An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.     

An optimised fuzzy clustering for wireless sensor networks

Minimising energy consumption has always been an issue of crucial importance in sensor networks. Most of the energy is consumed in data transmission from sensor nodes to the base station due to the long distance of nodes from the base station. In the recent past, a number of researchers have proposed that clustering is an efficient way of reducing the energy consumption during data transmission and enhancing the lifetime of wireless sensor networks. Many algorithms have been already proposed for cluster head selection. In this work, we analyse and compare the lifetime of the network with three different fuzzy-based approaches of cluster head selection. The three strong parameters which play an important role in lifetime enhancement – energy, centrality and node density – are considered for cluster head selection in our proposed fuzzy approaches. In the first approach, energy and centrality are considered simultaneously in a fuzzy system to select the cluster heads. In the second approach, energy and node density have been taken in a fuzzy system to select the cluster heads. In the third approach, node density and centrality are considered simultaneously by a fuzzy system to select the cluster heads. Simulation results of these fuzzy logic-based approaches show that all the three approaches are superior to the Low-Energy Adaptive Clustering Hierarchy (LEACH). Simulation results also show that the energy-centrality-based fuzzy clustering scheme gives best performance among all the three fuzzy-based algorithms and it enhances the lifetime of wireless sensor networks by a significant amount.     

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.     

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.     

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.     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.     

基于能量和密度的WSNs动态分区成簇路由算法

针对传统的层次型网络存在的分簇不合理和能耗不均衡等问题,提出了一种基于能量和密度的动态非均匀分区成簇路由算法。该算法先根据节点与基站之间的距离将网络合理地进行动态的区域划分,在区域内成簇,使靠近基站的簇规模小于距离基站较远的簇,减少靠近基站的簇首负担和能量消耗;通过综合考虑节点剩余能量和节点密度等因素来优化簇的非均匀划分和簇首的选择,簇首间采取基于数据聚合的多跳传输机制。仿真结果表明,与经典路由算法LEACH相比,该算法能有效均衡节点能耗,延长网络生命周期。     

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

Software defined wireless sensor network (SDWSN) is a recent evolution in networking that improves network performance and scalability. However, Quality of Service (QoS) and security are major the issues in SDWSN due to inefficient route selection (traffic load minimization algorithm) and insecure cryptography scheme (homomorphic algorithm). This paper proposes novel three‐tier architecture for secure cluster‐based SDWSN (SeC‐SDWSN) environment to ensure QoS and security for WSN using SDN. In the first tier, sensor nodes are segregated into multiple clusters by secure hash tree‐based clustering (SHTC) algorithm. Within each secure cluster, data transmission is performed through optimal route selected by adaptive spider monkey optimization (ASMO) algorithm in which two new fitness factors (F₁, F₂ ) are formulated by multiple QoS metrics. For data security, parallel advanced encryption standard with cipher block chaining (PAES‐CBC) algorithm is proposed. Aggregated ciphertext is transmitted to optimal switch in the second tier by using fuzzy weighted technique for order preference by similarity to ideal solution (FW‐TOPSIS) algorithm according to selection criteria. Switches forward the data to sink node based on flow rules deployed by SDN controllers in the third tier. SDN controllers provide global view on the entire network and deploy flow rules on switches in accordance to network status and security level. Extensive simulation in ns‐3 shows that the proposed three‐tier architecture achieves 5% throughput improvement, 7.8% PDR improvement, and 16% energy consumption improvement.     

一种针对无线传感器网络LEACH协议的改进算法

针对传统LEACH协议在簇首选取的随意性,以及簇首节点将数据以单跳形式传输给汇聚节点造成能耗大的缺点。文中提出了改进协议,该算法在对簇头节点的选择时会将节点的剩余能量考虑进去,会在选择剩余能量最多,同时以其到汇聚节点距离小的节点作为下一跳来传输数据,以实现多个簇之间的路由数据传输。通过Matlab仿真可以知道,改进后的协议使整个传感器网络的能量消耗变得更加均衡,同时使整个网络的生存时间得到了15%的延长。     

Fuzzy-based unequal clustering protocol for underwater wireless sensor networks

Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy-based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi-hop and inter-cluster routing. The FBUCP is compared with the LEACH-UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH-UWSN, CDBR, and FBCA.     

