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Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today's digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application. [ABSTRACT FROM AUTHOR]

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities. [ABSTRACT FROM AUTHOR]

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, "available" and "free." To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm's performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm's performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed "component-based throttled load balancing algorithm" is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. [ABSTRACT FROM AUTHOR]

Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively. [ABSTRACT FROM AUTHOR]

The microservice architecture has many advantages, such as technology heterogeneity, isolation, scalability, simple deployment, and convenient optimization. These advantages are important as we can use diversity and redundancy to improve the resilience of software system. It is necessary to study the method of improving the resilience of software system by diversity implementation and redundant deployment of software core components based on microservice framework. How to optimize the diversity deployment of microservices is a key problem to maximize system resilience and make full use of resources. To solve this problem, an efficient microservice diversity deployment mechanism is proposed in this paper. Firstly, we creatively defined a load balancing indicator and a diversity indicator. Based on this, a diversified microservices deployment model is established to maximize the resilience and the resource utilization of the system. Secondly, combined with load balancing, a microservice deployment algorithm based on load balance and diversity is proposed, which reduces the service's dependence on the underlying mirror by enriching diversity and avoids the overreliance of microservices on a single node through decentralized deployment, while taking into account load balancing. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms other algorithms. [ABSTRACT FROM AUTHOR]

With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node's processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The central network architecture takes the information content as the processing object and can better meet the user access needs. However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture. Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network. Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system. By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms. In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given. Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness. Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network. [ABSTRACT FROM AUTHOR]

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect. [ABSTRACT FROM AUTHOR]

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree. [ABSTRACT FROM AUTHOR]

Load-balancing optimization in software-defined networking (SDN) has been researched for a long time. Researchers have proposed many solutions to the load-balancing problem but have rarely considered the impact of transmission delay between controllers and switches under high-load network conditions. In this paper, we propose an adaptive load-balancing architecture based on link-state prediction (ALBLP) in SDN that can solve the influence of transmission delay between controllers and switches on network load balancing. ALBLP constructs the prediction model of the network link status, adopts the long-term and short-term memory neural network (LSTM) algorithm to predict the network link-state value, and then uses the predicted value as the Dijkstra weight to calculate the optimal path between network hosts. The proposed architecture can adaptively optimize network load balancing and avoid the empty window period, in which the switch flow table does not exist by actively issuing the flow table. Under the network architecture, we collect the data set of the network link-state by simulating the GÉANT network, and we verify the effectiveness of the proposed algorithm. The experiment results show that the ALBLP proposed in this paper can optimize load balancing in SDN and solve the problem of transmission delay between controllers and switches. It has a maximum load-balancing improvement of 23.7% and 11.7% in comparison with the traditional Open Shortest Path First (OSPF) algorithm and the reinforcement learning method based on Q-Learning. [ABSTRACT FROM AUTHOR]

Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, video and multimedia have become an integral part of visual communication design. Designers want higher interactivity, diversity, humanization, and plurality of attributes in the process of visual communication. This makes the process of visual communication have high requirements for the quality and real-time data transmission. To address the problem of transmitting HD video in a heterogeneous wireless network with multiple concurrent streams to improve the transmission rate and thus enhance the user experience, with the optimization goal of minimizing the system transmission delay and the delay difference between paths, the video sender and receiver are jointly considered, and the video transmission rate and the cache size at the receiver are adaptively adjusted to improve the user experience, and a cooperative wireless communication video transmission based on the control model for video transmission based on cooperative wireless communication is established, and video streams with self-similarity and long correlation are studied based on Pareto distribution and P / P / l queuing theory, based on which an adaptive streaming decision method for video streams in heterogeneous wireless networks is proposed. Simulation results show that the proposed multistream concurrent adaptive transmission control method for heterogeneous networks is superior in terms of delay and packet loss rate compared with the general load balancing streaming decision method, in terms of transmission efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation-intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle-assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved. [ABSTRACT FROM AUTHOR]

