SI-INSPIRED ENERGY AWARE QoS ROUTING TREE FOR WSN

A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.

An Energy-Aware Routing Protocol Based on Effective Transmission in Sensor Networks

In order to minimize the energy consumption in the discovery of the routing path, this paper introduces a novel concept of effective transmission （ET） that ensures each forwarding node is not only farther from the source node, but also nearer to the destination node with respect to its sender, An energ-aware routing protocol based on ET is proposed. It enables the energy consumption for each hop to be the least for the transmission. The simulation results show the routing protocol is effective in the performance of energy consumption comparing with some other routing protocols.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

Nowadays,virtual machine migration(VMM)is a trending research since it helps in balancing the load of the Cloud effectively.Several VMM-based strategies defined in the literature have considered various metrics,such as load,energy,and migration cost for balancing the load of the model.This paper introduces a novel VMM strategy by considering the load of the Cloud network.Two important aspects of the proposed scheme are the load prediction through the support vector regression(SVR)and the optimal VM placement through the proposed dragonfly-based crow(D-Crow)optimization algorithm.The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm(CSA)into dragonfly algorithm(DA).Also,the proposed VMM strategy defines a load balancing model based on the energy consumption,load,and the migration cost to achieve the energy-aware VMM.The simulation of the proposed VMM strategy is done based on the metrics such as load,energy consumption,and the migration cost.From the results,it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%,10.0368%,and 11.0639%for the load,energy consumption,and migration cost,respectively.

FloodNet： Coupling Adaptive Sampling with Energy Aware Routing in a Flood Warning System

We describe the design of FloodNet, a flood warning system, which uses a grid-based flood predictor model developed by environmental experts to make flood predictions based on readings of water level collected by a set of sensor nodes. To optimize battery consumption, the reporting frequency of sensor nodes is required to be adaptive to local conditions as well as the flood predictor model. We therefore propose an energy aware routing protocol which allows sensor nodes to consume energy according to this need. This system is notable both for the adaptive sampling regime and the methodology adopted in the design of the adaptive behavior, which involved development of simulation tools and very close collaboration with environmental experts.

An energy-degree evaluation metric for clustering purposes in mobile ad hoc networks

Network clustering is the process of partitioning a network into a number of virtual entities mastered by certain nodes,called cluster centers that are responsible for collecting and maintaining topology information and managing the routing processes.In ad hoc networking,clustering has been introduced to deal with the dynamic topology by providing a temporarily stable network core.Clustering process mainly depends on the metric upon which the selection of cluster centers is performed.A wide range of clustering metrics were introduced in the literature based on network issues including mobility and connectivity degree,giving rise to a variety of clustering schemes.Although clustering provides energy consumption reduction,residual energy has not received enough attention and few studies have addressed the clustering on the basis of this feature.This paper discusses the current clustering metrics and proposes an energy-degree evaluation metric with mobility consideration taking into account the nodes residual energy and the network connectivity as two main keys of clustering.

Energy-aware scheduling with reconstruction and frequency equalization on heterogeneous systems

With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.