Low-Power Communication Algorithm for Optimized Energy Consumption in Wireless Sensor Network-Based Solar Streetlights Technology

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.

In many modern situations, wireless sensor networks play a crucial role, used for monitoring things like environmental conditions, transactions, and various statuses. These networks gather a large amount of data, sending it to a central hub for analysis. However, a significant problem is that traditional wireless sensor networks heavily rely on energy, and this limits how long they can operate. This chapter develops an optimization approach for a variable clustering routing protocol to address the issue. The objective is to enhance the cluster structure in wireless sensor networks while reducing energy loss in cluster heads. The first step involves employing a dynamic estimation method for clustering to determine cluster heads, utilizing core concentration to establish the head within the cluster radius. The authors also introduce a fuzzy logic algorithm to handle uncertainties in selecting cluster heads. The residual energy of the cluster's head nodes, maintaining a balanced distribution of cluster heads, and optimizing node use of energy are all taken into account by this fuzzy logic approach. An ant colony algorithm-based technique for optimizing inter-cluster transport is presented in this outline. The primary objective is to optimize energy consumption while simultaneously reducing the data communication overhead among cluster heads. Chaotic mapping is utilized by this technique to update and perturb pheromones, ensuring optimal performance. Energy utilization among cluster heads is optimized by selecting the optimal path based on considerations of energy dispersion parameters and distance coefficients. Our experiments show that compared to traditional algorithms, Researchers provide a non-uniform clustering approach for route optimization that dramatically extends the network lifespan by 75% and lowers overall energy consumption by about 20%. This effectively optimizes network energy utilization and significantly extends the network lifetime, demonstrating the practical effectiveness of our method.

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.

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Since 2000, the usage of Wireless Sensor Networks (WSNs) in the military and civilian applications is increased rapidly. WSNs consist of low cost hardware which acts as multi-functional sensor nodes and are linked to the base station to efficiently aggregate and transmit the data. However, the limitation of sensor nodes is limited battery power. Efficient cluster head (CH) selection manages the WSNs in reducing the overall energy consumption. The cluster head combinations the data from the neighbouring nodes and transmits to the base station. The selection of cluster head in WSN is a Non-deterministic Polynomial-hard problem. In this paper, a bio inspired approach called firefly algorithm is used for selection of CH in WSNs. The synchronous firefly algorithm selects the optimal CH node among all nodes participated in the network, which also increases the performance of the network. The experimental results show that the proposed algorithm has effective CH selection procedure when compared to the other existing algorithms. The proposed algorithm reduced the packet loss ratio by 8.32% and it improved the reduction of energy consumption of overall network.

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Most of the sensor nodes in a Wireless Sensor Network (WSN) have limited energy. In order to increase the network lifetime, some energy efficient algorithms were proposed earlier. It has been a challenge to design wireless sensor networks to enable applications for oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The main objective of this work is to find out the data routing method such that the battery life of sensor nodes can be prolonged. In this paper, A Two Cluster Head Energy efficient Wireless Sensor Network (TCHE-WSN) Algorithm was put forward. WSN consists of sensor nodes which sense the physical parameters such as temperature, humidity, pressure and light etc and send them to a fusion center namely Base Station (BS) from where one can get the value of physical parameters at any time. Requirement of monitoring the environment might be anywhere, like middle of the sea or under the earth where man cannot go often to recharge the batteries which supplies the sensing device, transceiver and memory unit in the sensor node. So the usage of the battery power must be judicious in WSN. The present study demonstrates the data routing method from node to BS (Base Station) using two cluster heads to prolong the battery life of sensor nodes. The two cluster head analogy reduces the overhead of single cluster head, avoids packet collision, and improves reliable data transmission with little compromise in energy consumption and hop count compare to single cluster head method.

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.

A double cluster head based wireless sensor network routing algorithm is proposed aiming at the premature death of the cluster head due to very fast energy consumption and the unbalanced energy consumption of nodes. Firstly, the master cluster heads and the member nodes of the cluster are determined based on the LEACH algorithm. Secondly, the vice cluster head is selected from cluster member nodes according to energy consumed by all the member nodes to complete a process of data transmission, collection and fusion based on the principle of minimum energy consumption. Any member node in the cluster transmits the data to the cluster head near to it. Both the master cluster head and the vice cluster head collect and integrate the data, which effectively reduces and balances the energy consumption of nodes. Based on the simulation results, compared with the LEACH algorithm, the algorithm proposed in this article significantly delays the death time of the first node in the wireless sensor network, increasing the life cycle of the network by about 75%.

