based clustering strategy (MWIDOA-SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA-SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self-adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA-SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.     

An efficient routing protocol for the QoS support of large‐scale MANETs

The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large‐scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table‐driven and on‐demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end‐to‐end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state‐of‐the‐art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic‐Based Clustering, Dynamic Link Duration Clustering, and mobility‐based clustering algorithms.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named “Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)” to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named “Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)” and “Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT).” Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.     

Energy‐efficient multihop routing in WSN using the hybrid optimization algorithm

The technical growth in the field of the wireless sensor networks (WSNs) has resulted in the process of collecting and forwarding the massive data between the nodes, which was a major challenge to the WSNs as it is associated with greater energy loss and delay. This resulted in the establishment of a routing protocol for the optimal selection of the multipath to progress the routing in WSNs. This paper proposes an energy‐efficient routing in WSNs using the hybrid optimization algorithm, cat–salp swarm algorithm (C‐SSA), which chooses the optimal hops in progressing the routing. Initially, the cluster heads (CHs) are selected using the low‐energy adaptive clustering hierarchy (LEACH) protocol that minimizes the traffic in the network. The CHs are engaged in the multihop routing, and the selection of the optimal paths is based on the proposed hybrid optimization, which chooses the optimal hops based on the energy constraints, such as energy, delay, intercluster distance, intracluster distance, link lifetime, delay, and distance. The simulation results prove that the proposed routing protocol acquired minimal delay of 0.3165 with 50 nodes and two hops, maximal energy of 0.1521 with 50 nodes and three hops, maximal number of the alive nodes as 39 with 100 nodes and two hops, and average throughput of 0.9379 with 100 nodes and three hops.     

Adaptive concentric chains protocol for energy efficient routing in wireless sensor networks

This paper addresses the energy efficiency of data collection based on a concentric chain clustering topology for wireless sensor networks (WSNs). To conserve the energy dissipation of nodes spent in data routing, the paper attempts to take advantage of the two opportunities: (a) the impact of the relative positions of wireless nodes to the base station on the energy efficiency of the routing chain within each cluster; (b) the effect of the varying‐sized chains on the selection rule of cluster heads (CHs). To establish an energy‐efficient chain to connect all the nodes in a cluster, the paper proposes a principal vector projection approach, which takes into account both the position of each node and that of the base station, to determine the order to which a node can be linked into the chain in order to reduce the energy requirement of the chain. Since the CH selection rules in the concentric chains are mutually independent, solely based on their self‐cluster sizes, the multi‐hop path passing through all the CHs will consist of longer links and thus consume a significant fraction of the total energy. Thus, in order to suppress the effect of the unequal cluster sizes on decreasing the energy efficiency of the multi‐hop path of CHs, the paper offers an average‐cluster‐size‐based rule (ACSB) for each cluster in order to adapt the CH selection with both the number of active nodes in the current cluster and the average value of all cluster sizes. With these two proposed schemes, an adaptive concentric chain‐based routing algorithm is proposed which enables nodes to collaboratively reduce the energy dissipation incurred in gathering sensory data. By computer simulation, the results demonstrate that the proposed algorithm performs better than other similar protocols in terms of energy saved and lifetime increased capabilities for WSNs which deploy random sensor nodes. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal load balanced clustering in homogeneous wireless sensor networks

Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.     

Performance Analysis of Adaptive Routing Structure for Wireless Sensor Network Based on Load Balancing

Wireless sensor network consists of sensor nodes with battery operated device. The key challenges in the wireless sensor network are energy consumption and routing optimization. This work presents the cluster based load balancing (CBLB) routing protocol. The proposed routing protocol is used to minimize the energy consumption and increase the routing performance. It avoids the routing robustness, delay and increases the delivery rate and network performance. In existing techniques, different routing protocols such as LEACH, HEED and MESTER were used to increase the network performance and to decrease the energy consumption. But these existing techniques did not satisfy the performance requirements of wireless sensor networks. Hence, there is a requirement to develop a technique that meets the QoS requirements and needs of wireless sensor network. The proposed CBLB routing protocol creates a cluster head in the decentralized network and the cluster head will be used to distribute the workload evenly to the cluster members for reducing the energy consumption in wireless sensor network. Experimental results analyze the performance of the proposed protocol with the different existing protocols. The proposed protocol achieves high throughput, delivery rate and reduces the energy consumption, delay and routing overhead.