A double cluster head based wireless sensor network routing algorithm

Leach (low energy adaptive clustering hierarchy) algorithm is a self-clustering topology algorithm. Its execution process is cyclical. Each cycle is divided into two phases: cluster building phase and stable data communication phase. In the stage of cluster building, the adjacent nodes cluster dynamically and randomly generate cluster heads. In the data communication phase, the nodes in the cluster send the data to the cluster head, and the cluster head performs data fusion and sends the results to the aggregation node. Because the cluster head needs to complete data fusion, communication with the convergence node and other works, the energy consumption is large. Leach algorithm can ensure that each node acts as cluster head with equal probability, so that the nodes in the network consume energy relatively evenly. The basic idea of Leach algorithm is to randomly select cluster head nodes in a circular way. It evenly distributes the energy load of the whole network to each sensor node in the network. It can reduce network energy consumption and improve network life cycle. Leach repeatedly performs cluster refactoring during its operation. This paper studies the parameter detection of wireless sensor network based on Leach algorithm on the on-chip embedded debugging system. Because the classical low-power adaptive clustering layered protocol (Leach) has the problem of energy imbalance and short node life cycle, this paper uses embedded debugging technology based on Leach algorithm and the residual energy and position of nodes in wireless sensor networks were tested for research. This Leach algorithm uses the concept of wheel. Each round consists of two phases: initialization and stabilization. In the initialization stage, each node generates a random number between 0 and 1. If the random number generated by a node is less than the set threshold T (n), the node publishes a message that it is a cluster head. Through the research on the parameter detection, the simulation results show that the research in this paper has good feasibility and rationality.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self‐organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near‐optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Aiming to the disadvantage of the short lifetime due to the uneven distribution of the cluster head, and single-hop path communication style in the LEACH protocol. By analyzing the classic clumps and routing protocol LEACH, and changing the way of producing cluster head and the communication way between the base station, the modified algorithm reduces the setup time and the cluster head communication distance and balances the node energy consumption. The simulation results showed that the modified algorithm had efficiently reduced energy consumption of wireless sensor network nodes, and made survival time of network much longer than traditional LEACH algorithm.

The primary factor influencing the wireless sensor network (WSN) is the energy consumption of the sensor node. One of the key factors influencing WSN energy consumption is the high power consumption and packet delivery ratio needed for WSN processing. The suggested Energy Efficient Spherical Grid Routing (EESGR) protocol reduces the node's energy consumption to meet the requirements. To choose the cluster heads, the WSN is clustered into a collection of nodes using the pillar k-means clustering method defined in the proposed protocol. One optimization algorithm inspired by nature, the ant lion, is used to create cluster heads for assessing energy consumption in WSNs. The behavior concept of the ant lion is utilized for choosing the best nodes for the selection of the cluster head. The multi-tier spherical grid routing proposed in the paper is used to grid the cluster head generated by the ant-lion optimization algorithm to evaluate the total energy consumed for processing the sensor network. The overall performance of this method is evaluated in Network Simulator 2 (NS2). The proposed method improves performance in throughput, end-to-end delay, packet delivery ratio, and energy consumption compared to the existing techniques.