We focus on Partial Differential Equation (PDE)‐based Data Assimilation problems (DA) solved by means of variational approaches and Kalman filter algorithm. Recently, we presented a Domain Decomposition framework (we call it DD‐DA, for short) performing a decomposition of the whole physical domain along space and time directions, and joining the idea of Schwarz's methods and parallel in time approaches. For effective parallelization of DD‐DA algorithms, the computational load assigned to subdomains must be equally distributed. Usually computational cost is proportional to the amount of data entities assigned to partitions. Good quality partitioning also requires the volume of communication during calculation to be kept at its minimum. In order to deal with DD‐DA problems where the observations are nonuniformly distributed and general sparse, in the present work we employ a parallel load balancing algorithm based on adaptive and dynamic defining of boundaries of DD—which is aimed to balance workload according to data location. We call it DyDD. As the numerical model underlying DA problems arising from the so‐called discretize‐then‐optimize approach is the constrained least square model (CLS), we will use CLS as a reference state estimation problem and we validate DyDD on different scenarios. [ABSTRACT FROM AUTHOR]

With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become a hot issue of current research. This article is also in the context of the mobile Internet of Things, researching the solution to the service matching problem. In order to provide high-quality grid services, the grid system needs the support of quality of service (QoS). Based on the existing QoS matching strategy, a service matching strategy based on dynamic grid QoS is proposed. This paper combines semantic information, considers service quality QoS and user preferences, and improves the success rate of service matching through hierarchical matching, so as to provide users with high-quality services. At the same time, the classic Max-Min algorithm in grid task scheduling is analysed, and on this basis, the impact of QoS on task scheduling is fully considered, and combined with the concept of execution cost, a grid task based on QoS constraints (MOS) is proposed. Through simulation verification and analysis, the strategy in this paper not only achieved higher matching performance, but also improved the success rate of task execution, and the effect of system load balancing was also significantly improved. [ABSTRACT FROM AUTHOR]

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization. [ABSTRACT FROM AUTHOR]

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods. [ABSTRACT FROM AUTHOR]

This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality. [ABSTRACT FROM AUTHOR]

This article, titled "Research on Load Balancing and Caching Strategy for Central Network," has been retracted by Hindawi due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, and availability of data, as well as inappropriate citations and incoherent content. Hindawi cannot vouch for the reliability of the article's content and regrets that these issues were not identified before publication. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

The massive deployment of small cell Base Stations (SBSs) empowered with computing capabilities presents one of the most ingenious solutions adopted for 5G cellular networks towards meeting the foreseen data explosion and the ultralow latency demanded by mobile applications. This empowerment of SBSs with Multi-access Edge Computing (MEC) has emerged as a tentative solution to overcome the latency demands and bandwidth consumption required by mobile applications at the network edge. The MEC paradigm offers a limited amount of resources to support computation, thus mandating the use of intelligence mechanisms for resource allocation. The use of green energy for powering the network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted attention towards minimizing the carbon footprint and network operational costs. However, due to their high intermittency and unpredictability, the adoption of learning methods is a requisite. Towards intelligent edge system management, this paper proposes a Green-based Edge Network Management (GENM) algorithm, which is an online edge system management algorithm for enabling green-based load balancing in BSs and energy savings within the MEC server. The main goal is to minimize the overall energy consumption and guarantee the Quality of Service (QoS) within the network. To achieve this, the GENM algorithm performs dynamic management of BSs, autoscaling and reconfiguration of the computing resources, and on/off switching of the fast tunable laser drivers coupled with location-aware traffic scheduling in the MEC server. The obtained simulation results validate our analysis and demonstrate the superior performance of GENM compared to a benchmark algorithm. [ABSTRACT FROM AUTHOR]

Densely deploying small cells will be a solution to provide explosive data requirements in fifth-generation networks. Because users often cluster together in popular locations of an urban area, congested small cells in the popular sites are also gathered. Traditional load balancing schemes generally only consider neighboring cells when offloading users are not suitable for clusters of overloaded cells. This paper considers groups of overloaded cells in the load balancing problem. The objective is to maximize the quality-of-service satisfaction ratio. To solve the problem, we propose a multidepth offloading algorithm with the consideration of the radio resource allocation. The proposed multidepth offloading algorithm can be applied no matter that congested small cells are gathered or not. Compared with a previous offloading algorithm and a baseline, the simulation results show that our proposed algorithm can increase 16% QoS satisfaction ratio in a real user distribution and 13% QoS satisfaction ratio in a clustered user placement. [ABSTRACT FROM AUTHOR]

