Hierarchy Protocol Research Articles

Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems.

LEACH algorithm analysis and simulation using MATLAB

Research on Wireless Sensor Network (WSN) began to be carried out to meet various industrial needs including defense, health, environmental surveillance, and others. However, there are several obstacles in WSN, namely the problem of energy consumption which is the object of research by many researchers. The solution offered in this paper is to use the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a hierarchical protocol where this protocol focuses on saving energy use on WSN. This study used Matlab 2023 simulation software which used several measurement parameters to determine tissue life time, average residual energy, and throughput. The research scenario uses a homogeneous topology with three network sizes, namely 500 x 500, 750 x 750, and 1000 x 1000. Then also used three conditions for the number of sensor nodes, namely 100 nodes, 150 nodes, and 200 nodes. The results showed that the smaller the tissue size, the longer the life time and if the network size is wider, the network life time is shorter. The number of data packets transmitted depends on the number of active sensor nodes and sufficient energy to transmit.

Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field Games.

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach.

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

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.

Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.

Dual step hybrid routing protocol for network lifetime enhancement in WSN-IoT environment

Recent development in internet of things (IoT) has been generating huge data due to the large number of nodes deployed and utilized for different applications. In addition, these applications utilize big data and require a more efficient mechanism for data sensing and data transmission. This research work proposes dual step hybrid routing (DSHR) protocol for efficient cluster-based routing. It comprises a two-phase algorithm, which aims at finding the optimal path considering clustering. It further comprises several processes such as cluster head selection, optimal path construction, integrating of nodes to cluster head and sensing range optimization. DSHR is evaluated considering the network lifetime; thereafter model is compared with the existing low energy adaptive clustering hierarchy (LEACH) protocol to prove the efficiency.

ESMCH: An Energy-Saving, Multi-Hop, Clustering, and Hierarchy Protocol for Homogeneous WSNs

ESMCH: An Energy-Saving, Multi-Hop, Clustering, and Hierarchy Protocol for Homogeneous WSNs

Two-Level Clustering Algorithm for Cluster Head Selection in Randomly Deployed Wireless Sensor Networks

Clustering strategy in wireless sensor networks (WSNs) affects the lifetime, adaptability, and energy productivity of the wireless network system. The low-energy adaptive clustering hierarchy (LEACH) protocol is a convention used to improve the lifetime of WSNs. In this paper, a novel energy-efficient clustering algorithm is proposed, with the aim of improving the energy efficiency of WSNs by reducing and balancing the energy consumptions. The clustering-based convention adjusts the energy utilization by allowing an equal opportunity for each node to turn them into a cluster head (CH). Two-level clustering (TLC) is introduced by adopting LEACH convention where CH selection process undergoes first and second level of clustering to overcome boundary problem in LEACH protocol. The TLC method structures nodes within the scope of the appointed CHs, in order to extend the lifetime of the system. The simulation results show that, in comparison with state-of-the-art methodologies, our proposed method significantly enhanced the system lifetime.

Ad hoc wireless network implementing BEE-LEACH

Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Evaluation on Data Transmission in Wireless Sensor Networks Based on Computer Technology

Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.

Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks

The collection of data from the physical environment, such as temperature and humidity readings, is an essential function of Wireless Sensor Networks (WSNs). Because of its ability to communicate data wirelessly, WSNs are crucial for connecting the digital and physical worlds. Nevertheless, WSN energy efficiency optimization has become a more complicated challenge because of their battery dependency and deployment in adverse locations. The emphasis of this research is the Improved Low-Energy Adaptive Clustering Hierarchy (ILEACH) protocol, which is used as an example of a mathematics-driven strategy to improve WSN energy efficiency. To tackle important problems with energy consumption and network lifetime, mathematical modelling and equation-based analysis are used to study and enhance ILEACH's performance. Particularly in faraway places with few resources, energy efficiency is of the utmost importance when discussing WSNs. This study's mathematical models explore the equations and probability distributions that control the lifetime, energy consumption, and behaviour of sensor nodes. Our goal is to shed light on the energy optimization processes of ILEACH by means of thorough mathematical analysis and derivations. Our method incorporates both theoretical modelling and empirical assessments of ILEACH's cluster head formation, throughput, and network performance as a whole. Our comparison of the mathematical formulations of ILEACH and LEACH shows that our Improved LEACH is better in terms of data throughput, network lifetime, and energy efficiency. Using a figure of merits criterion, we compare LEACH, ILEACH, and the proposed Improved LEACH in detail to help with decision-making and protocol selection. With its solid mathematical groundwork, this study is a great tool for improving the efficiency of WSNs' energy-saving algorithms, which in turn can help networks last longer and run better.

Enhancing energy use efficiency of wireless sensor networks using newly proposed fault tolerance multipath routing protocol (MRP-FT)

The present study compared the newly proposed fault tolerance multipath routing protocol (MRP-FT) under the particle swarm optimization-based fault tolerant routing (PSO-FT) technique to the existing low-energy adaptive clustering hierarchy (LEACH) protocol applied in different combinations of inter-node numbers (N ) ranging from 100 to 300 nodes (N100−300) in wireless sensor networks (WSNs) with different area sizes (Xm × Ym) of 100–1000 m2 (WSN100−1000) to enhance energy efficiency and reliability of WSNs. The implementation of MRP-FT protocol significantly decreased the packet overhead by 36.0–69.5%, delay by 40.4–52.9%, and energy consumption by 35.9–52.9%, while increasing reliability by 97.0–104.1%, compared to the existing LEACH protocol. For instance, at N100−200, the LEACH simulated packet overhead increased from 76-89 packets at t = 500 s with an increase in WSN size from 100 to 1000. At N300 + WSN100, the packet overhead showed the highest decrease of ca. 69.5% with MRP-FT over the LEACH protocol. The implementation of the MRP-FT protocol reduced energy consumption by 35–123 J over the LEACH protocol.

