LEACH Protocol Research Articles

An efficient neural network LEACH protocol to extended lifetime of wireless sensor networks

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol’s potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.

Implementation of optimal routing in heterogeneous wireless sensor network with multi‐channel Media Access Control protocol using Enhanced Henry Gas Solubility Optimizer

SummaryThe majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

A Modified LEACH Protocol Using Multi-Hop Mechanism and Neural Networks for IoT Applications

A Modified LEACH Protocol Using Multi-Hop Mechanism and Neural Networks for IoT Applications

Energy-efficient routing protocol for WSN based on relay node selection and A-star algorithm

Due to miniaturization constraints and manufacturing costs, wireless sensor networks (WSN) sensor nodes are generally outfitted with restricted resources concerning computing capability, energy, data storage capacity, and data delivery rate. These constraints encourage a significant portion of the research issues in WSNs, particularly the energy constraint, which is a fundamental issue. This paper suggests an innovative routing protocol that optimizes energy use called RNS_A-star, which combines a routing algorithm using a relay node and an improved heuristic for the A-star algorithm. The RNS_A-star protocol first applies a data routing algorithm using a relay node, which allows either using a relay node or simply sending the intra-cluster data to the CH. Then, the A-star algorithm is used to construct a multi-hop routing route between the CHs and the base station (BS) in order to find the best path for reducing energy consumption during data transmission. Simulation results show that the suggested RNS_A-star protocol performs better than ECO-BAT, LEACH-IACA, and LEACH protocols regarding energy conservation and extending network lifetime.

Systematic Literature Review: Development of The Leach Protocol Algorithm for Efficient Energy Consumption in WSN

Systematic Literature Review: Development of The Leach Protocol Algorithm for Efficient Energy Consumption in WSN

Comparing Leach protocol and its descendants on transferring scalar data

In the last years, The CMOS was developed and miniaturized rapidly, which, made sensors very fast, small and accurate. Hence, the creation of wireless sensor network (WSN) which are a network of nodes that exchange the data between them until it reaches the sink (base station). It is responsible for treating the data and transfer them to other servers linked to the internet for further treatment or storage. Therefore, everything related to WSN is a big topic of research for scientific community, especially transferring scalar data. In fact, many factors enter into account when it comes to send data like a radio, range of transmission, energy consumption and routing protocol. Routing protocols are very important in transferring data. They also have a big impact on energy consumption by nodes. Many categories of routing protocols exist: planning and level routing. Each type has its strength and weakness points. So, using a routing protocol in high-density environments is very challenging in energy consumption and data delivery. In addition, since level routing protocols like Leach are known for their energy efficiency. We choose three level routing protocol (Leach, MLD-Leach and MRE-Leach) to put them in a harsh environment to test their energy consumption and data transferring. We found that MLD-Leach has better energy consumption and data delivery.

Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network

Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network

Load aware active low energy adaptive clustering hierarchy for IoT-WSN

Clustering is a primary process that takes place in an IoT based wireless sensor network environment commences from the deployment phase. Due to the heterogeneity and resource constrained nature of internet of things (IoT) networks, dynamic clustering, cluster head selection, and routing are required to optimize the network and to improve the overall network performance. Load aware active low energy adaptive clustering hierarchy (LAALEACH) work is an attempt to introduce novel components to the standard LEACH protocol. The main objective of LAALEACH work is to achieve a load aware active routing in IoT based wireless sensor network environments. Rapid load estimator, load pattern tracker, and load aware active routing are the contributed modules introduced in this LAALEACH work. Most recent related works are analyzed and the proposed modules are devised in a way to overcome the issues in the existing methods. Standard network performance parameters such as throughput, packet delivery rate, communication delays, and energy consumption are measured by the OPNET based simulation during the experiments. Obtained improvements in the overall performance is the accomplishment of LAALEACH work.

GTFR: A Game Theory-Based Fuzzy Routing Protocol for WSNs

Clustering is an approach exploited in Wireless Sensor Networks (WSNs) to enhance their performance and energy efficiency. Therefore, cluster formation and proper cluster head (CH) selection are of utmost importance in such scenarios. The proposed method, game theory-based fuzzy routing (GTFR) protocol, optimizes both of these phases of network operation in WSN. In the proposed work, fuzzy clustering is utilized for forming clusters with the desired quality of having low intra-cluster communication distance (IACD). In addition, CH selection is based on game theory and a fitness function that ensures optimal CH selection during two sub-phases of CH selection (tentative CH selection and final CH selection). In conjunction with using a penalty function for the clustering game, the proposed work also considers the nodes’ history of acting as a CH as a fitness criterion to enhance performance by impartially rotating the role among all the nodes. The proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL protocols in terms of stability by 238.27%, 183.94%, 432.04%, and 47.31%, respectively. Rigorous simulations prove that the proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL in all three considered network scenarios.

Using a Fuzzy Approach as an Assessment Method to Extend the Lifespan of Wireless Sensor Networks using the LEACH Protocol

Wireless sensor network is the term used to describe a network where network nodes are wirelessly configured to collect data from the real world. Node sensors depend on finite energy sources, such as batteries, because of the wireless configuration they have. If the battery-operated sensor of the node is not charged, it will be unable to carry out its intended function. If a specific amount of nodes fail, the network will cease to function. Several energy-efficient protocols were developed for Wireless Sensor Networks (WSN), including the LEACH Protocol. The LEACH protocol demonstrates a single cluster-based protocol by dividing available sensor nodes into sets and interacting with each set individually. The shape of an energy can be altered by compressing or expanding it, based on the cluster's configuration. We are comparing the network lifespans of three distinct versions of the LEACH protocol that utilize fuzzy techniques for cluster selection with the lifespan of WSNs generated by a previous version of the protocol.

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

PrioriNet

During disaster scenarios, effective communication systems are essential for coordinating emergency response efforts and ensuring the safety of affected individuals. However, existing communication protocols often face challenges in providing reliable and efficient communication in these highly dynamic and resource-constrained environments. To overcome these challenges a novel energy-efficient emergency priority protocol namely PrioriNet technique which specifically tailored for urban earthquake scenarios. The protocol focuses on prioritizing the transmission of emergency data packets to ensure their prompt and reliable delivery, while appropriately managing normal data packets. The PrioriNet prioritizes the emergency messages as high and low priority messages and allocate them to energy efficient nodes efficiently. The experimental results indicates that the suggested protocol performs better than the existing LEACH technique in terms of energy consumption, network coverage, packet delivery ratio, and throughput. In emergency data scenarios, the LEACH protocol demonstrates throughputs between 0.3 Mbps and 1.2 Mbps, whereas the proposed method consistently outperforms the LEACH protocol with throughputs ranging from 0.7 Mbps to 1.8 Mbps respectively.

An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks

An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks

Mathematical Modeling and Statistical Analysis of Leach Algorithm Based Cluster Head Selection Approach for Wireless Sensor Networks

In this paper, we present a comprehensive design and analysis of our proposed improved fuzzy logic-based algorithm. The algorithm utilizes a fuzzy logic system to assess node attributes such as residual energy, distance to the base station, and connectivity. By considering these parameters, the algorithm dynamically evaluates the most suitable candidates for cluster head roles, effectively distributing the energy load and promoting equitable cluster formation. An extensive performance evaluation of proposed algorithm has been conducted through simulations and comparisons with the original LEACH protocol. Our results reveal that the improved fuzzy logic-based algorithm outperforms the conventional LEACH in terms of network lifetime, energy consumption, and overall network stability. In this study, we delve into the mathematical modeling and statistical analysis of the proposed algorithm, focusing on its impact on energy efficiency, network stability, and data routing in WSNs. The Improved Fuzzy Logic-Based Cluster Head Selection Algorithm employs fuzzy logic to intelligently select cluster heads, considering parameters like residual energy, distance to the base station, and historical data. We develop mathematical models to describe the algorithm's behavior and analyze its performance through extensive simulations and statistical evaluations. The algorithm demonstrates enhanced adaptability to varying network conditions and node heterogeneity, showcasing its potential to significantly extend the operational duration of WSNs in diverse deployment scenarios. In conclusion, this paper contributes a novel perspective to the domain of WSNs by introducing an advanced fuzzy logic-based cluster head selection algorithm. The algorithm enhances the foundation of the LEACH protocol, effectively addressing energy optimization concerns and promoting prolonged network lifetime. The presented design and analysis substantiate the algorithm's superiority, offering valuable insights for researchers and practitioners working on energy-efficient routing protocols and wireless sensor network management.

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

HDRA: A Haybird Data Reduction and Routing Algorithm

Presently, wireless sensor networks (WSNs) are emerging as a vibrant field of research due to various challenging aspects such as energy consumption, routing strategies, effectiveness, among others. Despite unresolved issues within WSNs, a substantial array of applications has already been developed. For any application design, a primary objective is to optimize the WSN in terms of its lifecycle and functionality. Recent studies on data reduction methods have shown that sensor nodes often transmit data directly (single hop) to the base station (BS). However, a significant concern is that most existing multi-hop routing protocols do not address data reduction before forwarding data to the BS. Consequently, this study introduces a Hybrid Data Reduction and Routing Algorithm (HDRA). The principal aim of HDRA is to prolong the lifespan of cluster-based WSNs. It strives to decrease the packet transmission by sensor nodes, especially when there's minimal change in sensor readings. The findings indicate that HDRA outperforms the LEACH protocol in terms of energy efficiency in sensor networks, irrespective of network type (T, H, or TH) or deployment scenarios (200x200m or 400x400m). Overall, the proposed algorithm enhances network performance by conserving energy and extending network lifespan.

An Optimized Cluster Head Selection and Secured Wireless Sensor Network Using MRSA

Currently, Wireless Sensor Networks (WSNs) play a vital role in remote monitoring and sharing of information in an infrastructure. Even though WSNs have numerous advantages, their usage is reduced by node's lifetime and data loss. The aforementioned problems in WSNs were overcome by using energy efficient protocols by various researchers. But most of the authors focused on either trust model or energy efficient model, or analyzed both models using separate algorithms. However, this leads to repeated process and to high energy consumption. Here, the cluster head is selected by using the swarm intelligence algorithm and LEACH protocol. The hybrid ant colony and modified Particle swarm algorithm are used to select the cluster heads. The CH selection depends on the two parameters, namely residual energy and number of successful transmissions of node. The node's trust is assessed using these two parameters (transmission nodes and residual energy), which also assist in ensuring the safety of the data and energy. The proposed system uses a digital signature algorithm known as the modified RSA algorithm to achieve network security when performing cryptography on data. The performance obtained by the proposed method was compared to other existing techniques in terms of throughput, energy consumption, packet delivery ratio, and network lifetime.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.