The interaction between cores and memory blocks, in multiprocessor chips and smart systems, has always been a concern as it affects network latency, memory capacity, and power consumption. A new 2.5-dimensional architecture has been introduced in which the communication between the processing elements and the memory blocks is provided through a layer called the interposer. If the core wants to connect to another, it uses the top layer, and if it wants to interact with the memory blocks, it uses the interposer layer. In a case that coherence traffic at the processing layer increases to the extent that congestion occurs, a part of this traffic may be transferred to the interposer network under a mechanism called load balancing. When coherence traffic is moved to the interposer layer, as an alternative way, this may interfere with memory traffic. This paper introduces a mechanism in which the aforementioned interference may be avoided by defining two different virtual channels and using multiple links which specifically determines which memory block is going to be accessed. Our method is based on the destination address to recognize which channel and link should be selected while using the interposer layer. The simulation results show that the proposed mechanism has improved by 32% and 14% latency compared to the traditional load-balancing and unbalanced mechanisms, respectively. [ABSTRACT FROM AUTHOR]

The Low-Efficiency Adaptive Clustering Hierarchical (LEACH) protocol, a hierarchical routing protocol, has the advantage of simple implementation and can effectively balance network loads. However, to date there has been a lack of consideration for its use in heterogeneous energy network environments. To solve this problem, the Energy-Coverage Ratio Clustering Protocol (E-CRCP) is proposed, which is based on reducing the energy consumption of the system and utilizing the regional coverage ratio. First, the energy model is designed. The optimal number of clusters is determined based on the principle of "minimum energy consumption", and the cluster head selection is based on the principle of "regional coverage maximization". In order to balance the network load as much as possible, in the next iteration of cluster head selection, the cluster head with the lowest residual energy and the highest energy consumption is replaced to prolong the network's life. Our simulated results demonstrate that the proposed method has some advantages in terms of longer network life, load balancing, and overall energy consumption in the environment of a heterogeneous energy wireless sensor network. [ABSTRACT FROM AUTHOR]

Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capability of GPUs, heterogeneous computing architecture produces new challenges for system software development and application design. Because of the significantly different architectures and programming models of CPUs and GPUs, conventional optimization techniques for CPUs may not work well in a heterogeneous multi-CPU and multi-GPU system. We present a heterogeneous parallel LU factorization algorithm for heterogeneous architectures. According to the different performances of the processors in the system, any given matrix is partitioned into different sizes of basic column blocks. Then, a static task allocation strategy is used to distribute the basic column blocks to corresponding processors uniformly. The idle time is minimized by optimized sizes and the number of basic column blocks. Right-looking ahead technology is also used in systems configured with one CPU core to one GPU to decrease the wait time. Experiments are conducted to test the performance of synchronization and load balancing, communication cost, and scalability of the heterogeneous parallel LU factorization in different systems and compare it with the related matrix algebra algorithm on a heterogeneous system configured with multiple GPUs and CPUs. [ABSTRACT FROM AUTHOR]

Recent research activities have shown that opportunistic routing can achieve considerable performance gains in Cognitive Radio Ad hoc Networks (CRAHNs). Most of these studies focused on designing appropriate metrics to select and prioritize the forwarding candidates. However, in multiple-flow networks, a small number of nodes may always be with the higher priority order for different flows. Thus, some nodes may easily become overloaded with too much traffic and be severely congested. To overcome this problem, we propose a load balancing opportunistic routing (LBOR) scheme to maximize the total throughput of the whole network. We first formulate the problem of maximizing the total throughput of the network as a linear programming problem. Then, we develop heuristic load balancing candidate forwarder sorting and selection algorithms. Simulation results and comparisons demonstrate that our proposed LBOR scheme outperforms existing opportunistic routing protocols with nonload balancing methods in CRAHNs. [ABSTRACT FROM AUTHOR]

Over the last few decades, Cluster-Based Wireless Sensor Networks (CBWSNs) have played a crucial role in handling various challenges (load balancing, routing, network lifetime, etc.) of large scale Wireless Sensor Networks (WSNs). However, the security becomes a big problem for CBWSNs, especially when nodes in the cluster selfishly behave, e.g., not forwarding other nodes’ data, to save their limited resources. This may make the cluster obsolete, even destroying the network. Thus, a way to guarantee the secure and consistent clusters is needed for proper working of CBWSNs. We showed that the selfishness attack, i.e., passive attack or insider attack, in CBWSNs can cause severe performance disaster, when particularly a cluster head node becomes selfish. In order to prevent this situation, this paper proposes a security framework that involves a novel clustering technique as well as a reputation system at nodes for controlling selfishness, making them cooperative and honest. The novelty of the clustering comes from the existence of inspector node (IN) to monitor the cluster head (CH) and its special working style. The experimental results showed that the proposed security framework can control the selfishness and improve the security of the clusters. [ABSTRACT FROM AUTHOR]

Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM. [ABSTRACT FROM AUTHOR]

Cascading failures in the command and control networks (C2 networks) could substantially affect the network invulnerability to some extent. In particular, without considering the characteristics of hierarchy structure, it is quite misleading to employ the existing cascading failure models and effectively analyze the invulnerability of C2 networks. Therefore, a novel cascading failure model for command and control networks with hierarchy structure is proposed in this paper. Firstly, a method of defining the node’s initial load in C2 networks based on hierarchy-degree is proposed. By applying the method, the impact of organizational positions and the degree of the node on its initial load could be highlighted. Secondly, a nonuniform adjustable load redistribution strategy (NALR strategy) is put forward in this paper. More specifically, adjusting the redistribution coefficient could allocate the load from failure nodes to the higher and the same level neighboring nodes according to different proportions. It could be demonstrated by simulation results that the robustness of C2 networks against cascading failures could be dramatically improved by adjusting the initial load adjustment coefficient, the tolerance parameter, and the load redistribution coefficient. And finally, comparisons with other relational models are provided to verify the rationality and effectiveness of the model proposed in this paper. Subsequently, the invulnerability of C2 networks could be enhanced. [ABSTRACT FROM AUTHOR]

The purpose of this research was to investigate the effect of geometric variables on the bolt load distributions of a cold-formed steel bolt connection. The study was conducted using an experimental test, finite element analysis, and an analytical method. The experimental study was performed using single-lap shear testing of a concentrically loaded bolt connection fabricated from G550 cold-formed steel. Finite element analysis with shell elements was used to model the cold-formed steel plate while solid elements were used to model the bolt fastener for the purpose of studying the structural behavior of the bolt connections. Material nonlinearities, contact problems, and a geometric nonlinearity procedure were used to predict the failure behavior of the bolt connections. The analytical method was generated using the spring model. The bolt-plate interaction stiffness was newly proposed which was verified by the experiment and finite element model. It was applied to examine the effect of geometric variables on the single-column multibolt connection. The effects were studied of varying bolt diameter, plate thickness, and the plate thickness ratio (t2/t1) on the bolt load distribution. The results of the parametric study showed that the t2/t1 ratio controlled the efficiency of the bolt load distribution more than the other parameters studied. [ABSTRACT FROM AUTHOR]

In order to implement an optimized solution for cell range expansion (CRE) and enhanced intercell interference coordination (eICIC) schemes in long-term evolution-advanced (LTE-A) heterogeneous cellular networks (HCNs) and to realize good load-balancing performance in existing LTE-A systems, a practical tessellation-based algorithm is proposed. In this algorithm, a globalized cell-specific bias optimization and a localized almost blank subframe (ABS) ratio update are proposed. The proposed scheme does not require major changes to existing protocols. Thus, it can be implemented in existing LTE-A systems with any legacy user equipment (UE) with only a partial update to the BSs and core networks. From simulation results, it is shown that the tessellation formed by the proposed approach is quite consistent with the optimal one for various realistic scenarios. Thus, the proposed scheme can provide a much better load-balancing capability compared with the conventional common bias scheme. Owing to the improved load-balancing capability, the user rate distribution of the proposed scheme is much better than that obtained from the conventional scheme and is even indistinguishable from that of the ideal joint user association scheme. [ABSTRACT FROM AUTHOR]

A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large-scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial-temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel-based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi-objective algorithm called time and space optimized NSGA-II (TS-NSGA-II). Experiment results have shown that compared with the original load-balancing scheme, our approach can save approximate 47-55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6 % improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT By offering indisputable advantages over traditional grid including remote readings and load balancing, smart grid is regarded as the modernization of the electricity delivery system. In this paper, we propose a privacy-preserving and secure multi-dimensional aggregation scheme for smart grid communications in digital communities. By integrating privacy homomorphism encryption with aggregation signature scheme, data authentication and integrity protection are performed and proved without disclosing any fine-grained user consumption data. The construction of member list allows the fault tolerance of our scheme against accidental errors. In addition, the batch verification technique is adopted to reduce the computation cost of the operation center, and each user's computation cost is independent of the number of collected data types. Security analysis and performance evaluation demonstrate that the proposed scheme can resist various security threats and preserve identity privacy and has significantly less communication overhead and computation cost than other existing approaches. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the pervasive use of smart mobile devices and increasing wireless networking technologies, collaborations among mobile users are becoming deeper and ubiquitous. Appropriate task collaborations among mobile users could effectively improve the network processing ability with so called 'mobile cloud' or 'cloudlet'. However, task allocations confront with the security issues. The possible collusion or re-collaborations among the mobile users would possibly merge the allocated tasks of the specific users. Moreover, considering the delivery reliability and task execution efficiency, replications are applied for enhancement, which would also lead to more sever security threat for users. We investigate how to secure the security when task collaborations are allowed for mobile users. Our security scheme is built upon the load balancing scheme, and our intuitive solution is, if the tasks could be effectively balanced among users, the security issues could be guaranteed, because averaging the task assignment could effectively raise the threshold for collusion among potential malicious users. In this work, we propose 'SAFE-CROWD': a secure task offloading and reassignment scheme among mobile users. The basic idea is simple, we leverage the 'ball and bin' theory for task assignment, where d mobile users in contact range are investigated, and we select the least loaded ones among them. It has been proved that such simple cases can effectively reduce the largest queueing length from [ABSTRACT FROM AUTHOR]

Many algorithms can uniformly distribute data to storage nodes in a storage system. However, it cannot avoid load imbalance because data has different popularity. To resolve this issue, we propose a novel dynamic replication scheme, namely, Active Replica Management (ARM). ARM actively establishes optimal number of copies for hotspot data according to data access behaviors and then efficiently distributes copies to other storage nodes based on current amount of copies related to hotspot data. To improve storage utilization, ARM automatically and gradually dereplicates the useless copies of hotspot data when they become nonhotspot data. ARM resolves load imbalance by allocating dynamic copies to adequate storage nodes, and hence it can prevent partial storage nodes from overburdening. Simulation results demonstrate that ARM is an efficient scheme with excellent performance on load balancing, significantly closer to Optimal Load Balancing (OLB). In addition, ARM’s performance outperforms both Static Load Balancing (SLB) and No Replica schemes. [ABSTRACT FROM AUTHOR]

High-speed train (HST) is revitalizing the train as a preferred mid-range transportation system. The provision of broadband Internet connectivity onboard trains is one of the key challenges in the competition among train operators. Unprecedented spectral efficiency and data rates (up to 100Mbps in high mobility) of the Universal Mobile Telecommunications System long-term evolution (LTE) are expected to offer the solution for high-speed Internet access onboard in HSTs. Massive wireless access and frequent handovers (HOs) of a large number of users might potentially cause service interruptions, and this, in turn, degrades intolerably the quality of experience (QoE) of users' onboard Internet access. In this paper, we propose a solution based on distributed antenna system that combines directional and omni-directional antennas as train-to-ground radio-access terminals (T-RATs) and LTE femtocells in each carriage. Directional antennas are deployed at both ends of the HST to provide multi-cell access by diversifying the HOs over multiple LTE cells. This mechanism virtually elongates the train size by connecting the front and rear carriages' T-RATs to the faraway eNodeBs and augmenting the number of cells the HST can be simultaneously connected to. An ad hoc distributed load-balancing mechanism that consists in offloading backlogged packets to the on-service T-RATs is mandatorily paired with multi-cell access scheme to tie up with the request of seamless onboard Internet service at high QoE level. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the next-generation technology, Internet of Things (IoT), billions of smart objects will communicate with one another to make human lives more convenient. IoT is based on wireless sensor network (WSN), and Zigbee is one of the most popular WSN protocols. A mature IoT environment involves heavy WSN data transmission causing bottleneck problems. However, Zigbee’s AODV routing stack does not have load balance mechanism to handle bursty traffic. Therefore, we develop Multipath Load Balancing (MLB) Routing to substitute Zigbee’s AODV routing. Our proposed MLB consists of two main designs: LAYER_DESIGN and LOAD_BALANCE. LAYER_DESIGN assigns nodes into different layers based on node distance to IoT gateway. Nodes can have multiple next-hops delivering IoT data. All neighboring layer nodes exchange flow information containing current load, used by LOAD_BALANCE to estimate future load of next-hops. With MLB, nodes can choose the neighbors with the least load as the next-hops and thus can achieve load balance and avoid bottlenecks. Compared with Zigbee’s AODV and multipath version AODV (AOMDV), experiment results demonstrate that MLB achieves better load balance, lower packet loss rate, and better routing connectivity ratio in both grid and random uniform topologies. MLB provides a more convincing routing solution for IoT applications. [ABSTRACT FROM AUTHOR]

Simulating binary black hole (BBH) systems are a computationally intensive problem and it can lead to great scientific discovery. How to explore more parallelism to take advantage of the large number of computing resources of modern supercomputers is the key to achieve high performance for BBH simulations. In this paper, we propose a scalable MPM (Mesh based Parallel Method) which can explore both the inter- and intramesh level parallelism to improve the performance of BBH simulation. At the same time, we also leverage GPU to accelerate the performance. Different kinds of performance tests are conducted on Blue Waters. Compared with the existing method, our MPM can improve the performance from 5x speedup (compared with the normalized speed of 32 MPI processes) to 8x speedup. For the GPU accelerated version, our MPM can improve the performance from 12x speedup to 28x speedup. Experimental results also show that when only enough CPU computing resource or limited GPU computing resource is available, our MPM can employ two special scheduling mechanisms to achieve better performance. Furthermore, our scalable GPU acceleration MPM can achieve almost ideal weak scaling up to 2048 GPU computing nodes which enables our software to handle even larger BBH simulations efficiently. [ABSTRACT FROM AUTHOR]

In this paper, a game theoretic relay load balancing and power allocation scheme is proposed for downlink transmission in a decode-and-forward orthogonal frequency division multiple access-based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game-based scheme can considerably improve the average sum-spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Location privacy protection in vehicular ad hoc networks considers preserving two types of information: the locations and identifications of users. However, existing solutions, which either replace identifications by pseudonyms or hide locations in areas, cannot be directly applied to geographic routing protocols because they degrade network performance. To address this issue, we proposed a location-preserved contention (LPC) based routing protocol, in which greedy forwarding is achieved using dummy distance to the destination information instead of users' true locations. Unlike the contention-based forwarding protocol, the number of duplicated responses in LPC can be reduced by adjusting the parameter α, which is a timer scaling factor. To quantify the efficiency of location privacy protection, an entropy-based analytical method is proposed. LPC is compared with existing routing and location privacy protection protocols in simulations. Results show that LPC provides 11.7% better network performance and a higher level of location privacy protection than the second best protocol. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient. [ABSTRACT FROM AUTHOR]

Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM’s multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

