Idle Nodes Research Articles

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

SummaryWireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively.

Synergies between resource sustainability and energy performance of cloud servers: The role of virtual machine repacking approach

Dynamic virtual machine (VM) consolidation to combine the workload of active devices and switch idle nodes into low-power states while preserving the quality of service (QoS) guarantee is the primary proposed solution’s goal to maintain dependable services between providers and customers. We propose a multi-step resource provision algorithm to address this issue efficiently. The forwarding step unites the iterative search for critical paths and the priority selection for task layers to reduce the makespan and reach an optimal critical task placement. The backward step trains processing servers against different loads using eclectic utilization levels to guarantee those servers function at their most efficient utilization values. Furthermore, this step optimizes energy consumption and resource diffusion by reallocating the existent VMs and mitigating an attempted overload of machines. Different evaluation metrics are assessed with respect to the number of tasks and VMs. The results show that the proposed paradigm optimizes energy consumption and resource utilization efficiently, lowering the makespan, overhead time, and network utilization and maximizing the throughput productivity and QoS satisfaction.

Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes

A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.

Edge Computing-Based VANETs’ Anonymous Message Authentication

Vehicular Ad-hoc Networks (VANETs) have high requirements for real-time data processing and security of message authentication. In order to solve the computing power asymmetry between vehicles and road side units (RSUs) in VANETs under high-density traffic, accelerate the processing speed of message authentication, and solve the problems of high computational overhead and long message authentication time caused by the use of bilinear pairing encryption technology in similar message-batch-authentication schemes, we propose introducing the concept of edge computing (EC) into VANETs and using idle nodes’ resources to assist the RSU in quickly authenticating messages to achieve computing power load balancing under multiple traffic flows. We propose introducing the idea of edge computing (EC) into VANETs and using idle nodes’ resources to assist RSUs in quickly authenticating messages. This scheme performs two identity-based message authentications based on the identity signature constructed by elliptic curve cryptography (ECC). One of them is the batch authentication of the vehicle sending messages by the RSU-authenticated vehicles with free resources, as temporary edge computing nodes (TENs), and the other is the authentication of the temporary TEN messages by the fixed-edge-node RSUs. The resources of the TEN are used to reduce the computational burden of RSUs and message authentication time, thereby improving the efficiency of system authentication of messages. We performed a security analysis of the scheme to prove its security properties and compared it with other schemes in terms of performance. The experimental results show that our scheme has a transmission overhead of 2400 bytes when there are four TENs, and the number of verification message requests reaches 20, which outperforms other methods. The gap will be more evident as the numbers of TEN and message verification requests increase.

Bandwidth-Aware Scheduling Repair Techniques in Erasure-Coded Clusters: Design and Analysis

Erasure codes offer a storage-efficient redundancy mechanism for maintaining data availability guarantees in storage clusters, yet also incur high network traffic consumption and recovery time in failure repair. Extensive research has been carried out to reduce the recovery time. However, previous works either target specific erasure code constructions which are not commonly used in today’s distributed storage clusters or neglect the heterogeneous bandwidth property in real network environments. Since erasure-coded clusters are typically composed of multi-node with heterogeneous bandwidth and accessed in parallel, the whole recovery time is mainly restricted by the low-bandwidth links. In this article, we propose SMFRepair, a single-node multi-level forwarding repair technique that is designed to improve the performance in heterogeneous networks based on Reed-Solomon codes for general fault tolerance. SMFRepair carefully selects the helper nodes and uses idle nodes to bypass low-bandwidth links. Idle nodes have sufficient and unused network bandwidth. It also pipelines the repair links that are optimized by idle nodes. Furthermore, a multi-node scheduling repair technique, called MSRepair, is proposed. MSRepair carefully schedules the multi-node repair link to saturate the most unoccupied bandwidth and transfers data from as large-bandwidth links as possible, with the primary objective of minimizing the recovery time. Large-scale simulation and Amazon EC2 real experiments show that compared to state-of-the-art repair techniques, SMFRepair can accelerate the single-node recovery by up to 47.69%, and MSRepair can reduce the multi-node recovery time by 33.78% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 67.53%.

