Number Of Alive Nodes Research Articles

Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network

Wireless sensor network (WSN) is developed as a network of sensors, which engage in sensing and transmitting the data to the sink node. The constraints, such as energy, memory, and bandwidth insist the researchers to develop an efficient method for data transmission in WSN. Accordingly, this paper introduces a data aggregation mechanism based on query processing, Wavelet-based Least Common Ancestor-Sliding window (WLCA-SW). The energy-loss and memory-crisis is well addressed using the proposed WLCA-SW through the successive steps of query processing, duplicate detection, data compression using the wavelet transformation, and data aggregation. The proposed WLCA-SWA is developed with the integration of the weighed sliding window and Least Common Ancestor (LCA), which enables the energy-aware aggregate query processing and de-duplication such that the duplicate records are detected potentially prior to the communication of the sensed data to the sink node. It is prominent that the weighed sliding window is the extension of the existing time-based sliding windows. The effectiveness of the proposed aggregate processing approach is evaluated based on the metrics, such as number of alive nodes, data reduction rate, data-loss percentage, and residual energy, which is found to be 33, 85%, 8.222%, and 0.0610 J at the end of 1000 rounds using 150 nodes for analysis. Moreover, the proposed method has the minimum aggregation error of 0.03, when the analysis is performed using 50 nodes.

Energy‐Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks

In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy‐efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next‐hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state‐of‐the‐art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

IDBR: Iot Enabled Depth Base Routing Method for Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) gained the attention of researchers due to their substantial applications in various fields. The major application areas of UWSN are environmental monitoring, underwater oil and gas extraction and military surveillance, smart farming, communication, and others. However, UWSNs are also prone to significant issues, such as limited network lifetime, the low processing capability of nodes, high energy consumption to run routing protocols, and difficult node replacement. Therefore, enhancing the lifetime of UWSN by reducing energy consumption and processing is a research issue. In this research, we proposed the IoT enabled depth base routing method (IDBR) to utilize energy efficiently. The performance of the proposed IDBR method is compared with conventional DBR protocol using simulation conducting in MATLAB. The performance of both the methods (i.e., IDBR and DBR) is evaluated in network energy consumption, the number of alive nodes, sink utilization, and end‐to‐end delay. The simulation‐based experiment results show that IDBR consumes 27.7% less energy and increases network stability than the DBR. Similarly, the utilization of the surface sinks in IDBR is more as compared to DBR as in IDBR, and sinks work as relay forward data to the base station without processing which increases the power of field nodes. The proposed mechanism improves the network’s lifetime and increases the accessibility and security of the sensed data.

Securing wake up radio for green wireless sensor networks against denial of sleep attacks

This paper proposes the distributed cooperation model (DCM) based hierarchical smart routing protocol (HSRP) to counter the denial of sleep (DoSL) attacks in Green Wireless Sensor Networks (GWSNs). DCM solution combined with the Dynamic Elliptic Curves-RSA (DECRSA) algorithm is used to provide high security level and protect Wake-Up Radio (WUR) for GWSNs against DoSL attack targeting the energy resources of sensors. The used distributed schema ensures the load balancing in the network through the selection of a new Supervisor Node at the beginning of each round based on a special equation taking into account different metrics such as the remaining energy and the number of neighbour nodes. Simulation results conducted under the NS3 simulator show that the proposed DCM helps to effectively protect the GWSNs against the DoSL attacks and improves the network lifetime by more than 60% when three malicious nodes are used. Obtained results show also that the DCM extends either the period of stability and instability to the maximum and helps to counter the fall in the number of alive nodes which cause the problem of isolated area.

A less computationally complex clustering algorithm based on dynamic K-means for increasing lifetime of wireless sensor networks

Clustering in wireless sensor networks is a critical issue based on network lifetime, energy efficiency, connectivity and scalability. Sensor nodes are capable to collect data from any geographical region using routing protocol. This research endeavours to design a less computationally time complex clustering algorithm for hierarchical homogeneous wireless sensor network to extend network lifetime. It forms optimal number of clusters and reduces data communication span of sensor nodes using dynamic K-means algorithm. Selection of suitable cluster head is based on ratio of remaining energy of sensor node to its distance from centre of cluster. The simulation results prove that algorithm that has been presented achieves better energy efficiency when compared to other hierarchical homogeneous cluster based algorithms. It increases network lifetime, number of alive nodes per round, data delivered to base station, time of first node, middle node and last node to die for scalable situations in terms of node density and size of sensing region.

A PSO-based improved clustering algorithm for lifetime maximisation in wireless sensor networks

A wireless sensor network (WSN) is comprised of miniature devices known as sensor nodes. Sensor nodes are highly energy restrained due to their battery dependent operations and harsh area deployment. It is unviable to change their batteries. For maximising the network lifetime, energy conservation is a crucial measure of performance of WSNs. So, energy consumption and network lifetime are crucial issues in WSNs. Large portion of energy consumed in communication process. Several routing algorithms have been proposed in WSNs. Based on network structure, routing algorithms are divided into flat based, cluster based, and location-based algorithms. Due to energy efficiency, cluster-based routing is the best choice. In this paper, we propose a swarm based clustering algorithms for WSNs. We perform the analysis of our new algorithms with LEACH, LEACH-C, and HEED on the basis of energy, number of alive nodes, delay, PDR, and throughput. Simulation confirms that proposed algorithm outperform the other ones.

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing’s environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all.

Energy efficient zone‐based clustering algorithm using fuzzy inference system for wireless sensor networks

AbstractIn Wireless Sensor Networks (WSN), as sensor nodes have limited energy, the design of an energy‐efficient clustering technique is a critical challenge to be addressed. We propose an Energy‐Efficient Zone‐based Clustering algorithm for WSN that works efficiently in large area WSN as well. In the proposed method, the sensing field is divided into equal size zones, and one Zone Monitor is selected in each zone. The distance of a Zone Monitor from the Base Station is considered in the calculation of the number of desired Cluster Heads (CHs) in its zone for implementing Unequal clustering. The CHs are selected such that they are uniformly distributed inside each zone to conserve the energy of sensor nodes needed for intra‐cluster transmission. Also, for better load balancing among the selected CHs, we propose that cluster size should be uniform at zone level. Hence, after the CHs are selected, the remaining sensor nodes in a zone decide to become a cluster member of one of the selected CHs using a fuzzy‐based cluster formation technique. We compare the proposed algorithm with other approaches for three different network scenarios using parameters such as the number of alive nodes, the first node dies, half of the nodes die, the last node dies, the amount of data received at Base Station, and total remaining energy. Simulation results show that the proposed clustering algorithm provides better performance than other methods.

Memetic Algorithm-Based Data Gathering Scheme for IoT-Enabled Wireless Sensor Networks

Wireless Sensor Networks (WSNs) is the key element of the Internet of Things (IoT) for sensing the environment, collecting data and sending it to the base station (BS). In the IoT environment, routing protocols that are designed for WSNs are not working properly due to heterogeneity in the nodes. Therefore, an intelligent based routing protocol is needed to enhance the performance of the IoT-enabled WSNs. This paper presents a memetic algorithm based routing mechanism that can significantly enhance the performance of the network. In addition, we also proposed a mobile sink based data gathering mechanism that prevents network partitioning problem. We perform an extensive experiment on the proposed algorithm using the ns2.35 simulator. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of throughput, network lifetime, stability period, end to end delay and the number of alive nodes in the network.

Optimized and Dynamic Selection of Cluster Head Using Energy Efficient Routing Protocol in WSN

Wireless Sensor Networks plays an outstanding role in providing dynamic cluster head (CH) selection. However, the selection of CH is a major challenge due to erroneous CH selection and can lead to unbalanced energy consumption. This paper addresses this challenge by proposing a hybrid optimization algorithm for CH selection. The proposed CH selection comprises three phases, which includes the setup phase, transmission phase, and measurement phase. At first, the energy and the node’s mobility in the network are initialized. The setup phase is processed by choosing CH using the Optimized Sleep-awake Energy-Efficient Distributed clustering, which is designed by determining the optimal threshold and CH using proposed Rider-Cat Swarm Optimization (RCSO) algorithm. The proposed RCSO is designed by integrating Rider Optimization Algorithm into Cat Swarm Optimization. Here, the threshold and CH are chosen using multi-objective constraints, which involves distance, energy, and delay. After determining the CHs, the data transmission begins from CHs to the base station. At last, in the measurement phase, the residual energies produced from the nodes are being updated. The proposed RCSO method shows superior performance by providing maximal energy, throughput, and the number of alive nodes with values 0.0351 J, 74.715%, and 18 respectively.

