Energy Consumption Of Sensor Nodes Research Articles

An efficient dual layer data aggregation scheme in clustered wireless sensor networks

AbstractIn wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Relay node selection scheme and deep sleep period for power management in energy‐harvesting wireless sensor networks

SummaryThis paper presents Relay node selection scheme and Deep sleep period for power management in Energy Harvesting Wireless Sensor Networks (RD‐EHWSN), a new energy‐saving scheme founded on asynchronous duty cycling. RD‐EHWSN reduces sensor node energy consumption and guarantees equilibrium energy use between sensor nodes in WSN with the energy harvesting capacity by adjusting these sensor nodes duty cycles more drastically and deeply by according to the estimated value of its residual energy on the basis of future‐presented harvested energy, and this is done through the use of a new proposed energy threshold policy. RD‐EHWSN also grips the benefit of transmitter initiated using the low power listening (LPL) technique with short preamble messages and uses a new relay node selection procedure to achieve the load balancing in WSN. We implemented RD‐EHWSN by using OMNeT++/MiXiM. For evaluation, we compared it with PS‐EHWSN, under multiple concurrent multihop traffic flows scenarios and scenarios in which nodes can harvest different energy harvesting rate. In all experiments, RD‐EHWSN significantly outperformed the PS‐EHWSN scheme; the results of simulation demonstrate that our scheme enhances the general yielding of WSN thru lessening the energy consumption and the mean latency, as well as raising the packet delivery ratio and the throughput. Moreover, RD‐EHWSN improves the WSN lifetime and ensures it operates in good condition in the case where the energy harvesting rate is lower by comparing it with the PS‐EHWSN scheme.

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.

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

An energy consumption optimization strategy for Wireless sensor networks via multi-objective algorithm

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.

TEAM: A Layered-Cooperation Topology Evolution Algorithm for Multi-Sink Internet of Things

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

EAKRR : An energy-efficient key management in WSN with ECC-based cryptography

Environmental monitoring, industrial automation, and other applications require Wireless Sensor Networks (WSN). WSN must efficiently manage cryptographic keys for safe communication and energy efficiency. This study introduces “Energy-Aware Key Revocation and Rekeying” (EAKRR) to address energy efficiency and security in WSN using Elliptic Curve Cryptography (ECC). Energy-efficient key management is needed in WSN which have sensor nodes with limited resources and energy. EAKRR is designed to adapt to sensor node energy levels to make crucial key revocation and rekeying decisions while maintaining strong and resilient security. Energy profiling is used by EAKRR to assess sensor node energy consumption during cryptographic and operational operations. Energy conservation is achieved by localized key revocation when a node’s energy drops below a threshold. EAKRR also optimizes security and energy efficiency with ECC-based adaptive key length modifications. EAKRR is compared to LEACH and AES to determine its efficacy. The results show that EAKRR is highly energy efficient, extending the network’s lifespan and maintaining security. Cryptographic operations are careful and wise because they can adjust to energy levels in real time. The balance between security and energy usage shows that ECC-based encryption works. Energy-efficient key management in WSN is crucial. EAKRR solves WSN problems by saving energy, extending network life, and securing the network.

Energy Consumption Minimization in Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

Height-Fixed UAV Enabled Energy-Efficient Data Collection in RIS-Aided Wireless Sensor Networks

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time.

MO-CBACORP: A new energy-efficient secure routing protocol for underwater monitoring wireless sensor network

With the rapid progress of sensor technology, underwater monitoring wireless sensor network (UMWSN) equipped with water resource monitoring capabilities have surfaced. UMWSN is currently emerging as a popular research and application area, with a wide range of applications in various fields, including but not limited to disaster warning, pollutant monitoring and hydrological data monitoring. However, due to the limited size and computing power of wireless sensors, UMWSN are exceedingly susceptible to network security attacks by contaminating entities. In order to tackle the problems of inadequate security, elevated network energy consumption, and high system latency in existing QoS routing research, this paper puts forward an innovative multi-objective routing protocol named multi-objective chaotic Boltzmann ant colony optimization routing protocol (MO-CBACORP). MO-CBACORP can minimize the energy consumption of sensor nodes in UMWSN routing, thereby effectively diminishing system latency and significantly enhancing routing security. During the routing optimization process, MO-CBACORP proposed an innovative design of the chaos operator and Boltzmann selection strategy, which effectively avoids the issue of being stuck in local optimal solutions and greatly enhances the convergence speed of the algorithm. Additionally, a novel experiment for underwater monitoring environments is presented that precisely replicates underwater monitoring conditions and attack scenarios. According to the experimental results, MO-CBACORP exhibits superior performance compared to the latest state-of-the-art research including THOA-MHR, ACO-MCMC, and MAP-ACO, with a minimum improvement of 7.25%, 9.31%, and 7.68% in terms of route security, network energy consumption, and delay, respectively. All in all, MO-CBACORP exhibits superior performance compared to the three latest research algorithms.