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

The battery power limits the energy consumption of wireless sensor networks (WSN). As a result, its network performance suffered significantly. Therefore, this paper proposes an opportunistic energy-efficient routing protocol (OEERP) algorithm for reducing network energy consumption. It provides accurate target location detection, energy efficiency, and network lifespan extension. It is intended to schedule idle nodes into a sleep state, thereby optimising network energy consumption. Sleep is dynamically adjusted based on the network’s residual energy (RE) and flow rate (FR). It saves energy for a longer period. The sleep nodes are triggered to wake up after a certain time interval. The simulation results show that the proposed OEERP algorithm outperforms existing state-of-the-art algorithms in terms of accuracy, energy efficiency, and network lifetime extension.

Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum

User-facing services are now evolving towards the microservice architecture where a service is built by connecting multiple microservice stages. Since the entire service is heavy, the microservice architecture shows the opportunity to only offload some microservice stages to the edge devices that are close to the end users. However, emerging techniques often result in the violation of Quality-of-Service (QoS) of microservice-based services in cloud-edge continuum, as they do not consider the communication overhead or the resource contention between microservices and external co-located tasks. We propose Nautilus, a runtime system that effectively deploys microservice-based user-facing services in cloud-edge continuum. Nautilus ensures the QoS of microservice-based user-facing services while minimizing the required computational resources, which is comprised of a communication-aware microservice mapper, a contention-aware resource manager and an IO-sensitive and load-aware microservice migration scheduler. The mapper divides the microservice graph into multiple partitions based on the communication overhead and maps the partitions to appropriate nodes. On each node, the resource manager determines the optimal resource allocation for its microservices based on reinforcement learning that may capture the complex contention behaviors. Once the microservices are suffered from external IO pressure, the IO-sensitive microservice scheduler migrates the critical one to idle nodes. Furthermore, when the load of microservices changes dynamically, the load-aware microservice scheduler migrates microservices from busy nodes to idle ones to ensure the QoS goal of the entire service. Our experimental results show that Nautilus can guarantee the required QoS target under external shared resources contention while the state-of-the-art suffers from QoS violations. Meanwhile, Nautilus reduces the computational resource usage by 23.9% and the network bandwidth usage by 53.4%, while achieving the required 99%-ile latency.

(Retracted) Image segmentation and visualization allocation method of engineering training resources in biology training

Existing processes for visual allocation of engineering training resources utilizing computers are inefficient and extend the time needed to complete learning tasks. A new process visual allocation method in engineering training resources in a biology teaching process is proposed. The biological teaching engineering training resource perception module is set up to send the resource data to the controller through the module. By quantifying the computing power and resource consumption of each node, the nodes that currently are bottlenecks as well as nodes with potentially more computing power yet highly likely to stand idle (high-potential idle nodes) are found. Then, the optimal data allocation proportion is found through the idea of greedy search, and the amount of data actually processed by parallel instances on each node is adjusted to match its computing power. According to the dynamic partition strategy of engineering training resources, a process visual allocation model is established by using ant colony scheduling algorithm. Taking the task volume of 500 MB as an example, in the homogeneous cluster environment, the completion time of this method is 89.7 s, which is 28.6 and 30.9 s shorter than the methods based on cloud computing and mobile edge computing; in heterogeneous cluster environment, the completion time of this method is 99.4 s, which is 41.4 and 43.1 s shorter than the methods based on cloud computing and mobile edge computing. Therefore, this method reduces the task completion time and improves the efficiency of task scheduling.

A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks

Emergency event monitoring is an important application of wireless sensor networks (WSNs). In the traditional cloud-assisted WSNs, the monitored data needs to be sent back to the cloud for processing. The round-trip of huge and burst data causes long delay and high energy consumption. With the increase of the computing capability of sensor nodes, local processing of events can be accomplished through cooperative computing between sensor nodes. However, if a node continues to undertake cooperative computing tasks, the energy consumption of nodes will be unbalanced and the network lifetime will be shortened. Sleep scheduling is an effective method to achieve energy balance, which can activate idle nodes alternately to participate in cooperative computing. In this paper, in order to make WSN more efficient to complete the local processing of events, we build a new system model based on node cooperative computing, and propose a multi-node Q learning-based cooperative computing node selection algorithm to obtain the sleep scheduling strategy. Simulation results show that compared with the classical algorithms, the proposed algorithm can complete more event processing, improve the reliability, and prolong the lifetime of WSNs.

Maintenance analysis of transportation networks by the traffic transfer principle considering node idle capacity

Traffic congestion is a universal challenge that affects urban transportation networks, which inevitably age and deteriorate. Maintenance is an essential method for alleviating road congestion. Most of the previous studies concentrate on node load and capacity analysis. The capacity of an idle node is also an important element that affects traffic congestion, such as road damage or traffic accident at the crossroads. To explore the effect of the capacity of an idle node on road congestion, this paper introduces a traffic transfer principle to improve road maintenance efficiency. The nodes of a traffic network can be ranked based on their failure severity. The failure paths of a traffic network can be identified through the internal connections between nodes. Using the transfer time as the weight of each edge and the service time as the weight of each node, this paper proposes a maintenance model to find the shortest repair path for minimizing the maintenance time. To evaluate the proposed model, four different types of road networks are adopted by comparing the maintenance time. The experimental results show the proposed model outperforms previous models.

Cost Efficient GPU Cluster Management for Training and Inference of Deep Learning

Expanding the scale of GPU-based deep learning (DL) clusters would bring not only accelerated AI services but also significant energy consumption costs. In this paper, we propose a cost efficient deep learning job allocation (CE-DLA) approach minimizing the energy consumption cost for the DL cluster operation while guaranteeing the performance requirements of user requests. To do this, we first categorize the DL jobs into two classes: training jobs and inference jobs. Through the architecture-agnostic modeling, our CE-DLA approach is able to conduct the delicate mapping of heterogeneous DL jobs to GPU computing nodes. Second, we design the electricity price-aware DL job allocation so as to minimize the energy consumption cost of the cluster. We show that our approach efficiently avoids the peak-rate time slots of the GPU computing nodes by using the sophisticated mixed-integer nonlinear problem (MINLP) formulation. We additionally integrate the dynamic right-sizing (DRS) method with our CE-DLA approach, so as to minimize the energy consumption of idle nodes having no running job. In order to investigate the realistic behavior of our approach, we measure the actual output from the NVIDIA-based GPU devices with well-known deep neural network (DNN) models. Given the real trace data of the electricity price, we show that the CE-DLA approach outperforms the competitors in views of both the energy consumption cost and the performance for DL job processing.

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

End-users and service providers have recently favored lower latency services. Edge computing has improved user quality of service (QoS) guarantees through the reduced geographical distance, decreased use of the network backbone, and flexible placement of the container hosting edge devices. The under-utilized isolated edge computing idling nodes are significant for service providers, especially for Internet of Things (IoT) applications. However, the nodes&#x2019; minimal maintenance remains a hindrance due to related increased failures. Orchestrating over the edge alongside core environments allows tolerant services and more demanding ones to coexist without impacting the user experience. Therefore, the orchestrator&#x2019;s second priority is achieving and maintaining the QoS through optimal recovery method selection by either migrating the live containers or re-instantiating them. This paper proposes an Optimal Container Migration/Re-Instantiation (OC-MRI) model to optimize the orchestration methods focusing on downtime, container dependencies, and latency requirements. Next, we introduce a real-time heuristic-based solution, Edge Computing-enabled Container Migration/Re-Instantiation (EC2-MRI). Both models are bench-marked alongside staple greedy approaches. Simulation results showcase the lowest latencies and downtime with the OC-MRI model. Furthermore, the EC2-MRI model shows comparable results to the optimal model with minimal lag.

Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer

In this work, we jointly investigate the issues of node scheduling and transceiver design in a sensor network with multiple clusters, which is endowed with simultaneous wireless information and power transfer. In each cluster of the observed network, S out of N nodes are picked, each of which is capable of performing information transmission (IT) via uplink communications. As for the remaining idle nodes, they can harvest energy from radio-frequency signals around their ambient wireless environments. Aiming to boost the intra-cluster performance, we advocate an interference alignment enabled opportunistic communication (IAOC) scheme. This scheme can yield better tradeoffs between IT and wireless power transfer (WPT). With the aid of IAOC scheme, the signal projected onto the direction of the receive combining vector is adopted as the accurate measurement of effective signal strength, and then the high-efficiency scheduling metric for each node can be accordingly obtained. Additionally, an algorithm, based on alternative optimization and dedicated for transceiver design, is also put forward, which is able to promote the achievable sum rate performance as well as the total harvested power. Our simulation results verify the effectiveness of the designed IAOC scheme in terms of improving the performance of IT and WPT in multi-cluster scenarios.

Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network

Wireless Multimedia Sensor Networks (WMSNs) based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in detecting intrusions in selected monitored areas. The IEEE 802.11-based WMSNs offer high bit rate video transmissions but are challenged by energy inefficiency issues and concerns. To resolve the energy inefficiency challenges, the salient research studies proposed a hybrid architecture. This newly evolved architecture is based on the integration of IEEE 802.11-based mesh WMSNs along with the LoRa network to form an autonomous and high bitrate, energy-efficient video surveillance system. This paper proposes an energy-aware and Quality of Service (QoS) routing mechanism for mesh-connected visual sensor nodes in a hybrid Internet of Things (IoT) network. The routing algorithm allows routing a set of video streams with guaranteed bandwidth and limited delay using as few visual sensor nodes as possible in the network. The remaining idle visual sensor nodes can be turned off completely, and thus it can significantly minimize the overall energy consumption of the network. The proposed algorithm is numerically simulated, and the results show that the proposed approach can help in saving a significant amount of energy consumption while guaranteeing bandwidth and limited delay.

Performance Improvement in Cloud Computing Environment by Load Balancing-A Comprehensive Review

With the advent of cloud computing, the affinity between business and technology had increased manifold, allowing users to access IT resources at their convenience through the pay-per-use scheme. With such huge demand surging day to day, the cloud environment must cater to the user requirements flawlessly and also should be rewarding to the providers of cloud service. To maintain its high level of efficiency, there are several challenges that the cloud environment should tackle. One amongst those challenges is the balancing of load. It is one of the primary features of cloud computing that focuses on avoiding the overloading of nodes where there may be idle nodes or nodes with lesser load present at the same juncture. By keeping an effective check on the load several the Quality of Service (QoS) parameters including response time, throughput, resource utilization, energy consumption, cost etc., can be improved, adding to better performance of the entire cloud environment. Even distribution of load among datacenters will contribute to optimal energy consumption and keeps a check on carbon emissions. In this paper we have presented a methodical review on literature pertaining to load balancing strategies that had been proposed in the cloud environment. We had made in-depth analyses of available load balancing techniques and had come up with their advantages, limitations along with the challenges to be addressed by researchers for developing efficient load balancing strategies in the near future. We had also suggested prospective insights about the aspects in load balancing that could be applied in the cloud environment.

BER Performance Analysis of Energy Harvesting Underlay Cooperative Cognitive Radio Network With Randomly Located Primary Users and Secondary Relays

In this work, we investigate the error probability of an energy harvesting co-operative cognitive radio network (CRN) in the presence of Poisson field of primary users (PU). We consider the scenario where, after harvesting energy from primary transmitters (PTs), a secondary transmitter (ST) would transmit its symbol towards secondary destination (SD) through a suitable secondary relay from a group of randomly scattered idle nodes within a circular region. Several relay selection criteria have been considered for relay node selection. Performance of a secondary transceiver has been analysed in terms of probability of error. Relationships among the bit error ratio (BER) performance of an ST and several other network entities such as density of PUs, transmit power of PTs and required transmit power of ST have also been investigated through detailed analysis. Numerical results are provided to assess the precision of derived analytical expressions.