CS-CGMP: Clustering Scheme Using Canada Geese Migration Principle for Routing in Wireless Sensor Networks

Recently, researchers focus on wireless sensor networks (WSNs) as it plays a vital role in numerous applications. Node energy is one of the resource constraints of WSN. Hence, it is significantly important to design an energy efficient routing. In this paper, a Clustering Scheme using Canada Geese Migration Principle (CS-CGMP) has been proposed for routing in WSNs. In this approach, the network field is partitioned into zones and number of Cluster Heads (CHs) are selected based on the density of each zone. CGMP is applied for CH rotation. If residual energy of a CH is below threshold value, it has to be rotated to a member node which has high energy. Further, the optimal number of CHs required is assured in every round. CS-CGMP based approach was simulated and the performance parameters with respect to network lifetime, number of alive nodes, number of packets transferred to the BS and CH were analysed by comparing with LEACH-C, (ACH) $$^2$$ , and DEEC.

A modified rider optimization algorithm for multihop routing in WSN

AbstractWireless Sensor Networks (WSNs) have an important role in establishing the communication between the Internet of things (IoT) devices. Routing is an important research field in the WSN as it helps in finding the suitable paths for the communication. This paper proposes a multipath routing algorithm based on the optimization approach. The proposed routing algorithm has two important phases, namely, Cluster head selection and multipath routing. Initially, the cluster head selection is carried out by the kernel based Fuzzy C Means (kernel FCM) algorithm. Then, the multipath routing is established by the newly developed Rider Salp Swarm Optimization algorithm (RSSA), which is the integration of the Rider Optimization Algorithm (ROA) and Salp Swarm Algorithm (SSA). The fitness function of the proposed RSSA is designed by considering the several factors, such as energy, QoS and trust. The simulation of the proposed work is carried out using different WSN setups, and the results are compared with several comparative techniques. From the results, it can be summarized that the proposed RSSA based multipath routing has better performance with values of 0.2526, 0.0764, 0.5, and 26 for delay, energy, throughput and number of alive nodes, respectively.

Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Wireless sensor networks depend on the effective functioning of the nodes in the network, which is concerned regarding the energy that is essential for the extended network life-time. Clustering plays a major role in enabling energy efficiency, which extends the life-time of the network. Thus, the paper introduces a cluster head (CH) selection phenomenon based on the algorithm, Taylor kernel fuzzy C-means (Taylor KFCM), which is the modification of the kernel-based fuzzy c-means (KFCM) algorithm in the Taylor series. The developed algorithm chooses the cluster head using the selection phenomenon, acceptability factor, which is computed using the energy, distance, and trust. In other words, a node acts as a CH when the fitness constraints of minimal distance, maximal trust, and maximal energy are attained. The simulation environment is established using 50, 100, and 200 nodes with 5 and 10 CHs and the effectiveness of the proposed CH selection is revealed through the analysis depending on the metrics, throughput, energy, delay, and the number of alive nodes. The proposed Taylor kernel fuzzy C-means acquired a maximal throughput, energy, and alive nodes of 0.2857, 0.0947, and 31, and minimal delay and routing overhead of 0.1219, 0.0418 respectively.

Green communication in IoT networks using a hybrid optimization algorithm

There has been a huge surge in the Internet of Things (IoT) applications in recent years. The sensor nodes in the IoT network generate data continuously that directly affects the longevity of the network. Even though the potential of IoT applications are immense, there are numerous challenges like security, privacy, load balancing, storage, heterogeneity of devices, and energy optimization that have to be addressed. Of those, the energy utilization of the network is of importance and has to be optimized. Several factors like residual energy, temperature, the load of Cluster Head (CH), number of alive nodes, and cost function affect the energy consumption of sensor nodes. In this paper, a hybrid Whale Optimization Algorithm-Moth Flame Optimization (MFO) is designed to select optimal CH, which in turn optimizes the aforementioned factors. The performance of the proposed work is then evaluated with existing algorithms with respect to the energy-specific factors. The results obtained prove that the proposed method outperforms existing approaches.

An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks.

Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio.

