Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after th...Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader <span style="font-family:Verdana;">may exit the system with prior knowledge of the exit. Our proposed algo</span><span style="font-family:Verdana;">rithm, t</span><span style="font-family:Verdana;">he dynamic leader selection algorithm, provides several benefits through</span><span style="font-family:Verdana;"> selection (not, election) of a set of future leaders which are then alerted prior to </span><span style="font-family:Verdana;">the failure of the current leadership and handed over the leadership. A </span><span style="font-family:Verdana;">specific </span><span style="font-family:Verdana;">illustration of this algorithm is provided with reference to a peer-to-peer</span><span style="font-family:Verdana;"> distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.</span>展开更多
In IoT networks,nodes communicate with each other for computational services,data processing,and resource sharing.Most of the time huge data is generated at the network edge due to extensive communication between IoT ...In IoT networks,nodes communicate with each other for computational services,data processing,and resource sharing.Most of the time huge data is generated at the network edge due to extensive communication between IoT devices.So,this tidal data is transferred to the cloud data center(CDC)for efficient processing and effective data storage.In CDC,leader nodes are responsible for higher performance,reliability,deadlock handling,reduced latency,and to provide cost-effective computational services to the users.However,the optimal leader selection is a computationally hard problem as several factors like memory,CPU MIPS,and bandwidth,etc.,are needed to be considered while selecting a leader amongst the set of available nodes.The existing approaches for leader selection are monolithic,as they identify the leader nodes without taking the optimal approach for leader resources.Therefore,for optimal leader node selection,a genetic algorithm(GA)based leader election(GLEA)approach is presented in this paper.The proposed GLEA uses the available resources to evaluate the candidate nodes during the leader election process.In the first phase of the algorithm,the cost of individual nodes,and overall cluster cost is computed on the bases of available resources.In the second phase,the best computational nodes are selected as the leader nodes by applying the genetic operations against a cost function by considering the available resources.The GLEA procedure is then compared against the Bees Life Algorithm(BLA).The experimental results show that the proposed scheme outperforms BLA in terms of execution time,SLA Violation,and their utilization with state-of-the-art schemes.展开更多
Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of ...Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.展开更多
As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big probl...As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.展开更多
无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够...无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够真实反映无线信号之间的干扰,提出一种在动态无线网络中基于SINR模型的常数近似因子的在线分布式链路调度算法(OLD_LS).在线的意思是指,在算法执行的过程中任意节点可以随时加入网络,也可以随时离开网络.节点任意加入网络或者从网络中离开体现了无线网络的动态变化的特性. OLD_LS算法把网络区域划分为多个正六边形,局部化SINR模型的全局干扰.设计动态网络下的领导者选举算法(LE),只要网络节点的动态变化速率小于1/ε, LE就可以在O(log n+log R)ε≤5(1-21-α/2)/6,α表示路径损耗指数, n是网络节点的规模, R是最长链路的长度.根据文献调研,所提算法是第1个用于动态无线网络的在线分布式链路调度算法.展开更多
文摘Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader <span style="font-family:Verdana;">may exit the system with prior knowledge of the exit. Our proposed algo</span><span style="font-family:Verdana;">rithm, t</span><span style="font-family:Verdana;">he dynamic leader selection algorithm, provides several benefits through</span><span style="font-family:Verdana;"> selection (not, election) of a set of future leaders which are then alerted prior to </span><span style="font-family:Verdana;">the failure of the current leadership and handed over the leadership. A </span><span style="font-family:Verdana;">specific </span><span style="font-family:Verdana;">illustration of this algorithm is provided with reference to a peer-to-peer</span><span style="font-family:Verdana;"> distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.</span>
基金supported by the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 3(Grant No:XMUMRF/2019-C3/IECE/0006).
文摘In IoT networks,nodes communicate with each other for computational services,data processing,and resource sharing.Most of the time huge data is generated at the network edge due to extensive communication between IoT devices.So,this tidal data is transferred to the cloud data center(CDC)for efficient processing and effective data storage.In CDC,leader nodes are responsible for higher performance,reliability,deadlock handling,reduced latency,and to provide cost-effective computational services to the users.However,the optimal leader selection is a computationally hard problem as several factors like memory,CPU MIPS,and bandwidth,etc.,are needed to be considered while selecting a leader amongst the set of available nodes.The existing approaches for leader selection are monolithic,as they identify the leader nodes without taking the optimal approach for leader resources.Therefore,for optimal leader node selection,a genetic algorithm(GA)based leader election(GLEA)approach is presented in this paper.The proposed GLEA uses the available resources to evaluate the candidate nodes during the leader election process.In the first phase of the algorithm,the cost of individual nodes,and overall cluster cost is computed on the bases of available resources.In the second phase,the best computational nodes are selected as the leader nodes by applying the genetic operations against a cost function by considering the available resources.The GLEA procedure is then compared against the Bees Life Algorithm(BLA).The experimental results show that the proposed scheme outperforms BLA in terms of execution time,SLA Violation,and their utilization with state-of-the-art schemes.
文摘Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.
文摘As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.