The recent advent of novel multi- and many-core architectures forces application programmers to deal with hardware-specific implementation details and to be familiar with software optimisation techniques to benefit from new high-performance computing machines. Extra care must be taken for communication-intensive algorithms, which may be a bottleneck for forthcoming era of exascale computing. This paper aims to present a high-level stencil framework implemented for the EULerian or LAGrangian model (EULAG) that efficiently utilises multi- and many-cores architectures. Only an efficient usage of both many-core processors (CPUs) and graphics processing units (GPUs) with the flexible data decomposition method can lead to the maximum performance that scales the communication-intensive Generalized Conjugate Residual (GCR) elliptic solver with preconditioner. [ABSTRACT FROM AUTHOR]

A utility-based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game-theoretic heuristic load-balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy-efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load-balanced trees, resulting in over 30 % longer network lifetime compared with the cumulative algorithm proposed in the literature. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Transmission links in wireless sensor networks (WSNs) are prone to failure because of breakdown, movement, and energy depletion of nodes. Aiming at node-intensive environment, this paper proposes a highly reliable forwarding model based on virtual nodes (HRVN) in tree-based topology networks. A virtual node (VN) consists of several neighboring physical sensor nodes, which have common parent node and children nodes. Normally, the physical sensor nodes in the same VN forward packets in a load-balancing scheme. When a sensor node in a VN is in failure, the others would take over its forwarding work immediately. Moreover, the internal changes of a VN are transparent to its parent and children nodes. The experiments prove the model can provide load-balance and fast recovery from failure with low cost. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is currently seen as one of the most promising future network technologies, which can realize the separation between control and data planes. Furthermore, the increasing complexity in future wireless networks (i.e., 5G, wireless sensor networks) renders the control and coordination of networks a challenging task. Future wireless networks need good separation of control and data planes and call for SDN method to handle the explosive increase of mobile data traffic. Relying on a single controller in future wireless networks imposes a potential scalability problem. To tackle this problem, the thought of using multiple controllers to manage the large wide-area wireless network has been proposed, where the load balance problem of multicontroller needs to be resolved. In this paper, we propose a multicontroller load balancing approach called HybridFlow in software-defined wireless networks, which adopts the method of distribution and centralization and designs a double threshold approach to evenly allocate the load. Simulation results reveal that the proposed approach can significantly relieve the working load on the super controller and reduces the load jitter of multicontroller load in a single cluster compared with the BalanceFlow method. [ABSTRACT FROM AUTHOR]

Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O(n2), where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more and more compounds have been synthesized and extracted now, even more than tens of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (seen as a multiple compound comparison problem, abbreviated to MCC). The intrinsic time complexity of MCC problem is O(k2n2) with k compounds of maximal length n. In this paper, we propose a GPU-based algorithm for MCC problem, called CUDA-MCC, on single- and multi-GPUs. Four LINGO-based load-balancing strategies are considered in CUDA-MCC in order to accelerate the computation speed among thread blocks on GPUs. CUDA-MCC was implemented by C+OpenMP+CUDA. CUDA-MCC achieved 45 times and 391 times faster than its CPU version on a single NVIDIA Tesla K20m GPU card and a dual-NVIDIA Tesla K20m GPU card, respectively, under the experimental results. [ABSTRACT FROM AUTHOR]

Regarding the energy shortage problem of wireless sensor networks (WSNs), various schemes and protocols are proposed to prolong the network lifetime. Data communication is undoubtedly the most important determinant for this energy scarce network type. In this paper, we propose a sophisticated architecture comprising data reduction, load balance, and topology control. Data reduction is ensured by the parameter spatial correlation proximity range (SCPR) that can be adjusted statically at the setup phase or dynamically revised depending on the necessities in the network. Four-layer virtual architecture is applied for implementing topology control. Furthermore, network area is partitioned into fixed-size hexagonal clusters. Depending on the regions in which the clusters take place, cluster heads (CHs) are elected from the respective subregions of the clusters. Load balance is achieved by considering residual energies and distances to the sink during both CH election and data transmission stages. Aggregated data in each cluster is transmitted towards the sink by using a load balancing single-hop intercluster routing protocol instead of direct transmission as offered in LEACH. Simulation results demonstrate that the proposed architecture ECMTADR shows almost 50% better performance in terms of energy conservation and network lifetime when it is compared to LEACH and HEED. [ABSTRACT FROM AUTHOR]