Wireless sensor networks are emerging and involved in a wide range of applications. Improving energy efficiency is a significant task in WSNs. Energy consumption is a crucial problem that affects the network lifetime due to energy limitations in sensor nodes. So, it's required to introduce more energy-efficient protocols for WSNs. Different types of energy-efficient solutions exist for WSNs. The energy consumption is optimized and controlled if clustering is an efficient data collection method. The cluster heads aggregate the data from the member nodes and transmit the aggregated data to BS (base station) either in an uncompressed or compressed manner after collecting all data by the cluster-heads (CHs). Due to the heavy inter-cluster relay and aggregation Overhead, earlier death happens to the CHs located very close to the sink. There is a need for an efficient and reliable CH selection algorithms to select reliable CHs to prevent the earlier death of CHs and frequent reselection of CHs. Therefore, in this work, a fuzzy optimal CH selection algorithm is proposed to select the optimal CHs to improve the network's lifetime. The proposed algorithm uses the following multi-objective parameters: cluster load, communication cost, neighbour density, inter and intra-cluster distance, and node energy for CH selection. The modified Emperor Penguin Optimization (EPO) is used in the proposed work to determine an optimal route for transmitting the data from CH to sink. The modified EPO algorithm helps to improve the multi-hop routing efficiency, reduces the cost of long-distance transmission, and decreases energy utilization during data transmission. The modified EPO considers the parameters like link quality factor, relative distance factor, and residual energy factor for relay selection. The proposed technique was compared against earlier methods like Z-DSS, CCR, RETOPSIS, and LEACH-FC. The various parameters like packet delivery ratio, lifetime, energy consumption, end-to-end delay, and Throughput are studied. The evaluation results prove that the proposed mechanism achieves 40% energy efficiency with 60% lesser Overhead and improves the Throughput rate by 50% than the compared methods.

Wireless sensor networks have drawn tremendous attentions from all fields because of their wide application. Maximizing network lifetime is one of the main problems in wireless sensor networks. This article proposes an energy-efficient routing protocol which adopts unequal clustering technology to solve the hot spots problem and proposes double cluster head strategy to reduce the energy consumption of head nodes in the clusters. In addition, to balance the energy consumption between cluster heads and cluster members, a hybrid cluster head rotation strategy based on time-driven and energy-driven is proposed, which can make the timing of rotation more reasonable and the energy consumption more efficient. Finally, we compare the proposed protocol with LEACH, DEBUC, and UCNPD by simulation experiments. The simulation results prove that our proposed protocol can effectively improve the performance in terms of network lifetime, energy consumption, energy balance, stability, and throughput.

The routing protocols are of great importance to researchers which deal with the QoS, energy consumption, network design and packet overhead. This paper describes a new method for efficient performance of wireless sensor networks concentrating on cluster design strategy. The proposed approach Water-Rippling Shaped Clustering (WARIS), is a hybrid approach applies to cluster the large-scale network which looks like the shape of water rippling. It considers the parameters like per node energy consumption, total network energy consumption, the number of designed clusters and their member nodes, and the time spent during cluster design for evaluating the performance of competing algorithms. Major achievements are improved cluster design, energy aware cluster head (CH) selection method and reducing re-clustering overhead. Simulation results prove the enhancements.

In recent years, wireless sensor networks have been used for various applications such as environmental monitoring, military and medical applications. A wireless sensor network uses a large number of sensor nodes that continuously collect and send data from a specific region to a base station. Data from sensors are collected from the study area in the common scenario of sensor networks. Afterward, sensed data is sent to the base station. However, neighboring sensors often lead to redundancy of data. Transmission of redundant data to the base station consumes energy and produces traffic, because process is run in a large network. Data aggregation was proposed in order to reduce redundancy in data transformation and traffic. The most popular communication protocol in this field is cluster based data aggregation. Clustering causes energy balance, but sometimes energy consumption is not efficient due to the long distance between cluster heads and base station. In another communication protocol, which is based on a tree construction, because of the short distance between the sensors, energy consumption is low. In this data aggregation approach, since each sensor node is considered as one of the vertices of a tree, the depth of tree is usually high. In this paper, an efficient hierarchical hybrid approach for data aggregation is presented. It reduces energy consumption based on clustering and minimum spanning tree. The benefit of combining clustering and tree structure is reducing the disadvantages of previous structures. The proposed method firstly employs clustering algorithm and then a minimum spanning tree is constructed based on cluster heads. Our proposed method was compared to LEACH which is a well-known data aggregation method in terms of energy consumption and the amount of energy remaining in each sensor network lifetime. Simulation results indicate that our proposed method is more efficient than LEACH algorithm considering energy consumption.