An Advanced Energy-Efficient Environmental Monitoring in Precision Agriculture Using LoRa-Based Wireless Sensor Networks.

Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment.

Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage

Sensor nodes in a wireless sensor network are distributed across an area for data collection. These nodes have basic capabilities in terms of interfaces and components, and they often operate in dynamic, hostile environments. Sensor networks present numerous challenges: they are dispersed, generate constant high rates data streams, function in dynamic and time-changing situations; and may involve a large number of sensors. Sensor nodes have enough power to transmit their readings to a central high-performance computing unit for processing. Sensor networks generate data streams, which are sequences of real-time data records characterized by their high data rates that consume significant network computing resources. However, only a few studies address the issue of collecting highly redundant data, leading to nodes wasting energy by sending redundant information to a central high-performance computing unit. Improved scheduling tactics can help reduce energy consumption in sensor nodes. This research developed a Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage (NSPC). Partial coverage can be obtained by dividing the area of interest into smaller sub-regions and determining the monitoring intensity for each sub-region by sensor nodes. Various strategies, such as clustering and scheduling, can be employed to accomplish partial coverage. Considering partial coverage when designing a WSN is crucial, as it can enhance network stability and reliability while reducing the cost and energy consumption of each sensor nodes.

On the Use of Wireless Technologies for Wildlife Monitoring: Wireless Sensor Network Routing Protocols

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in WSNs. The use of source location privacy (SLP) routing protocols was considered for secure wildlife monitoring in areas such as game reserves. In such areas, sensor node energy consumption minimization and battery lifetime maximization are crucial. Hence, energy-efficient SLP protocols are more suitable for deployment. Using MATLAB simulation environment, performance analysis of various phantom- based SLP protocols was done to identify effective and energy-efficient SLP protocols. Simulation results show that two-level phantom with a backbone route protocol (TBP) and phantom with angle protocol (PAP) exhibit advantageous performance features in terms of SLP protection, energy efficiency, effective long-term SLP protection, and scalability. Thus, TBP and PAP are suitable for deployment in wildlife monitoring WSNs.

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.

Modelling and Analysis of a Sigfox-Based IoT Network Using uppaalsmc

Wireless sensor nodes are usually powered by batteries that have limited energy capacity. In many applications, the nodes are installed in inaccessible locations where they are problematic to replace or recharge. Therefore, energy optimisation is crucial for increasing the node’s lifetime. This study presents a method for the analysis and prediction of the energy consumption of Sigfox-based Wireless Sensor Nodes. The method is illustrated in a use case where the nodes monitor the water level in drainage lines in cities to improve surface and wastewater management. We propose a formal model-based technique using the UPPAAL Statistical model checker tool to model and analyse the node’s lifetime. Statistical model checking provides a highly scalable technique for performance analysis of complex cyber-physical systems. The model captures the energy-related behaviour of the node, the Sigfox radio specification, and the sensor, each parameterised with values from the device’s datasheets. Furthermore, we calibrate the model using measurements obtained from real-world hardware. Finally, we evaluate a collection of strategies to optimise the battery lifetime of the node. We simulate the model with a 10,000 mAh battery, and the results indicate that we can extend the node’s lifetime from 202 days to 2.71 years using our most optimised transmission strategy.

Weight-imposed Elite Hybrid Binary Cuckoo Search (EHBCS)-based clustering for improving network lifetime longevity

In Wireless Sensor Networks (WSNs), Clustering aids in maximizing the lifetime of the network with sustained energy stability in the sensor nodes during data dissemination. In this clustering process, the sensor nodes are organized into clusters with the potential fitness node designated as Cluster Heads (CHs) for collecting and forwarding the data to the sink. In specific, the energy consumption of sensor nodes during their role as CH is maximized with great impact over the network lifespan. In this paper, a Weight-imposed Elite Hybrid Binary Cuckoo Search (EHBCS)-based Clustering Mechanism is proposed for facilitating potent data transmission with minimized energy consumption and improved network lifetime. This EHBCS is proposed as a novel energy-sensitive CH selection framework based on the process of hierarchical routing through the inclusion of hybrid optimization algorithm. It selected CH depending on the parameters of Quality of Service (QoS), delay, distance, and energy into account. It integrated the merits of Binary Cuckoo Search and Elite Mechanism for selecting CHs and performing effective processes by preventing sinkhole issues in WSNs. The results of EHBCS confirmed better throughout by 11.32%, minimized energy consumption by 13.84%, and minimized delay by 16.12% with an increasing number of sensor nodes, compared to the baseline CH selection approaches used for exploration.

A novel approach based on bio‐inspired efficient clustering algorithm for large‐scale heterogeneous wireless sensor networks

SummaryIn large‐scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K‐means clustering algorithm and optimised K‐means based modified intelligent CH selection based on BFOA for large‐scale network (lar‐OK‐MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK‐MICHB algorithm for large‐scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar‐OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi‐level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment.