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The transmission of point cloud video streaming requires the scheduling of various computing and transmission resources. Existing research rarely considers the energy consumption issues caused by the computing tasks of terminal display devices. To solve this problem, a point cloud video streaming transmission scheme assisted by mobile edge computing (MEC) was proposed, which offloaded part of computing tasks to the MEC server based on the access bandwidth and point cloud video content. A joint optimization model was established in this scheme to maximize the quality of user’s viewing experience and minimize the energy consumption of terminal device under the constraints of network resources, terminal and edge computing resources. Experimental results show that the proposed scheme can improve the viewing quality of users and reduce the energy consumption of terminal equipment compared with the contrast scheme under different conditions.

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy-aware programming (EAP) is a new approach to reduce energy consumption of computing systems. It introduces energy as one of the main design metrics into the process of software development to reduce program energy consumption by adjusting the way of programming. The implementation of EAP is facing some difficulties in finding energy consumption hot spots, identifying main factors which cause excessive energy consumption, and locating inappropriate code segments in the program. To address these issues, this paper proposes a new method called EPC (energy-performance correlation) for joint measurement and analysis of energy consumption and perfor-mance events during program execution. Firstly, the basic principles of EPC are introduced and the implementation of an EPC-based tool, FPowerTool, for program energy consumption measurement and analysis is presented. Then, the method of energy-performance events correlation analysis for identifying the main factors influencing energy consumption is presented. Finally, a set of programs is used as case studies to show how to locate the code segments related to high energy consumption by correlation analysis, and how to change the coding and data placement and access to reduce the program energy consumption. The experiment results show that based on the energy-aware and analysis capabilities provided by the EPC method, program performance and energy efficiency can be improved by improving data definition, assignment, placement, and access methods.

Edge computing can provide users with low-latency and high-bandwidth services by deploying many edge servers at the network edge.However, a large number of deployments also bring problems of high energy consumption.When dispatching tasks from end devices to different edge servers, different energy consumption and delays will occur due to the edge servers’ heterogeneity.Therefore, it is a challenge to select an optimal server among many edge servers for task dispatching so that energy consumption and delay are relatively low.An energy-aware task dispatching method with quality of service (QoS) guarantee based on online learning was proposed.It can obtain real-time information by interacting with the environment to ensure energy consumption was minimal while the QoS was acceptable when dispatching tasks.Experiments show that the proposed method can dispatch tasks efficiently to the optimal server compared with other methods, thereby reducing the edge computing network’s overall energy consumption significantly.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wireless sensor network has been increasingly applied for detecting，monitoring and locating targets in smart grid.In a wireless sensor network，energy is deemed as a critical resource and life of the system can be prolonged by energy saving strategy.A node detection and localization method based on energy-aware was presented.The method could be used for the clustering-based wireless sensor networks.It employed a posterior algorithm using a two-step communication protocol between the clustering head sensor and sensors inside.Only a small amount of node information was needed for the clustering head to run the localization program to determine the subset of sensors for further localization.In this way，consumption of energy and demand for communication bandwidth could be reduced，and the system life could be prolonged as well.Experiment results show that this locating method is able to save up to 32% of energy.

In order to enhance the energy efficiency and balance node of energy load in the clustering wireless sensor network, this paper proposed cognitive network energy optimization algorithm based on energy-aware and gamma statistical model. First, the algorithm based on cognitive radio channel energy perceptual model, it could get the energy distribution equation under the sleep mode and network operation mode. Next, it analysed the total energy consumption of network by using gamma function in multiple relay network scenarios, and it based on the number of network hops and the total energy consumption of cluster head to propose the energy optimization strategy, to balance the energy of cluster head while minimizing the total network energy consumption. Simulation results show, the proposed energy optimization algorithm compared based on cognitive radio networks saving routing scheme and multi-energy detector-based cognitive networks in the total energy consumption of network, the energy savings were increased by 37.6% and 12. 2% , and the algorithm analysed energy distribution network by the gamma function with a certain accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of cluster unbalanced energy consumption limiting the network lifetime in traditional routing algorithm of wireless sensor networks (WSN),this paper proposed a new energy-aware routing algorithm based on improved backoff distributed clustering protocol (IBDCP)and random virtual backbone trees (RVBT).Firstly, it organized the sensor nodes into different clusters. Then, used IBDCP to initialize time delay for each node to competition. Finally, in order to form a cluster, the nodes were considered their residual energy and distance to decide whether to join CH, and built random virtual backbone trees in the sink to facilitate the routing of data. It proves that the algorithm can achieve constant news and linear time complexity, and experimental results show that this algorithm is superior to other existing in aspects of the network life-time, energy consumption and other parameters. And the network lifetime is extended at least 15% than EMRA's and at least 150% more than the BDCP's. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional energy-aware OLSR protocol cannot reduce transmission power consumption and balance the residual energy between nodes at the same time, we develop a new routing protocol called OLSR routing protocol based on residual energy ratio and transmission power consumption (OLSR_RC). A composite energy cost involving the above two indicators is constructed, and is used as a routing metric. On one hand, the OLSR_RC protocol reduces the total power consumption of the entire network. On the other hand, it prevents the energy of the low-energy nodes from being depleted rapidly. In addition, we adopt the hybrid ARIMA-ANN model for forecasting residual energy level of the nodes, which can reduce the influence on route selection caused by topology control(TC) message loss. The new routing protocol has wide application prospects in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network(WSN)is usually deployed in complex environment, and an attacker can easily inject false data by capturing nodes, thus causing serious consequences. A malicious nodes detection algorithm based on feature nodes analysis(DAFNA)is proposed. Firstly, the energy values of the event sources are estimated, and nodes healthy characteristics are maintained in this process. Secondly, we establish coordinates according to the feature nodes, conduct a variance analysis of the calculated and perceived distances between the nodes to be detected and the event source, thus the malicious nodes are identified. Simulation result verifies the effectiveness of the proposed algorithm, and compared with Hur algorithm it has a better accommodation of malicious nodes while requiring less prior knowledge. [ABSTRACT FROM AUTHOR]

In order to resolve the shortcomings of AODV protocol in energy consumption, improve the energy-aware, and achieve flexible compromise of transmission efficiency and energy consumption in a variety of network environment, this paper proposed an optimized energy-aware reverse AODV (ER-AODV) routing protocol. ER-AODV protocol applied the design scheme on the basis of backward AODV and with combination of minimum power and energy balance, and proposed an optimized link cost function to select the best path as the route. The simulation result shows that when the weighting factor in the cost function is unchangeable, ER-AODV can clearly improve the energy-aware compared to AODV. When the weighting factor in the cost function is changeable, ER-AODV is able to achieve flexible compromise of transmission efficiency and energy consumption in a variety of network environment. [ABSTRACT FROM AUTHOR]