Deploying SDN Control in Internet of UAVs: Q-Learning-Based Edge Scheduling

Nowadays, wilderness monitoring provides massive data output for supporting agricultural production, environmental protection, and disaster monitoring. However, smart upgrading alone for these wireless nodes cannot meet the softwarized network needs today, relating to the explosion of multi-dimensional data and multi-species equipment. In this article, we conduct a comprehensive solution for the UAV based data collection strategy in an “air-to-ground” intelligent softwarized collection system. The innovation in this article is that after using the IoT nodes to complete the data collection process through the proposed bandwidth-weighted traffic pushing optimization (BWPTO) algorithm, the system infers the future changes according to the current network state using a deep Q-learning (DQL) network. Then, by developing the proposed AIIPO (Air-to-Ground Intelligent Information Pushing Optimization) algorithm, the entire network can “forward-looking” the uploaded information to potentially idle nodes in the future, thus achieve the optimized system performance. Through the final mathematical experiments, we prove the optimality of our proposed routing algorithm and forwarding strategy, which are more applicable in the dynamic “air-to-ground” distributed data collection system than other benchmark solutions.

Cooperation-Based Adaptive and Reliable MAC Design for Multichannel Directional Wireless IoT Networks

Internet of Things (IoT) is the ultimate enabler of modern civilization. Cooperative communication in multichannel directional wireless networks is one of the cutting-edge IoT research themes. Typically, cooperation in wireless networks is the opportunistic relaying of data packets for neighboring nodes by idle nodes. Current IoT-based researches related to multichannel with directional antenna introduce the control channel cooperation. But, the iterative phenomenon of negotiation in control channel results in higher communication delay. Hence, to reduce the data transmission delay, cooperation in data relaying need to be used along with control channel cooperation. In this paper, we propose a Cooperation-based Adaptive and Reliable MAC (CAR MAC) Design for Multichannel Directional Wireless IoT Networks that combines both of these cooperation and multichannel directional concepts of cooperation. Multichannel directional hidden terminal problems and deafness problems in medium access are solved using both concepts of cooperation jointly. Besides, multidirectional data packet relaying in the same data channel enables parallel transmission that increases the bandwidth utilization. Moreover, the proposed protocol uses a smart GPS (Global Positioning System) based neighbor discovery. Therefore, the directional position and distance among the IoT-enabled wireless nodes are smartly determined to make the control channel cooperation more informative. The results of extensive simulations reveal that CAR MAC achieves significant improvement in network performances.

Relay Selection Based on Price Incentive For Device-to-Device Relay Cooperative Communications

Relay selection is the key to the Device-to-Device (D2D) cooperative communication. Since idle users often consider their own privacy, security and other self-contained issues such as the energy consumption of their mobile devices, they’re not willing to participate in cooperative communications as relay nodes. In order to encourage idle nodes to participate, an effective reward mechanism is essential. This paper proposes a relay selection algorithm based on price incentive to encourage self-interested idle nodes to participate in relay cooperative communications. Through adopting the Nash negotiation model of gaming theory and introducing utility function, the utility gain of each node is presented and become the standard to strike the bargains between the source node and relay nodes. After that, the appropriate compensation price are obtained, with which each D2D user can gain more benefits while the fairness among users can also be ensured, then the idle user’s intention to cooperate as a relay user in the D2D communication can be enhanced and the terms whether or not to perform relay cooperative communications can be derived. Simulations show that with the appropriate compensation price and the certain terms, the D2D relay selection algorithm based on price incentives is feasible.

Throughput improvement by mode selection in hybrid duplex wireless networks

Hybrid duplex wireless networks, use half duplex (HD) as well as full duplex (FD) modes to utilize the advantages of both technologies. This paper tries to determine the proportion of the network nodes that should be in HD or FD modes in such networks, to maximize the overall throughput of all FD and HD nodes. Here, by assuming imperfect self-interference cancellation (SIC) and using ALOHA protocol, the local optimum densities of FD, HD and idle nodes are obtained in a given time slot, using Karush–Kuhn–Tucker (KKT) conditions as well as stochastic geometry tool. We also obtain the sub-optimal value of the signal-to-interference ratio (SIR) threshold constrained by fixed node densities, using the steepest descent method in order to maximize the network throughput. The results show that in such networks, the proposed hybrid duplex mode selection scheme improves the level of throughput. The results also indicate the effect of imperfect SIC on reducing the throughput. Moreover, it is demonstrated that by choosing an optimal SIR threshold for mode selection process, the achievable throughput in such networks can increase by around 5%.