ABSTRACTIn wireless sensor networks (WSNs), nodes typically operate with limited energy supplies, making efficient data gathering essential for prolonging network lifespan. One effective approach to reduce energy consumption is clustering. However, using a fixed sink to collect data can lead to energy depletion in specific nodes, causing bottlenecks. A mobile sink, on the other hand, can address this issue by enhancing network performance and reducing energy load on individual nodes. This paper introduces a hierarchical cluster‐based data aggregation method that employs fuzzy logic alongside a mobile sink to improve energy efficiency. The strategy has two main stages: clustering and data aggregation. In the clustering stage, the process is split into two steps: identifying cluster heads and organizing clusters. A fuzzy inference system assesses each node's potential as a cluster head based on factors such as remaining energy, node connectivity, and centrality. The nodes with the highest scores are selected as primary cluster heads, while those with slightly lower scores serve as backup cluster heads. Clusters are then formed around these chosen heads. In the data aggregation phase, cluster heads gather data from cluster members and forward it either to a mobile sink or directly to the base station (BS). Cluster heads located within a specified range (distance ≤ r) of the BS send data directly, while others route data via the mobile sink. This technique enhances data transmission efficiency and optimizes energy consumption, contributing to overall network improvement. The HDAMM approach demonstrated considerable advancements over earlier methods in terms of energy efficiency, delay reduction, packet delivery rate, and network longevity.

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.

The Wireless Sensor Network (WSN) personifies vital and active functions in multi-disciplinary research sectors, as it can deploy in harsh and antagonistic atmospheres where the deployment of a wired system is not possible. However, designing an energy efficient and durable WSN is still a key challenge. Though the contribution of the clustering mechanism attempts to augment the network LifeTime, the energy consumption in the Cluster Heads (CHs) is rapidly high. This led to the frequent change of CHs and minimized the network’s lifetime. To diminish these issues, we propose an Energy Efficient LifeTime Maximization (EELTM) approach which utilizes the intelligent techniques Particle Swarm Optimization (PSO) and Fuzzy Inference System (FIS). Further, we propose an optimal CH–CR selection algorithm in our approach which exploits the fitness values calculated by the PSO technique to determine two optimal nodes in each cluster to act as CH and Cluster Router (CR). The selected CH exclusively gathers the information from its cluster members, whereas the CR is liable for receiving the gathered information from its CH and transferring it to the BS. Thus, the overhead of CH is reduced. Another intelligent technique is that FIS figures out the radius for each CH, and thereby it partitions the network into unequal clusters. The performance of our proposed EELTM approach is analyzed, and evaluations are elaborated with well-known existing clustering algorithms. To assess the proficiency of EELTM and to evaluate the endurance of the network, efficiency parameters such as total-remaining-energy, first-node-expires and fifty-percent-expires are exploited. The experimental outcomes justify that the EELTM approach surpasses the existing mechanisms by 14%.

Wireless sensor networks (WSN) usually contains sensors powered by small batteries with limited power. These sensors collect data and transfer it to the base station (BS) where it will be manipulated; the process of data transfer could be direct or indirect depending on the used protocol. The batteries’ limited power will be partially consumed during every data transfer process. In some applications, such as agricultural, military and others, WSN use a large number of these sensors, so the energy consumption should be taken into consideration during the design of such networks as the batteries’ replacement is sometimes very difficult and expensive. Many researchers have been working on inventing the best battery-conserving protocols to extend the lifetime of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) protocol have better power-saving performance in this field, where the network is divided into groups called clusters, and each cluster selects its cluster head (CH). Sensors will transfer its data to the CH that belongs to it which in turn will transfer this data to the BS. However, a selection of CHs that ignores a node's energy change will cause the nodes serving as CHs to die earlier than the other nodes as too much energy is consumed. Also, the high frequency of re-use wastes a certain amount of energy. In order to distribute the power consumption evenly between the different nodes, we intend to classify the CH according to its distance from the BS, one operating at a close distance from the BS and the other at a far distance from it. The CH in the far distance will transfer its data not directly to the BS but through CH near to the BS. We aim with this proposal to distribute the energy consumption evenly between the nodes. Simulation results show that the proposed protocol GE-LEACH gives better results in the lifetime of the node especially in a field with a large area