Ideal Cluster Research Articles

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Optimizing cluster spacing in multistage hydraulically fractured shale gas wells: balancing fracture interference and stress shadow impact

Horizontal drilling and multistage hydraulic fracturing have seen widespread application in shale formations during the past decade, leading to significant economic productivity gains through the creation of extensive fracture surfaces. The determination of the ideal cluster spacing in shale gas wells is contingent upon the unique geological and formation characteristics. Generally, reducing the spacing between clusters has the potential to augment gas recovery, albeit at the expense of higher drilling and completion costs, as well as the influence of stress shadows on fracture propagation. This study introduces an integrated methodology designed to explore the impact of cluster interference on well performance. Commencing with a fracture propagation model accommodating stress shadow effects for an equivalent slurry volume injection, analytical rate transient analysis (RTA) was amalgamated with reservoir numerical simulation to compute the effective fracture surface area (Aca.) for hydrocarbon production. The correlation between the effective fracture surface area determined by RTA and the actual stimulated fracture area (Aca.) derived from numerical simulations was established in relation to cluster spacing. The findings of this research reveal that wells featuring a greater number of stages and tighter cluster spacing tend to exhibit elevated cluster interference, resulting in a lower effective-to-actual fracture surface area ratio and heightened stress shadow effects impeding fracture propagation. A cluster spacing of 33 feet with six clusters per stage emerges as the optimal choice at formation permeability of 0.00005 md that decreased to 18 ft at formation permeability of 0.00001 md. ACe either stabilizes or decreases above the optimal value, suggesting that more clusters would not have a major impact on increasing the effective stimulated area. Allowing 20% interference, regardless of the permeability of the formation, maximized cumulative production while preventing thief zones and excessive cluster interference. The insights gained from this study will serve as a valuable resource for completion and reservoir engineers, enabling them to fine-tune cluster spacing to maximize well revenue in the dynamic landscape of shale gas extraction.

Partitioned uneven cluster routing algorithm based on gray wolf optimization in WSNs

WSNs have various uses across numerous industries and are one of the essential technologies of modern life. Energy consumption is the issue that has drawn the greatest attention and still has to be resolved because the nodes that comprise WSNs have a limited amount of energy. Numerous factors influence energy consumption, and our algorithm design considerations are centered on extending the network lifetime and energy efficiency through the resolution of imbalance and hotspot issues related to WSNs clustering. Because of this, we suggest a partitioned uneven cluster routing algorithm based on gray wolf optimization. To find the ideal cluster head, we first divide the network into areas with distinct important influence factors, then we improve the final cluster head election function and the candidate cluster head competition radius. Subsequently, to reduce the energy consumption resulting from multiple rounds of clustering, similarity determination is introduced. Finally, the optimal transmission path in the multi-hop process is obtained by combining the Gray Wolf optimization algorithm with the relay node selection function. Simulation results show that the network lifetime of the proposed algorithm is extended by 54.6 %, 46.2 %, 58.6 %, and 18.5 % compared to LEACH, DEBUC, LEACH-EDP, and LEACH-IM, respectively. The energy efficiency of the proposed algorithm is extended by 40.8 %, 7.1 %, 22.7 %, and 34.0 %, respectively. The proposed algorithm significantly extends the network lifetime and improves the energy efficiency of the network.

Numerical Analysis of the Stress Shadow Effects in Multistage Hydrofracturing Considering Natural Fracture and Leak-Off Effect

As a critical technological approach, multistage fracturing is frequently used to boost gas recovery in compact hydrocarbon reservoirs. Determining an ideal cluster distance that effectively integrates pre-existing natural fractures in the deposit creates a fracture network conducive to gas movement. Fracturing fluid leak-off also impacts water resources. In our study, we use a versatile finite element–discrete element method that improves the auto-refinement of the grid and the detection of multiple fracture movements to model staged fracturing in naturally fractured reservoirs. This computational model illustrates the interaction between hydraulic fractures and pre-existing fractures and employs the nonlinear Carter leak-off criterion to portray fluid leakage and the impacts of hydromechanical coupling during multistage fracturing. Numerical results show that sequential fracturing exhibits the maximum length in unfractured and naturally fractured models, and the leak-off volume of parallel fracturing is the smallest. Our study proposes an innovative technique for identifying and optimizing the spacing of fracturing clusters in unconventional reservoirs.

Evaluating Pauli Errors on Cluster States by Weighted Distances

Evaluating Pauli Errors on Cluster States by Weighted Distances

A hybrid fennec fox and sand cat optimization algorithm for clustering scheme in VANETs

The popularity of intelligent vehicles with cutting-edge vehicular applications has fueled the rapid expansion of Vehicular Ad hoc Networks (VANETs) in recent years. VANETs are a network of vehicles designed to exchange and explore real-time data using a well-developed and effectively organized data transport technology. However, the major issue of dynamic topology and cluster stability always has an impact on choosing an optimal path between the cars. At this point, an intelligent clustering technique in VANETs that handles dynamic topology and cluster stability is critical for efficient route selection between vehicular nodes. This is an NP-hard issue that can be effectively solved using an intelligent nature-inspired algorithm that can discover near-optimal solutions in the search space. An Intelligent Hybrid Fennec Fox and Sand Cat Optimization Algorithm (HFFSCOA) -Based Clustering Scheme is proposed in this paper as a novel route clustering optimization strategy that takes grid size, orientation, velocity node density, and communication range into account while achieving its goal. This HFFSCOA contributed to the route clustering process, which determines dependable and optimal routes between vehicular nodes for the purpose of building and evaluating ideal Cluster Heads (CHs) in the network. HFFSCOA's findings clearly demonstrated its usefulness and efficacy in terms of the number of vehicles, network size, changeable communication ranges, and number of clusters built in the network. The statistical results of HFFSCOA also confirmed an enhanced cluster Optimization rate of 56.21% and an increased cluster stability of 92.34.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

Influence of Sphericity on Surface Termination of Icosahedral Colloidal Clusters.

Colloids self-organize into icosahedral clusters composed of a Mackay core and an anti-Mackay shell under spherical confinement to minimize the free energy. This study explores the variation of surface arrangements of colloids in icosahedral clusters, focusing on the determining factors behind the surface arrangement. To efficiently assemble particles in emulsion droplets, droplet-to-droplet osmotic extraction from particle-laden droplets to salt-containing droplets is used, where the droplets are microfluidically prepared to guarantee a high size uniformity. The icosahedral clusters are optimally produced during a 24-h consolidation period at a 0.04m salt concentration. The findings reveal an increase in the number of particle layers from 10 to 15 in the icosahedral clusters as the average number of particles increases from 3300 to 11 000. Intriguingly, the number of layers in the anti-Mackay shells, or surface termination, appears to more strongly depend on the sphericity of the clusters than on the deviation in the particle count from an ideal icosahedral cluster. This result suggests that the sphericity of the outermost layer, formed by the late-stage rearrangement of particles to form an anti-Mackay shell near the droplet interface, may play a pivotal role in determining the surface morphology to accommodate a spherical interface.

Jarvis-Patrick-Clusterative African Buffalo Optimized Deep Learning Classifier for Device-to-Device Communication in 5G Networks

With the fifth generation (5G), there are several mobile devices may connect to the network from any location. Device-to-device (D2D) communication is a crucial component of 5G wireless networks since it increases performance. D2D communication in the 5G networks is the subject of extensive study. However, the existing D2D communication techniques did not show an improvement in energy economy or a reduction in latency. The Jarvis-Patrick-Clusterative African Buffalo Optimised Deep Learning (JPCABODL) Model was created to overcome the problems. The JPCABODL Model's main goal is to improve D2D communication performance in 5G networks using a deep learning classifier with improved energy efficiency and lower latency. Four layers make up the JPCABODL Model: one output layer, two hidden levels, and one layer for device-to-device communication. A layer of input is the total number of mobile devices. After that, each mobile device's received signal strength (RSS) and residual energy are taken into account when the Jarvis-Patrick-Clustering procedure is carried out at hidden layer 1. After that, each cluster's cluster head is selected from mobile devices with the highest residual energy, bandwidth, and RSS. The cluster is then transmitted to hidden layer 2 together with information about the cluster leader. African Buffalo Optimisation is used in that layer to choose the ideal cluster head. In 5G networks, effective device-to-device communication is accomplished with the aid of an ideal cluster head. The JPCABODL Model and current techniques are used for simulation, and several metrics such as energy efficiency, data delivery ratio, packet loss rate, throughput, and latency are measured. When compared to traditional approaches, the experimental evaluation of the JPCABODL Model enhances EE, DDR, and minimizes latency.

Constraints on thermal conductivity in the merging cluster Abell 2146

ABSTRACT The cluster of galaxies Abell 2146 is undergoing a major merger and is an ideal cluster to study intracluster medium (ICM) physics, as it has a simple geometry with the merger axis in the plane of the sky, its distance allows us to resolve features across the relevant scales and its temperature lies within Chandra’s sensitivity. Gas from the cool core of the subcluster has been partially stripped into a tail of gas, which gives a unique opportunity to look at the survival of such gas and determine the rate of conduction in the ICM. We use deep 2.4-Ms Chandra observations of Abell 2146 to produce a high spatial resolution map of the temperature structure along a plume in the ram-pressure stripped tail, described by a partial cone, which is distinguishable from the hot ambient gas. Previous studies of conduction in the ICM typically rely on estimates of the survival time for key structures, such as cold fronts. Here we use detailed hydrodynamical simulations of Abell 2146 to determine the flow velocities along the stripped plume and measure the time-scale of the temperature increase along its length. We find that conduction must be highly suppressed by multiple orders of magnitude compared to the Spitzer rate, as the energy used is about 1 per cent of the energy available. We discuss magnetic draping around the core as a possible mechanism for suppressing conduction.

Fuzzy Enhanced Black Widow Spider with Secure Encryption Random Permutation Pseudo Algorithm for Energy Efficient Cluster Communication in WSN

Devices connected to the Internet of Things (IoT) are increasingly being used. Modern mobile apps may be incorporated onto low-cost, low-power devices with the help of these extensions. This integration is made possible by the utilization of low-cost, low-power sensor nodes. All sensor nodes transmit data either directly or via a multi-hop path to other receivers or nodes. In order to enable effective inter-cluster communication through ideal cluster head selection, we build a fuzzy-enhanced black widow spider based on the Secure Encrypted Random Permutation Pseudo Algorithm (FEBWS-SERPPA) in this study. By taking energy, latency, and distance characteristics into account, the suggested FEBWS-SERPPA algorithm enhances the black widow spider optimization method by choosing the optimal cluster head in the cluster. To ensure its effectiveness, the suggested FEBWS-SERPPA algorithm's performance is compared to the state-of-the-art. The suggested FEBWS-SERPPA algorithm delivers enhanced performance speed with very low energy consumption and extended network lifetime when compared to existing cutting-edge methods.

Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol

In this era of technological advancements, our globe has witnessed the tremendous potential of wireless technology, which has revolutionized data transport. One area with a lot of promise for increasing the usage of this technology is traffic control. Real-time path planning algorithms have emerged as an important tool for reducing traffic congestion in urban areas, addressing current state-of-the-art. Real-time traffic data collection, which leverages the capabilities of Vehicle Ad-Hoc Networks (VANETs), offers opportunities to reduce travel costs and complexity. Our study offers an optimized cluster tree-based routing protocol (OCTRPL) within the framework of an advanced IoT-based computational energy transportation system to accomplish this. Vehicles serve as network nodes in this architecture, while traffic stations serve as hubs and routers. We assure the efficient implementation of a shortest-path strategy by using the OCTRPL, thereby lowering data traffic between network nodes and increasing the network's overall lifespan. The Euclidean distance facilitates the development of an ideal cluster, allowing for the selection of Cluster Heads (CHs) using a game theoretic method. Following that, criteria such as expected transmission count, queue utilization ratio, and residual energy ratio (RER) are considered in the route selection process. This set of criteria, known as ETC, assists in selecting the best path for efficient energy transit throughout the network. We illustrate the outstanding performance of our suggested approach in meeting its intended aims by a comparative study of data. The sophisticated IoT-based computational energy transportation system, in conjunction with the efficient wireless tree-based routing protocol, demonstrates promising results and opens up new options for further research and development in this sector.

Fast Markov Clustering Algorithm Based on Belief Dynamics.

Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods.

Slice Visualization for Imaging Nanocluster Transformations.

Metal nanoclusters have served as an emerging class of modular nanomaterials. Several efficient strategies have been proposed for transforming cluster precursors into new nanoclusters with customized structures and enhanced performance. However, such nanocluster transformations have still been in a "blind box" state, meaning that the existing intermediates were hard to track with atomic precision. Herein, we present a "slice visualization" approach for in-depth imaging of the nanocluster transformation from Au1Ag24(SR)18 to Au1Ag30(SR)20. With this approach, two cluster intermediates, namely, Au1Ag26(SR)19 and Au1Ag28(SR)20, were monitored with atomic precision. The four nanoclusters constituted a correlated Au1Ag24+2n (n = 0, 1, 2, and 3) cluster series with comparable structural features─the same Au1Ag12 icosahedral kernel but evolutionary peripheral motif structures. The mechanism of nanocluster structure growth was mapped in detail─insertion of Ag2(SR)1 or Ag-induced assembly of surface subunits. The presented "slice visualization" approach not only contributes an ideal cluster platform for in-depth investigations of structure-property correlations but also hopefully acts as a powerful means for obtaining clear information on nanocluster structure evolution.

STEM-EELS/EDS Chemical Analysis of Solute Clusters in a Dilute Mille-Feuille-Type Mg–Zn–Y Alloy

Dilute Mg-Zn-Y alloy with a mille-feuille structure (MFS) exhibits a mechanical strength comparable to Mg-Zn-Y alloy with long period stacking/ordered (LPSO) structure through kink deformation. In order to deepen understanding the thermal stability of the MFS-type Mg alloys, it is required to clarify the solute cluster structures composed of Zn and Y in solute enriched stacking faults (SESFs). In this study, electron energy-loss and energy dispersive X-ray spectroscopy based on scanning transmission electron microscopy (STEM-EELS/EDS) were conducted to investigate the electronic structure and composition of Zn and Y in the SESFs of the MFS-Mg alloy. Zn-L2,3 spectra indicated that the valence charges of Zn in the dilute Mg alloy were different from that of the LPSO-type Mg-Zn-Y alloy. In addition, the intensity ratio of L3/L2 in Y-L2,3 spectrum of the dilute MFS-Mg alloy was larger than that of the LPSO-Mg alloy, reflecting the electron occupancies of 4d3/2 and 4d5/2 orbitals of Y atoms were different from those of the LPSO-Mg alloys. STEM-EELS analysis of the SESF composition in the dilute MFS-Mg alloy indicated that the Zn/Y ratio should be lower than that of the LPSO-Mg alloy, which was confirmed also by STEM-EDS measurements. These results indicate that the cluster structure in the SESFs of the dilute MFS-Mg alloy should be different from the ideal Zn6Y8 cluster in the LPSO-type Mg-Zn-Y alloys.