Unequal clustering and scheduling in Wireless Sensor Network using Advance Genetic Algorithm

Duo to limited energy of the sensor nodes which forming the wireless sensor network (WSN), and almost deployed in harsh environment, therefore it is very important to minimize the consumption of the sensor node’s energy. For this reasons designing an energy efficient clustering and scheduling of sensor nodes considered the most efficient methods for extending the WSN’s lifetime. In this paper, we have proposed Genetic algorithm based methods for clustering and scheduling. The proposed methods have two stages; in the first stage GA is used for cluster formation where the chromosome is represented by using the sensor node’s position. While in the second stage GA is used for selecting a minimum number of nodes while maintaining the full coverage and the connectivity of the selected nodes. The simulation result shows that the proposed algorithm (AGA) is more efficient than the existent algorithms in terms of number of first node die per round, number of a live nodes, and energy consumption.

A Coherent Approach for Dynamic Cluster-Based Routing and Coverage Hole Detection and Recovery in Bi-layered WSN-IoT

Wireless sensor network is widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring. Coverage hole detection and recovery is a major problem in WSN. It causes energy holes and increases the number of dead nodes. In this paper, we propose a bi-layered WSN architecture for dynamic clustering based routing and coverage hole detection and recovery. Our proposed work has four steps cluster formation, cluster head (CH) selection, coverage hole detection, and recovery and routing. Clusters are formed by the K-means algorithm. CH is elected by Determined Weight (DW). This DW is calculated by residual energy, distance from cluster and center to the base station. Based on the weight CH is selected. When the size of the cluster which is nearer to the base station is large then we explore cluster maintenance (cluster splitting and merging) for effective clustering by Efficient Entropy Function. After cluster formation, we perform coverage hole detection and recovery for successful packet transmission. Coverage holes occur in different locations. In this paper, we detect the coverage hole in three locations: (1) within the cluster, (2).among the cluster, (3).edge of the network. We use the hole manager (HM) at four different locations, it uses fuzzy logic (energy, stability) to find which node can recover coverage hole. The best multi-hop route is selected for transmission by Multi-objective Emperor Penguin Optimization Algorithm (MO-EPO). We implement our proposed approach for Agriculture Applications in WSN assisted with IoT. Finally, we analyse the performance of our proposed approach with respect to following metrics: Energy consumption, Network lifetime, Number of alive nodes and Packet delivery ratio. Our simulation results are implemented on the NS3.26 simulator.

A metaheuristic optimization approach for energy efficiency in the IoT networks

SummaryRecently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Cluster based routing strategies are popular categories of routing protocols among wireless sensor networks (WSNs). In a typical cluster based routing protocols, few nodes are elected as cluster Head (CH) nodes and they form clusters with other nodes of the networks known as cluster members. The data sensed by cluster members is sent to CH node for data aggregation and further processing. Data Aggregation is considered to be one of the important methods used to prolong the network lifetime of WSN. Data aggregation is quite popular among cluster based routing protocols for WSN, where the data packets are aggregated by intermediate nodes till it reaches to BS. Data aggregation ultimately helps in reducing the number of data messages thereby assisting in network life prolongation. In this paper, an intra-cluster data aggregation technique (ICA) for WSNs is proposed. ICA constructs the intra cluster data aggregation path from a source node to its CH node in an energy efficient way. The data packets are aggregated along the aggregation path by intermediate relay nodes till the message reaches to designated CH node. Comparative analysis of ICA, LEACH and LEACH-C protocols are carried by considering the parameters such as number of alive nodes, energy consumption, number of data packets received by BS. Results obtained show that ICA performs better compared to LEACH and LEACH-C protocols.

Research on 3D Wireless Sensor Networks ISC-EAR Routing Algorithm

In order to improve the performance of 3D wireless sensor networks, an Iterative Split Clustering Energy Angle Routing (ISC-EAR) Algorithm is proposed. The design idea is discussed theoretically, and the design idea is as follows: on the basis of the link bandwidth and other meeting user QoS requirements, all nodes within the node perception radius or communication radius are regarded as next hop candidates. Select the next hop node according to the total energy consumption of the node (including sending and receiving energy consumption), and try to ensure that the farthest transmission distance consumes the least energy. Select the current node, the destination node and the next hop node, the space vector with the current node as the vertex has the smallest angle, that is, the candidate node closest to the destination node as the next hop node. In order to verify the performance of the algorithm, use C/C++ for programming simulation. Through three different topological structures of mine topology, average topology and random topology, the performance evaluation is carried out using four indicators: the number of alive nodes, network lifetime, network energy consumption and average energy of node. Through simulation calculation, the ISC-EAR routing algorithm has good technical performance advantages compared with the benchmark routing algorithm IGreedy, which can increase the survival time of nodes, reduce network energy consumption, and prolong the survival time of the network. It has better advancement.