𝑃c𝜀𝜅max-Means++: Adapt-𝑃 Driven by Energy and Distance Quality Probabilities Based on 𝜅-Means++ for the Stable Election Protocol (SEP)

Attaining a prolonged network lifetime, maximized coverage, and high performance are vital design factors that have to be maintained in a Wireless Sensor Networks (WSN). Such factors are dependent on the stability and optimality of the protocol employed to formulate Sensor Nodes (SNs) into mutual clusters that effectively work around fulfilling specific performance goals. SEP is a heterogeneity-aware protocol implemented based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, designed to prolong the stability period of the network defined by the time interval before the death of the first SN. Nevertheless, adaptability in design is a scheme that has not been extensively applied in the formation of WSN election protocols. Adapt-𝑃 is a probabilistic model for adaptivity designed to evolve the probability of selecting a cluster-head based on the active status of the WSN, represented by the residual energy of and distances between SNs. However, the adaptive probability 𝑃adp formalized in Adapt-𝑃 is developed based on the remaining number of SNs 𝜁 and optimal clustering κmax, yet 𝑃adp does not implement the probabilistic ratios of energy and distance factors in the network. Furthermore, Adapt-𝑃 does not localize cluster-heads in the first round properly because of its reliance on distance computations defined in LEACH, that might result in uneven distribution of cluster-heads in the WSN area and hence might at some rounds yield inefficient consumption of energy. This paper utilizes 𝑘-means++ and Adapt-𝑃 to propose 𝑃c𝜅max-means++ clustering algorithm that better manages the distribution of cluster-heads and produces an enhanced performance. The algorithm employs an optimized cluster-head election probability 𝑃c developed based on energy-based 𝑃𝜂(𝑗,𝑖) and distance-based 𝑃𝜓(𝑗,𝑖) quality probabilities along with the adaptive probability 𝑃adp, utilizing the energy 𝜀 and distance optimality 𝑑opt factors. Furthermore, the algorithm utilizes the optimal clustering 𝜅max derived in Adapt-𝑃 to perform adaptive clustering through 𝜅max-means++. The proposed 𝑃c𝜅max-means++ is compared with the energy-based algorithm 𝑃𝜂𝜀𝜅max-means++ and distance-based 𝑃𝜓𝑑opt𝜅max-means++ algorithm, and has shown an optimized performance in term of residual energy and stability period of the network.

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

Effect of Energy-Efficient Routing for Packet Splitting using Chinese Remainder Theorem (CRT) for Wireless Sensor Networks (WSNS)

The most popular Wireless Sensor Network (WSN) protocol utilized for energy optimization of WSN nodes is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, which is a basic energy-efficient routing protocol of hierarchical routing protocol’s family, where the sensor nodes remain static. There is a need to extend the lifetime with respect to reducing the energy consumption of nodes, and this was accomplished using the Chinese Remainder Theorem (CRT. In this research large data packets are broken into smaller packets. The focus of the study is to develop an energy routing for splitting packets in Wireless Sensor Networks (WSNs) using Chinese Remainder Theorem (CRT) techniques. A further improvement in WSN lifetime is by utilizing fuzzy logic. Factoring node parameters such as residual energy of nodes, their centrality within clusters, and their distance to the base station and improving upon the selection of nodes as cluster heads are required. The developed application can be used to reduce the communication cost of Energy efficiency of WSN. The forwarding technique for this research is to split the packet sent by the source node of a WSN so that the maximum number of bits per packet that a node has to transmit is significantly minimized.

Blockchain-enabled smart agriculture: Enhancing data-driven decision making and ensuring food security

Agriculture plays a vital role in global food security, but conventional practices have led to environmental degradation and health risks due to excessive use of agricultural nutrients and chemicals. In this era of increased consumer awareness and preference for organic food, the challenge lies in making informed choices amidst various crop production options. To address this, the present research introduces a groundbreaking study that utilizes blockchain technology to record and trace information on crop production from seeds to end-user consumption, enabling easy consumer choices. This study introduces a model that integrates Internet of Things (IoT) sensors to monitor the impact of selected chemicals and nutrients used in agriculture to enhance production yields. To ensure sustainability, the study employs the low-energy adaptive clustering hierarchy (LEACH) protocol, which optimizes energy consumption and network stability, thereby avoiding disruptions. Results indicate that different fertilizers affect soil physical properties such as temperature and moisture. These parameters are monitored using the Agriculture Vercel App, equipped with temperature, moisture, and light sensors in agricultural fields. The app provided threshold readings via alert notifications, revealing significant fluctuations in temperature (12%–43%) and moisture (83%–41%) concentrations upon the application of specific fertilizers like “Agricultural lime” and “Calcium Ammonium Nitrate.” Our IoT-based model is linked with private blockchain technology to prioritize consumer choice and facilitate transparency. This integration enhances food security while mitigating environmental degradation risks. By providing a comprehensive record of crop production practices, our model empowers consumers to make informed decisions aligned with their health and environmental concerns. This research demonstrates the potential of combining IoT and blockchain technologies to revolutionize agriculture, ensure sustainable practices, and promote food security. The findings offer significant implications for policymakers, farmers, and consumers, fostering a more sustainable and health-conscious approach to agriculture worldwide.

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

SummaryWireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively.