Centralized Clustering Research Articles

Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.

Federated Momentum Contrastive Clustering

Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate a global model while training on distributed local data, respecting the data privacy of edge devices. However, most FL research effort focuses on supervised learning algorithms. A fully unsupervised federated clustering scheme has not been considered in the existing literature. We present federated momentum contrastive clustering (FedMCC), a generic federated clustering framework that can not only cluster data automatically but also extract discriminative representations training from distributed local data over multiple users. In FedMCC, we demonstrate a two-stage federated learning paradigm where the first stage aims to learn differentiable instance embeddings and the second stage accounts for clustering data automatically. The experimental results show that FedMCC not only achieves superior clustering performance but also outperforms several existing federated self-supervised methods for linear evaluation and semi-supervised learning tasks. Additionally, FedMCC can easily be adapted to ordinary centralized clustering through what we call momentum contrastive clustering (MCC). We show that MCC achieves state-of-the-art clustering accuracy results in certain datasets such as STL-10 and ImageNet-10. We also present a method to reduce the memory footprint of our clustering schemes.

An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW is compared to MWCRSF, DCM, and GWO, and the evaluation results are presented in two scenarios, namely network evaluation in the clustering process and network evaluation in the routing process. Accordingly, in the first scenario, ICW has low clustering time and a high cluster lifetime. In the second scenario, ICW optimizes energy consumption, network longevity, packet delivery rate, routing overhead, and delay compared to other approaches. However, throughput in ICW is about 3.9% lower than that in MWCRSF.

On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified Salp Swarm Algorithm for Centralized Clustering and Routing

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified Salp Swarm Algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

Dual-Tier Cluster-Based Routing in Mobile Wireless Sensor Network for IoT Application

Mobile wireless sensor network (MWSN) technology is a fundamental element of the Internet of Things (IoT) in which hundreds to thousands of sensor nodes (SNs) are connected via wireless channels capable of providing a digital interface to real-life objects. Energy consumption, connectivity, scalability, and security are the main challenges in MWSN, and mobility increases the effort required to find an efficient routing protocol to improve the MWSN performance. In this paper, we propose a novel routing protocol based on the dual-tier clustering concept and virtual network zones to improve MWSN performance. The proposed protocol named “Dual Tier Cluster-Based Routing” (DTC-BR) divides the network area into virtual zones which a cluster-head mechanism selects the most appropriate SN to act as Cluster Head (CH). Furthermore, virtual zones are designed to cover the entire network area based on a dual-tier routing mechanism: the main connectivity zone (MCZ) and candidate cluster zone (CCZ). The DTC-BR protocol was deployed and assessed using MATLAB, assuming three levels: energy consumption, network lifetime, and scalability. The comparative results demonstrate the efficiency of DTC-BR, where the network lifetime increased by 6%, 21%, 25%, and 37% compared to state-of-the-art dynamic directional routing (DDR), mobility-aware centralized clustering algorithm (MCCA), low-energy adaptive clustering hierarchy-mobile energy efficient and connected (LEACH-MEEC), and low-energy adaptive clustering hierarchy mobile (LEACH-M) protocols, respectively. In addition, the simulation results show that DTC-BR is more efficient for large network sizes and a high number of SNs.

Distributed Sensor Clustering Using Artificial Neural Network With Local Information

Internet of Things (IoT) has facilitated the emergence of various applications which require deploying a large number of sensors over a wide geographical area. For efficient communications, sensors with similar data within a vicinity are grouped into clusters. Compared to centralized sensor clustering, a distributed scheme is more scalable, can avoid traffic congestion, and does not suffer from single-point failure. However, due to a lack of global information, distributed sensor clustering may create more clusters than a centralized scheme. In this article, we propose to use an artificial neural network (ANN) as a tool to summarize the experience of a centralized clustering scheme in the presence of global information. Then, this experience becomes a learned knowledge to be transferred to distributed decision makers, which may subsequently approximate the centralized scheme in making the clustering decision even with access to only local information. To achieve the desired performance, we derive some secondary local information from original local information, and use it as input to ANNs to compensate for the loss of global information. Evaluation results show that it is feasible in achieving at a distributed decision maker, a same clustering solution as the centralized scheme, with about 5% clustering error probability.