On the Ideal Convergent Sequences in Fuzzy Normed Space

This article discusses a variety of important notions, including ideal convergence and ideal Cauchyness of topological sequences produced by fuzzy normed spaces. Furthermore, the connections between the concepts of the ideal limit and ideal cluster points of a sequence in a fuzzy normed linear space are investigated. In a fuzzy normed space, we investigated additional effects, such as describing compactness in terms of ideal cluster points and other relevant but previously unresearched ideal convergence and adjoint ideal convergence aspects of sequences and nets. The countable compactness of a fuzzy normed space and its link to it were also defined. The terms ideal and its adjoint divergent sequences are then introduced, and specific aspects of them are explored in a fuzzy normed space. Our study supports the importance of condition (AP) in examining summability via ideals. It is suggested to use a fuzzy point symmetry-based genetic clustering method to automatically count the number of clusters in a data set and determine how well the data are fuzzy partitioned. As long as the clusters have the attribute of symmetry, they can be any size, form, or convexity. One of the crucial ways that symmetry is used in fuzzy systems is in the solution of the linear Fuzzy Fredholm Integral Equation (FFIE), which has symmetric triangular (Fuzzy Interval) output and any fuzzy function input.

A comprehensive modeling approach for polymorph selection in Lennard-Jones crystallization.

Computational predictions of the polymorphic outcomes of a crystallization process, referred to as polymorph selection, can accelerate the process development for manufacturing solid products with targeted properties. Polymorph selection requires understanding the interplay between the thermodynamic and kinetic factors that drive nucleation. Moreover, post-nucleation events, such as crystal growth and polymorphic transformation, can affect the resulting crystal structures. Here, the nucleation kinetics of the Lennard-Jones (LJ) fluid from the melt is investigated with a focus on the competition between FCC and HCP crystal structures. Both molecular dynamics (MD) simulations and 2D free energy calculations reveal that polymorph selection occurs not during nucleation but when the cluster sizes exceed the critical cluster size. This result contrasts with the classical nucleation mechanism, where each polymorph is assumed to nucleate independently as an ideal bulk-like cluster, comprised only of its given structure. Using the 2D free energy surface and the MD simulation-derived diffusion coefficients, a structure-dependent nucleation rate is estimated, which agrees with the rate obtained from brute force MD simulations. Furthermore, a comprehensive population balance modeling (PBM) approach for polymorph selection is proposed. The PBM combines the calculated nucleation rate with post-nucleation kinetics while accounting for the structural changes of the clusters after nucleation. When applied to the LJ system, the PBM predicts with high accuracy the polymorphic distribution found in a population of crystals generated from MD simulations. Due to the non-classical nucleation mechanism of the LJ system, post-nucleation kinetic events are crucial in determining the structures of the grown crystals.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

Developed cluster-based load-balanced protocol for wireless sensor networks based on energy-efficient clustering

One of the most pressing issues in wireless sensor networks (WSNs) is energy efficiency. Sensor nodes (SNs) are used by WSNs to gather and send data. The techniques of cluster-based hierarchical routing significantly considered for lowering WSN’s energy consumption. Because SNs are battery-powered, face significant energy constraints, and face problems in an energy-efficient protocol designing. Clustering algorithms drastically reduce each SNs energy consumption. A low-energy adaptive clustering hierarchy (LEACH) considered promising for application-specifically protocol architecture for WSNs. To extend the network's lifetime, the SNs must save energy as much as feasible. The proposed developed cluster-based load-balanced protocol (DCLP) considers for the number of ideal cluster heads (CHs) and prevents nodes nearer base stations (BSs) from joining the cluster realization for accomplishing sufficient performances regarding the reduction of sensor consumed energy. The analysis and comparison in MATLAB to LEACH, a well-known cluster-based protocol, and its modified variant distributed energy efficient clustering (DEEC). The simulation results demonstrate that network performance, energy usage, and network longevity have all improved significantly. It also demonstrates that employing cluster-based routing protocols may successfully reduce sensor network energy consumption while increasing the quantity of network data transfer, hence achieving the goal of extending network lifetime.