In cluster based Wireless Sensor Networks, lifetime of the network may be increased by conserving the energy spent by the cluster members and cluster heads. Energy consumption by cluster based WSN depends on the factors like number of data bits transmitted, received, aggregated by the nodes. In two hop cluster based networks member nodes acquires data and pushes to cluster heads (CH). Cluster Heads in WSN consumes more energy as they are affected by all the three factors. This paper proposes three different methods that cleverly selects the cluster heads, avoids unnecessary data transmission and balances the aggregation energy. The first method maintains threshold distance between the cluster heads for even distribution of cluster heads to avoid unnecessary CH. In the second approach, redundant data collected by the cluster member are curbed to save aggregation energy at CH. In the last method member nodes are distributed uniformly to balance the energy consumption.

In order to solve the problem of unbalanced load consumption of nodes for wireless sensor networks (WSNs), this paper proposes a load-balanced routing algorithm based on cluster heads optimization for wireless sensor network. The proposed algorithm first applies first-order wireless transmission model to calculate the optimal number of clusters, then calculate nodes competitiveness rating by fuzzy algorithm considering the residual energy of node and distance from the node to base station, cluster head selection uses unequal clustering algorithm according to the competitiveness of nodes. By node competitiveness and energy management mechanism which cooperate with each other to select the best cluster heads. Use connected optimization between clusters to search multi-hop paths base station for reducing energy consumption of node, and consider transmission energy consumption, residual energy, transmission distance and other factors. The experimental results show that the proposed algorithm compared with LEACH and UCDP algorithm, can balance loading and effectively extend the life cycle of wireless sensor network.

In Wireless Sensor Network (WSN), unreasonable node clustering algorithm will inevitably lead to premature death, unawareness of regional information.To address problems of traditional LEACH algorithm, An improved LEACH multi-hop routing algorithm based on Energy Distance Threshold (EDT) is presented. It mainly improves from aspects of cluster number, cluster head selection and inter-cluster communication. The improved clustering hierarchy algorithm considers the nodes Energy Distance Threshold as an important factor in selection then chooses the best cluster head within the area. The selection of cluster head is based on re-select manner, which dynamically and effectively select cluster heads by control cluster number and combing with EDT. After cluster head completes local data fusion and processing, it look for adjacent cluster head as next hop closer to base station with larger EDT if the head is far away from station. The next hop repeats above process till the base station is arrived. Such manner of multi-hop among clusters enables communication among clusters and base stations so as to avoid direct communication between head and station. The method reduces energy consumption and also balances energy. Simulation results show the improved LEACH algorithm has higher energy utilizing rate, and it helps prolong network’s lifetime.

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster heads in a cluster based WSN is more catastrophic as they are responsible not only for data aggregation and transmission of the aggregated data to the base station but also relaying data for multi-hop communication. Therefore, a routing algorithm in WSNs should be energy aware as well as fault tolerant. In this paper, we present a distributed energy efficient and fault tolerant routing algorithm for WSNs. The algorithm selects next-hop cluster head in energy efficient manner in the data routing phase and carefully restores the connectivity of the neighbours of a cluster head in case of its failure. The algorithm is tested extensively by considering several scenarios of WSN. The experimental results are compared with the existing algorithms in terms of several metrics to show the effectiveness of the proposed algorithm.

One of the critical and vital parameter of Wireless Sensor Networks (WSNs) is its lifetime. There are various methods to increase WSN lifetime, clustering technique is one of them. In clustering, selection of desired percentage of Cluster Head (CHs) is performed among the sensor nodes (SNs). Selected CHs are responsible to collect data from its member nodes, aggregates the data and finally send to the sink. In this chapter, Fuzzy-TOPSIS techniques based on multi criteria decision making to choose CH efficiently and effectively to maximize the WSN lifetime are presented. These five criteria includes; residual energy, node energy consumption rate, number of neighbor nodes, average distance between neighboring nodes and distance from sink. Threshold based intra-cluster and inter-cluster multi-hop communication mechanism is used to decrease energy consumption. Moreover impact of node density and different type mobility strategies are presented in order to investigate impact over WSN lifetime.