ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of these networks is highly dependent on routing protocols directly affecting the network lifetime. Clustering is one of the most popular techniques preferred in routing operations. In this work we propose a novel energy-efficient protocol for WSN based on a bat algorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithm for WSN) to prolong the network lifetime. We use an objective function that generates an optimal number of sensor clusters with cluster heads (CH) to minimize energy consumption. The performance of the proposed approach is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy Efficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interesting in terms of energy-saving and prolongation of the network lifetime.     

Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks

In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly distributed node distribution, a cluster with equal nodes is not an obvious possibility to reduce the energy consumption. To resolve this issue, this paper provides a novel, Balanced-Imbalanced Cluster Algorithm (B-IBCA) with a Stabilized Boltzmann Approach (SBA) that attempts to balance the energy dissipation across uneven clusters in WSNs. BIBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’. So as to handle the changing topological characteristics of sensor nodes, this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes. The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency, lifetime, network stability, average residual energy and so on.     

A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection in the network is fair and that the location of the selected CH is not concentrated within a certain range, we chose the appropriate CH competition radius. Simulation results show that, compared with LEACH, LEACH-C, and the DEEC clustering algorithm, this algorithm can effectively balance the energy consumption of the CH and extend the network life.     

An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Recently, Wireless sensor networks (WSNs) have become very popular research topics which are applied to many applications. They provide pervasive computing services and techniques in various potential applications for the Internet of Things (IoT). An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism (ACMDGTM) algorithm is proposed which would mitigate the problem of “hot spots” among sensors to enhance the lifetime of networks. The clustering process takes sensors’ location and residual energy into consideration to elect suitable cluster heads. Furthermore, one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself. Related experimental results display that the presented method can avoid long distance communicate between sensor nodes. Furthermore, this algorithm reduces energy consumption effectively and improves package delivery rate.     

基于LEACH协议的改进路由算法

针对LEACH协议在簇头选择过程中消耗能量多和节点间能量消耗不均匀的问题,本文提出了一种基于时间的均匀分簇混合路由协议( ECHT)在簇头竞选阶段中,节点广播成为簇头的时间与其剩余能量成反比,越早广播的节点将成为簇头.在数据传输阶段中,采用多跳与单跳相结合的方式将数据传送到基站,并计算数据传送开销来修改节点能量以此确定网络生命周期.仿真结果显示,ECHT协议能有效地均衡网络节点的能量消耗和延长网络生命周期.     

无线传感器网络中LEACH协议的节能改进算法

分析了低功耗自适应分簇路由协议(LEACH)算法,对算法中簇头选举数目的随机性做了改进并且在簇头选举时加入了对节点剩余能量的考虑,同时提出采用欧式平面上两条曲线交叉概率很大的思想,在簇头与基站之间建立多跳链路,从而解决了原协议中簇头与基站单跳通信能量消耗过大的问题.性能分析和仿真实验表明:改进的协议有效均衡了节点能耗,提高了网络寿命.     

An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps for WSN

Wireless Sensor Networks (WSNs) have hardware and software limitations and are deployed in hostile environments. The problem of energy consumption in WSNs has become a very important axis of research. To obtain good performance in terms of the network lifetime, several routing protocols have been proposed in the literature. Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency. It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent, and then the parent node forwards them, directly or via other parent nodes, to the base station (sink). In this paper, we present a new Energy-Efficient clustering protocol for WSNs using an Objective Function and Random Search with Jumps (EEOFRSJ) in order to reduce sensor energy consumption. First, the objective function is used to find an optimal cluster formation taking into account the ratio of the mean Euclidean distance of the nodes to their associated cluster heads (CH) and their residual energy. Then, we find the best path to transmit data from the CHs nodes to the base station (BS) using a random search with jumps. We simulated our proposed approach compared with the Energy-Efficient in WSNs using Fuzzy C-Means clustering (EEFCM) protocol using Matlab Simulink. Simulation results have shown that our proposed protocol excels regarding energy consumption, resulting in network lifetime extension.     