Improved bat optimization algorithm and enhanced artificial bee colony-based cluster routing scheme for extending network lifetime in wireless sensor networks

In this paper, improved bat and enhanced artificial bee colony optimization algorithm-based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)-based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.     

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.     

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.     

A fuzzy decision scheme for relay selection in cooperative wireless sensor network

In energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.     

FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks

Wireless sensor network comprises billions of nodes that work collaboratively, gather data, and transmit to the sink. “Energy hole” or “hotspot” problem is a phenomenon in which nodes near to the sink die prematurely, which causes the network partition. This is because of the imbalance of the consumption of energy by the nodes in wireless sensor networks. This decreases the network's lifetime. Unequal clustering is a technique to cope up with this issue. In this paper, an algorithm, “fuzzy‐based unequal clustering algorithm,” is proposed to prolong the lifetime of the network. This protocol forms unequal clusters. This is to balance the energy consumption. Cluster head selection is done through fuzzy logic approach. Input variables are the distance to base station, residual energy, and density. Competition radius and rank are the two output fuzzy variables. Mamdani method is employed for fuzzy inference. The protocol is compared with well‐known algorithms, like low‐energy adaptive clustering hierarchy, energy‐aware unequal clustering fuzzy, multi‐objective fuzzy clustering algorithm, and fuzzy‐based unequal clustering under different network scenarios. In all the scenarios, the proposed protocol performs better. It extends the lifetime of the network as compared with its counterparts.     

一种基于演化博弈的分簇协作路由算法

现有的分簇协作路由没有依据协作通信的特点选择簇头,也没能根据簇头节点的服务能力均衡簇成员负载,因而不能充分发挥协作通信能量高效的优势.本文提出了一种基于演化博弈的分簇协作路由算法CCREG.算法首先定义虚节点剩余能量作为簇头确立的指标,然后通过动态演化博弈为簇联盟问题建立模型.簇成员节点选择不同簇头结成联盟,可获得不同的收益.收益由簇头的能力、簇成员节点个数等因素决定.簇成员节点都可以根据自身得到的信息有限理性的选择簇结成联盟,直到网络中所有节点改变簇联盟都不能获得更高的收益.实验结果表明,与协作多输入多输出路由算法CMIMO相比,CCREG算法的网络生存周期在两个簇头情况下延长14%到70%,三个簇头情况下延长5%到80%.     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

An optimized forwarding protocol for lifetime extension of wireless sensor networks

Optimized routing (from source to sink) in wireless sensor networks (WSN) constitutes one of the key design issues in prolonging the lifetime of battery‐limited sensor nodes. In this paper, we explore this optimization problem by considering different cost functions such as distance, remaining battery power, and link usage in selecting the next hop node among multiple candidates. Optimized selection is carried out through fuzzy inference system (FIS). Two differing algorithms are presented, namely optimized forwarding by fuzzy inference systems (OFFIS), and two‐layer OFFIS (2L‐OFFIS), that have been developed for flat and hierarchical networks, respectively. The proposed algorithms are compared with popular routing protocols that are considered as the closest counterparts such as minimum transmit energy (MTE) and low energy adaptive clustering hierarchy (LEACH). Simulation results demonstrate the superiority of the proposed algorithms in extending the WSN lifetime. Copyright © 2008 John Wiley & Sons, Ltd.     

Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Clustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real-life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade-off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.     

基于事件驱动的动态分簇网络的协作传输方法

针对事件驱动的无线传感器网络的传输可靠性问题,该文利用节点间的互助,提出一种基于事件驱动的动态分簇网络的协作传输方法。无事件发生时,各节点按预先形成的静态簇低频传输数据。而一旦有事件发生,能感知事件发生的节点快速组成事件簇,向簇头发送采集的数据,簇头融合数据后发往汇聚节点。为提升传输可靠性,当簇头传输失败时,由最佳中继协作转发数据给汇聚节点。在最佳中继的选择上,考虑到事件的连续移动,以及处于事件前向通道上的节点具有较大的感应值和较好的协作能力等条件,该文提出了基于前向通道的最佳中继选择策略。仿真和实验结果表明,所提协作传输方法能够有效提高传输可靠性。