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 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.     

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.     

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.     

A grid based clustering and routing algorithm for solving hot spot problem in wireless sensor networks

Energy conservation of the sensor nodes is the most important issue that has been studied extensively in the design of wireless sensor networks (WSNs). In many applications, the nodes closer to the sink are overburdened with huge traffic load as the data from the entire region are forwarded through them to reach the sink. As a result, their energy gets exhausted quickly and the network is partitioned. This is commonly known as hot spot problem. Moreover, sensor nodes are prone to failure due to several factors such as environmental hazards, battery exhaustion, hardware damage and so on. However, failure of cluster heads (CHs) in a two tire WSN is more perilous. Therefore, apart from energy efficiency, any clustering or routing algorithm has to cope with fault tolerance of CHs. In this paper, we address the hot spot problem and propose grid based clustering and routing algorithms, combinedly called GFTCRA (grid based fault tolerant clustering and routing algorithms) which takes care the failure of the CHs. The algorithms follow distributed approach. We also present a distributed run time management for all member sensor nodes of any cluster in case of failure of their CHs. The routing algorithm is also shown to tolerate the sudden failure of the CHs. The algorithms are tested through simulation with various scenarios of WSN and the simulation results show that the proposed method performs better than two other grid based algorithms in terms of network lifetime, energy consumption and number of dead sensor nodes.     

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.     

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.     

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 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.     

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.     

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.     

WCDS‐DCR: an energy‐efficient data‐centric routing scheme for wireless sensor networks

Wireless sensor networks (WSNs) typically consist of a large number of battery‐constrained sensors often deployed in harsh environments with little to no human control, thereby necessitating scalable and energy‐efficient techniques. This paper proposes a scalable and energy‐efficient routing scheme, called WCDS‐DCR, suitable for these WSNs. WCDS‐DCR is a fully distributed, data‐centric, routing technique that makes use of an underlying clustering structure induced by the construction of WCDS (Weakly Connected Dominating Set) to prolong network lifetime. It aims at extending network lifetime through the use of data aggregation (based on the elimination of redundant data packets) by some particular nodes. It also utilizes both the energy availability information and the distances (in number of hops) from sensors to the sink in order to make hop‐by‐hop, energy‐aware, routing decisions. Simulation results show that our solution is scalable, and outperforms existing schemes in terms of network lifetime. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks

In wireless sensor networks (WSNs), clustering can significantly reduce energy dissipation of nodes, and also increase communication load of cluster heads. When multi-hop communication model is adopted in clustering, “energy hole” problem may occur due to unbalanced energy consumption among cluster heads. Recently, many multi-hop clustering protocols have been proposed to solve this problem. And the main way is using unequal clustering to control the size of clusters. However, many of these protocols are about homogeneous networks and few are about heterogeneous networks. In this paper, we present an unequal cluster-based routing scheme for WSNs with multi-level energy heterogeneity called UCR-H. The sensor field is partitioned into a number of equal-size rectangular units. We first calculate the number of clusters in each unit by balancing energy consumption among the cluster heads in different units. And then we find the optimal number of units by minimizing the total energy consumption of inter-cluster forwarding. Finally, the size of clusters in each unit is elaborately designed based on node’s energy level and the number of clusters in this unit. And a threshold is also designed to avoid excessive punishment to the nodes with higher energy level. Simulation results show that our scheme effectively mitigates the “energy hole” problem and achieves an obvious improvement on the network lifetime.     

基于改进LEACH的多簇头分簇路由算法

为了降低无线传感器网络(WSN)的能耗,延长网络的生存周期,提出一种多簇头双工作模式的分簇路由算法.算法对低功耗自适应集簇分层(LEACH)协议作了以下改进:采用多簇头双工作模式来分担单簇头的负荷,以解决单簇头因能耗较大而过早消亡的问题;选举簇头时充分考虑节点位置和节点剩余能量,并应用粒子群优化(PSO)算法优化簇头的选举,以均衡网络内各节点的能耗;建立簇与簇之间的数据传输路由,以减少簇间通信的能耗.仿真结果表明,算法有效降低了网络的能耗,延长了网络的生存周期.     

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.     

Modulated relay based stable election protocol for large-scale wireless sensor networks

Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.