Low-Energy Clustering Protocol for Query-Based Wireless Sensor Networks

Nowadays, clustering protocols have been widely used to reduce energy consumption in wireless sensor networks (WSNs). However, when existing clustering protocols are used for query-based WSNs, the energy efficiency becomes limited because in such networks, clusters are formed without considering the positional distribution of query targets, and the procedures of node clustering, cluster head selection and inter-cluster routing are performed in a distributed way, which is energy-consuming. In this paper, a centralized clustering protocol, called Low-Energy Dynamic Clustering (LEDC), is developed to enhance the energy efficiency in query-based WSNs. The main contributions of LEDC include: (1) a novel prediction-based scheme is raised to make the centralized network information maintenance energy-efficient and scalable; (2) centralized optimal dynamic cluster formation algorithms are proposed to cluster the query targets in a centralized way, which not only helps the distribution of clusters match with that of query targets but also saves the high cost incurred in the distributed procedures of node clustering, cluster head selection and inter-cluster routing; (3) an energy-aware forwarding strategy is suggested to improve the energy efficiency and balance the energy load among nodes in inter-cluster communication; (4) a parallel processing algorithm is raised to increase the network throughput and reduce the latency of queries. The performance of LEDC is then compared to the latest clustering protocol. Simulation results demonstrate that LEDC considerably saves the energy of nodes and prolongs the network lifetime.

Cauchy Density-Based Algorithm for VANETs Clustering in 3D Road Environments

Vehicular ad hoc networks (VANETs) are emerging to serve various types of applications for serving smart cities and intelligent transportation systems. There are several challenging factors for ensuring reliable and stable VANETs communications. VANETs clustering is essential functionality to serve routing protocols and in enable reliable VANETs. Clustering algorithms for VANETs operate in decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create suboptimality due to the local nature of the decentralized approach. In addition, the challenging architecture of the road environment can cause confusing clustering decisions. This problem becomes more challenging due to the evolving nature of clusters in VANETs in general and in 3D VANETs in particular. This paper tackles the problem of 3D VANETs using a centralized clustering model based on the developed Cauchy density model. The development has included a traffic generation model, mobility model for generating driving behavior, and an algorithm for enabling modeling of road curvature based on an adjacency list that defines the road’s points that define a straight-line segment. In addition, a clustering algorithm that defines mobility vector and uses Cauchy-based density for enabling adding vehicles to their respective clusters. For evaluation, conducted scenarios in three locations with 3D architecture are considered, and a comparison with benchmarks shows the superiority of our developed over the benchmarks with an improvement percentage of 1%, 10%, and 3% for average cluster head duration, average cluster member duration, and clustering efficiency respectively.

Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks

5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN) will have an alarming impact on throughput, interference and latency. To ensure high throughput with reduced interference in UDNs a clustered architecture is required. A HetNet with clustered approach enables the network to mitigate interference effectively and achieve efficient radio resource management (RRM). In this paper, we analyzed different clustering classifications and existing clustering techniques that are used for proficient radio resource management. The centralized clustering techniques and decentralized clustering techniques are analyzed and as a result, it is assumed that improved performance can be achieved by emphasizing on hybrid clustering approaches. In addition to this, performed a thoughtful review of existing hybrid clustering techniques to achieve improved throughput and mitigate interference in dense heterogeneous networks. Our analysis shows that improved radio resource management and increased throughput in HetNets is achieved by applying hybrid clustering techniques with reduced inter and intra tier interference.

EEHCHR: Energy Efficient Hybrid Clustering and Hierarchical Routing for Wireless Sensor Networks

In cluster-based Wireless Sensor Network (WSN), the Cluster Heads (CHs) consume massive amount of energy due to uneven processing and routing of the information of their Cluster Members (CMs) to the Base Station (BS), and results in lower network lifetime e.g., REHR (Residual Energy based Hybrid Routing). Also, the centralized clustering algorithms introduce unnecessary consumption of energy e.g., FFTHR (Fitness Function based Two-Hop Routing) and UCRA-GSO (Uneven Clustering Routing Algorithm using Glowworm Swarm Optimization). Thus, for extending the network lifetime, we introduce a new clustering algorithm named as Energy Efficient Hybrid Clustering and Hierarchical Routing (EEHCHR) in WSN. Here, a new scheme of adaptive and hybrid clustering has been proposed for minimum usage of the node’s energy using the Euclidean distance parameter, Fuzzy C-Means (FCM) technique, location of BS, and residual energy of the nodes. Here, the clustering is performed only in a few rounds, and this results in reduction of energy consumption of the network. All the CHs are selected using the energy efficient fitness function, which works in an adaptive way with the residual energy of the nodes to improve the CH selection process. For efficient usage of the network’s energy, we have also given a hierarchical packet routing strategy by introducing the concept of DCH (Direct Cluster Head) and CCH (Central Cluster Head), which are selected by different fitness functions, and work as a relay for few other CHs. The simulation results of EEHCHR proved that it extends the network lifetime, coverage, and saves the network energy as compared to similar existing algorithms e.g., FCM, REHR, FFTHR, UCRA-GSO and CCA-GWO.

HETA: end-to-end delay analysis of enhanced centralized clustering protocol for wireless sensor networks

HETA: end-to-end delay analysis of enhanced centralized clustering protocol for wireless sensor networks

A novel method of mobility-based clustering protocol in software defined sensor network

The distributed clustering method is widely used to extend network lifetime in traditional wireless sensor networks. However, it is difficult to achieve the idea performance of the whole network, such as transmission rate, energy consumption, and control overhead, neglecting the global stability of the network. To tackle this problem, a centralized mobility-based clustering (CMBC) protocol is proposed in the software defined sensor network. The method of CMBC mainly consists of two aspects: first, CMBC clusters the nodes with the connection time between the mobile nodes (i.e., noncluster head nodes, non-CH) and the cluster head (CH) and establishes stable topological structures between the non-CHs and the CH; second, when emergencies occur, the centralization management control center sends the configuration files to replace the CH. Compared to the distributed network of MBC, WCRA and IMP-MECA protocol, the proposed method can be applied in scenarios with high-speed mobile nodes to improve the network performance in terms of transmission successful rate, average power consumption, and average control overhead.

Computational Intelligence based Clustering Algorithms for Wireless Sensor Networks: Trends and Possible Solutions

A wireless sensor network (WSN) is a state-of-the-art technology for radio communication. A WSN includes several sensors that are arbitrarily distributed in a particular region to detect and track physical characteristics that are hard for humans to observe, like temperature, humidity, and pressure. Because of the nature of WSNs, many issues may arise, including information routing, power consumption, clustering, and cluster head (CH) selection. Although there are still some difficulties in the WSN, owing to its versatility and robustness, it has gained considerable attention among scientists and technologists despite the shortcomings. Various protocols were designed to solve these problems. Low energy adaptive clustering hierarchy (LEACH) is one of the significant hierarchical protocols used to reduce energy consumption in WSNs. This article provides an extensive analysis of LEACH-variant clustering protocols for WSNs. Recent research on Machine Learning, Computational Intelligence, and WSNs has highlighted the optimized WSN clustering algorithms. However, the selection of a suitable paradigm for a clustering solution continues an issue owing to the diversity of WSN applications. In this paper, a comprehensive review of suggested optimized clustering alternatives has been conducted and a comparison of these optimized clustering methods has been suggested based on various performance parameters. The centralized clustering approaches based on the Swarm Intelligence paradigm are observed to be more suitable for the applications in which low energy is required, high information delivery rate, or elevated scalability than algorithms that are based on the other paradigms described.

Distributed clustering using multi-tier hierarchical overlay super-peer peer-to-peer network architecture for efficient customer segmentation

Customer segmentation divides customers into groups of consumers with similar patterns or characteristics relevant to marketing, especially with respect to target advertising. These patterns or characteristics could involve age, gender, interests, and spending habits. A customer segmentation model provides decision-makers with an effective allocation of marketing resources and maximization of cross-selling and up-selling opportunities. Clustering analysis is a key approach to understand business participants and obtain customers’ segments with similar demographics, behaviors, or trends. In e-business, online trade organizations store customer data in distributed data centers to limit the infrastructure or comply with the company norms. Incrementally, they need to update customer’s information to align the business offers with the contemporary trend. It is costly and exhaustive to collect the e-customer data at one location to exert centralized clustering. Therefore, distributed clustering is best suitable to categorize and segment customer data from inherently distributed resources. Currently, distributed architectures suffer from communication overhead or inaccurate global solutions. In this paper, a novel multi-layer hierarchical super-peer P2P (MT-SP2P) network architecture is proposed. The proposed MT-SP2P architecture provides a solution to enhance the speed of a distributed clustering problem while maintaining clustering quality. A novel distributed clustering algorithm is also proposed using the MT-SP2P architecture to improve the clustering speed without compromising the clustering quality. Customer segmentation is also seen as a managerial concept. Our distributed segmentation model and architecture help enterprises increase profits and improve customer service levels through effective and scalable customer segmentation. Computational results and managerial insights are discussed. We found from experimental results on different real customer data with various configurations and sizes. The proposed architecture and distributed clustering algorithm have improved the clustering speed by more than 90% compared to the centralized approaches. The findings show that the proposed model provided better insights and managerial implications concerning the chosen clustering techniques and distributed customer segments.

Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure

In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute the traditional distance or similarity measures with new metrics in the existing centralized clustering algorithms in order to tackle uncertainty in data. However, in order to perform uncertain data clustering, representation plays an imperative role. In this paper, a Monte-Carlo integration is adopted and modified to express uncertain data in a probabilistic form. Then three similarity measures are used to determine the closeness between two probability distributions including one novel measure. These similarity measures are derived from the notion of Kullback-Leibler divergence and Jeffreys divergence. Finally, density-based spatial clustering of applications with noise and k-medoids algorithms are modified and implemented on one synthetic database and three real-world uncertain databases. The obtained outcomes confirm that the proposed clustering technique defeats some of the existing algorithms.

A Reduced Network Traffic Method for IoT Data Clustering

Internet of Things (IoT) systems usually involve interconnected, low processing capacity, and low memory sensor nodes (devices) that collect data in several sorts of applications that interconnect people and things. In this scenario, mining tasks, such as clustering, have been commonly deployed to detect behavioral patterns from the collected data. The centralized clustering of IoT data demands high network traffic to transmit the data from the devices to a central node, where a clustering algorithm must be applied. This approach does not scale as the number of devices increases, and the amount of data grows. However, distributing the clustering process through the devices may not be a feasible approach as well, since the devices are usually simple and may not have the ability to execute complex procedures. This work proposes a centralized IoT data clustering method that demands reduced network traffic and low processing power in the devices. The proposed method uses a data grid to summarize the information at the devices before transmitting it to the central node, reducing network traffic. After the data transfer, the proposed method applies a clustering algorithm that was developed to process data in the summarized representation. Tests with seven datasets provided experimental evidence that the proposed method reduces network traffic and produces results comparable to the ones generated by DBSCAN and HDBSCAN, two robust centralized clustering algorithms.

Privacy-Preserving Distributed Clustering for Electrical Load Profiling

Electrical load profiling supports retailers in identifying consumer categories for customizing tariff design. However, each retailer only has access to the data of the customers it serves. Centralized joint clustering on retailers' union load dataset either enables the identification of more types of users that allows to design more customized retail plans, or informs whether each retailer already has a sufficiently broad customer base. However, the centralized clustering requires access to the confidential data of retailers. This may cause privacy issues among retailers, because retailers can not or do not want to share their confidential information with others. To tackle this issue, we propose a privacy-preserving distributed clustering framework by developing a privacy-preserving accelerated average consensus (PP-AAC) algorithm. Using the proposed framework, we modify several commonly used clustering methods, including k-means, fuzzy C-means, and Gaussian mixture model, to provide privacy-preserving distributed clustering methods. In this way, the clustering on retailers' union dataset can be achieved only by local calculations and information sharing between neighboring retailers without sacrificing privacy. The correctness, privacy-preserving property, time-saving feature, and robustness to random communication failures of the proposed methods are verified using a real-world Irish residential dataset.

Entropy Weighting Based Centralized Multi-View Fuzzy Clustering: A Case Study on Brain MR Image Segmentation

Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.

A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks.

Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the value of the network. The , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.

Swarm intelligence based centralized clustering: a novel solution

Recent evolutions of MEMS technology, digital electronics, and wireless communication technologies have made smart environments possible; especially through the apparition or incorporation of sensors. Despite being small-sized, the sensors have paved the way for data collection in the environments where they are applied; including luminosity, gas presence, water content, humidity, pressure, and temperature. This research aims to pragmatically optimize Wireless Sensor Network Localization and Network Coverage issues using nature-inspired algorithms. The specific objective is to establish an optimal nature-inspired algorithm and comparing it with other algorithms regarding the capacity to achieve manufacturing optimization in large WSNs, especially in relation to the parameters of high scalability, data delivery rate, and low-energy consumption. Also, the study seeks to determine the extent to which swarm intelligence (SI)-based centralized clustering solutions (optimal nature-inspired algorithms), compared to other approaches, might optimize the WSN features of localization and network coverage. To determine the solutions’ performance, the study involved three scenarios. In scenario 1, the network operational time and the stability of SI-based algorithms were investigated for large WSNs that had the minimum heterogeneity. Imperative to note is that the WSNs on focus had different numbers of nodes, which included 500, 300, and 100. In scenario 2, the motivation was to investigate the SI-based WSN protocols in relation to the parameters of packet delivery, energy conception, and network lifetime for large WSNs. In scenario 3, the performance of SI-based solutions over large WSNs was compared to that which had been reported previously for other algorithms; with the target parameters of comparison being attributes such as packet delivery, energy conception, and network lifetime. From the findings, this study established that SI-based centralized clustering solutions are not only more recent but also exhibit superior performance compared to other algorithms; with the parameters of the amount of data delivered to the BS, energy consumption and scalability on the focus.