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm Intelligence (SI) techniques can be implemented. The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence (QoSCRSI) algorithm. The proposed QoSCRSI technique performs two-level clustering and proficient routing. Initially, the fuzzy is hybridized with Glowworm Swarm Optimization (GSO)-based clustering (HFGSOC) technique for optimal selection of Cluster Heads (CHs). Here, Quantum Salp Swarm optimization Algorithm (QSSA)-based routing technique (QSSAR) is utilized to select the possible routes in the network. In order to evaluate the performance of the proposed QoSCRSI technique, the authors conducted extensive simulation analysis with varying node counts. The experimental outcomes, obtained from the proposed QoSCRSI technique, apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency, network lifetime, overhead, throughput, and delay.     

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python user-defined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.     

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.     

基于距离分区的多级异构无线传感器网络成簇算法

为了防止无线传感器网络(WSN)节点因为通信距离过长而过早死亡,有效延长网络生命周期,提出了一种基于距离分区的高能效的多级异构无线传感器网络成簇算法(MHCADP)。此算法将监测区域分为三部分,并根据不同监测区域和基站的距离部署能量不同的三类节点,按照节点剩余能量与网络平均能量的比例来选举簇头节点,让较高初始能量和剩余能量的节点拥有更多的机会成为簇头。另外,在数据传输时,考虑节点和基站的距离以及自身剩余能量,选择单跳或多跳的传输方式。仿真实验结果表明,与现有的重要成簇算法——低能耗自适应分簇分层(LEACH)算法和稳定选举协议(SEP)算法相比,MHCADP算法能够有效减少网络能量消耗和平衡网络负载,使网络稳定周期和生命周期延长50%以上。     

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.     

低时延能耗均衡的水声传感器网络簇间路由算法

针对水声传感器网络的簇间路由选择问题,提出了一种基于前向网关的低时延能耗均衡路由算法,该算法采用最优方向角原则和能耗均衡原则选择中继簇头和中继网关,以减小长延迟和高能耗对水声通信的影响。仿真结果表明该算法在网络平均能耗、端到端时延和网络生命周期等方面具有较好的性能。     

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.     

Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks

In this paper, the energy conservation in the ununiform clustered network field is proposed. The fundamental reason behind the methodology is that in the process of CH election, nodes Competition Radius (CR) task is based on not just the space between nodes and their Residual Energy (RE), which is utilized in Energy-Aware Distributed Unequal Clustering (EADUC) protocol but also a third-degree factor, i.e., the nearby multi-hop node count. In contrast, a third-factor nearby nodes count is also used. This surrounding data is taken into account in the clustering feature to increase the network’s life span. The proposed method, known as Energy Conscious Scattered Asymmetric Clustering (ECSAC), self-controls the nodes’ energy utilization for equal allotment and un-equal delivery. Besides, extra attention is agreed to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s lifetime. The proposed methodology reduces the clustering overhead and node communication energy consumption to extend the network’s lifetime. Our suggested method’s results are investigated against the classical techniques using the lifetime of the network, RE, alive hop count and energy consumption during transmission as the performance metric.     

Photovoltaics in the assessment of wireless sensor network reliability with changing environmental conditions

This paper presents a reliability assessment of a wireless sensor network (WSN) equipped with mini photovoltaic cells (PV‐WSN) under natural environmental conditions while accounting for different types of system failures. In particular, our assessment considers the hardware specifications of the sensors, photovoltaic (PV) specifications, the use of rechargeable batteries, communication protocols, and various elements required for efficient detection of environmental conditions. We accomplished this by developing a simulator that generated data for 2 broad WSN conditions: (1) WSN without PV and (2) WSN with PV. The dynamic source routing protocol was employed for these simulations, and the following variables were assessed for both conditions: WSN reliability, the impact of energy consumption on the network, and the types of failures that lead to sensor unavailability. The following assumptions were made to run the simulation: the distribution of WSN nodes is random, with 1 sink node per rectangular cluster, the sensor nodes are structurally and functionally identical, environmental interference and suboptimal orientation impair PV cell recharge capacity randomly, and no communication loss occurs. Our reliability assessment assumed extreme environmental conditions and further made assessments of component reliability that included the following parameters: sensor and PV cell hardware specifications, the rechargeable nature of PV cell batteries for different sensor activity states, the availability of sunlight for powering PV cells, and the energy efficiency of PV cells. We found that network lifetime was prolonged for the PV‐WSN condition over the WSN without PV condition, introducing a role for PV cells as potential energy sources for WSNs.     

Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

The Internet of Things (IoT) is gaining attention because of its broad applicability, especially by integrating smart devices for massive communication during sensing tasks. IoT-assisted Wireless Sensor Networks (WSN) are suitable for various applications like industrial monitoring, agriculture, and transportation. In this regard, routing is challenging to find an efficient path using smart devices for transmitting the packets towards big data repositories while ensuring efficient energy utilization. This paper presents the Robust Cluster Based Routing Protocol (RCBRP) to identify the routing paths where less energy is consumed to enhances the network lifespan. The scheme is presented in six phases to explore flow and communication. We propose the two algorithms: i) energy-efficient clustering and routing algorithm and ii) distance and energy consumption calculation algorithm. The scheme consumes less energy and balances the load by clustering the smart devices. Our work is validated through extensive simulation using Matlab. Results elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption, the number of packets received at BS and the number of active and dead nodes. In the future, we shall consider edge computing to analyze the performance of robust clustering.     

Rssi- and Lqi-based clustering: analysis and implementation of multihop EOP-LEACH for WSN using Sun SPOT

Network energy is the main constraint that affects the practical design of wireless sensor networks (WSNs) as the nodes have limited resource capabilities. This aticle presents a novel EOP-LEACH (Efficient Optimized Practical-LEACH) that is proposed to overcome limitations of conventional low energy adaptive clustering hierarchy (LEACH) protocol to improve the life time and reduce the energy consumption of the WSN. The proposed enhancement is achieved by inserting novel factors in the threshold equation of conventional LEACH in order to choose the optimum node to be Cluster Head (CH).. The novel proposed parameters to be inserted are the Received Signal Strength (RSSI) which is related to the communication pass distance and link quality indication (LQI) that reflect the effect of communication channel noise and interference. Multihop routing, based mainly on RSSI values of neighbor nodes, is another proposed improvement to conventional LEACH to decrease distance of transmission which leads to savings in network energy. The simulation of the proposed protocols was done using MATLAB software. Comparison between the performance of proposed protocols and conventional LEACH shows that the WSN performance is improved using the proposed protocols.     

HREF: a highest remaining energy first adaptive clustering algorithm for wireless sensor/mesh networks

A simple mechanism to prolong the life cycle of the network by balancing nodes’ energy consumption is to rotate the active dominating set (DS) through a set of legitimate DSs. This paper proposes a novel adaptive clustering algorithm named HREF (Highest Remaining Energy First). In the HREF algorithm, cluster formation is performed cyclically and each node can declare itself as a cluster head autonomously if it has the largest residual energy among all its adjacent nodes. The performance effectiveness of the HREF algorithm is investigated and compared to the D-WCDS (Disjoint Weakly Connected Dominating Set) algorithm. In this paper, we assume the network topology is fixed and does not require sensor mobility. This allows us to focus on the impact of clustering algorithms on communication between network nodes rather than with the base station. Simulation results show that in the D-WCDS algorithm energy depletion is more severe and the variance of the node residual energy is also much larger than that in the HREF algorithm. That is, nodes’ energy consumption in the HREF algorithm is in general more evenly distributed among all network nodes. This may be regarded as the main advantage of the HREF adaptive clustering algorithm.     

一种新的分布式无线传感器网络定位方法

针对无线传感器网络节点能源受限的特征,以系统最小硬件开销为设计原则,提出了一种适用于基于测距的分布式定位方法(3/2-NANDB),该方法可在不增加单个独立节点硬件开销的情况下,利用附加的外部控制系统发射一个旋转定向波束充分挖掘节点间的冗余信息,有效排除节点位置的模糊性,从而可完全确定只有两个邻居节点的节点位置和部分只有一个邻居节点的节点位置,达到减少GPS携带节点数量、最大化网络内部可定位节点数目、扩大网络观察范围和延长无线传感器网络存活时间等目的.而利用该方法的节点二义性排除算法,还可以辅助其他现有的基于三邻居(3-NA)的定位算法提高整体定位性能.