Peng Zhang

Bio: Peng Zhang is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Efficient energy use & Energy consumption. The author has an hindex of 2, co-authored 2 publications receiving 159 citations.

Energy-Efficient Multi-Constraint Routing Algorithm With Load Balancing for Smart City Applications

Abstract: Many researches show that the power consumption of network devices of ICT is nearly 10% of total global consumption. While the redundant deployment of network equipment makes the network utilization is relatively low, which leads to a very low energy efficiency of networks. With the dynamic and high quality demands of users, how to improve network energy efficiency becomes a focus under the premise of ensuring network performance and customer service quality. For this reason, we propose an energy consumption model based on link loads, and use the network’s bit energy consumption parameter to measure the network energy efficiency. This paper is to minimize the network’s bit energy consumption parameter, and then we propose the energy-efficient minimum criticality routing algorithm, which includes energy efficiency routing and load balancing. To further improve network energy efficiency, this paper proposes an energy-efficient multi-constraint rerouting (E2MR2) algorithm. E2MR2 uses the energy consumption model to set up the link weight for maximum energy efficiency and exploits rerouting strategy to ensure network QoS and maximum delay constraints. The simulation uses synthetic traffic data in the real network topology to analyze the performance of our method. Simulation results that our approach is feasible and promising.

Energy-Efficient Heterogeneous Networking for Electric Vehicles Networks in Smart Future Cities

Abstract: Electric vehicles networks have become hot topics in research and industry and played an important role in smart future cities. However, high energy consumption is a significantly challenge for these applications. This article proposes a electric vehicles cloud computing framework to perform energy-efficient heterogeneous networking for electric vehicles network in smart future cities. The software-defined networking ideas is used to enable different devices including electric vehicles to access the cloud computing network for electric vehicles connected. The edge computing is exploited to run quick computing and communication for these application. Then an energy-efficient heterogeneous networking method is presented to overcome high energy consumption. The mixed integer linear programming optimization model and two heuristic models are proposed to perform energy-efficient networking. An networking algorithm is proposed to achieve highly energy-efficient networking for electric vehicles network. The detailed simulation experiments are conducted to validate our approach. Simulation results illustrate that the proposed method is efficient and feasible.

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

Abstract: Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.

Greening the Smart Cities: Energy-Efficient Massive Content Delivery via D2D Communications

Abstract: Massive multimedia services have been considered as one the most prominent characteristics for smart cities. In this paper, we propose an energy-efficient content delivery system via the device-to-device communications, which realizes the large-scale content delivery among mobile devices with constrained energy, unpredictable demand, limited storage, random mobility, and opportunistic transmission. The highlights of this paper lie in two parts. On the theoretical end, through exploring the relationship among the coding, storage, and transmission, a systematic energy-saving content delivery fashion is investigated. On the technical end, a totally distributed content delivery system is designed in a simple and efficient manner, in which each device only utilizes local information to make decisions and implements its own scheme individually. Importantly, the proposed scheme is realized in a practical smart city system, and numerical results demonstrate that it is flexible to various users’ needs and communication environments.

Resource Allocation in Energy-Cooperation Enabled Two-Tier NOMA HetNets Toward Green 5G

Abstract: This paper focuses on resource allocation in energy-cooperation enabled two-tier heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA), where base stations (BSs) are powered by both renewable energy sources and the conventional grid. Each BS can serve multiple users at the same time and frequency band. To deal with the fluctuation of renewable energy harvesting, we consider that renewable energy can be shared between BSs via the smart grid. In such networks, user association and power control need to be re-designed, since existing approaches are based on OMA. Therefore, we formulate a problem to find the optimum user association and power control schemes for maximizing the energy efficiency of the overall network, under quality-of-service constraints. To deal with this problem, we first propose a distributed algorithm to provide the optimal user association solution for the fixed transmit power. Furthermore, a joint user association and power control optimization algorithm is developed to determine the traffic load in energy-cooperation enabled NOMA HetNets, which achieves much higher energy efficiency performance than existing schemes. Our simulation results demonstrate the effectiveness of the proposed algorithm, and show that NOMA can achieve higher energy efficiency performance than OMA in the considered networks.

Power-domain non orthogonal multiple access (PD-NOMA) in cooperative networks: an overview

Abstract: Non-orthogonal multiple access (NOMA) scheme is emerging as a favourable multiple access scheme for future 5G networks. Compared to orthogonal multiple access techniques, NOMA provides spectral efficiency, user fairness, better connectivity, enhanced data rate and reduced latency. Thus, NOMA can be a suitable multiple access technique for 5G networks. On the other hand, in wireless networks, cooperation is a well-recognized proven technique for performance enhancement. Cooperative networks offer multiple desirable advantages, including high performance, reliability and greater coverage area. It is believed that in future 5G systems, many existing wireless technologies will be combined with new technologies. Power domain-NOMA (PD-NOMA) has features that can provide opportunities of improved performance and better spectral utilization for downlink cooperative networks. Recently, research works of incorporating PD-NOMA in cooperative networks have gained attention of researchers around the globe. This article surveys the recent research trends in PD-NOMA based cooperative network by reviewing related recent research on performance analysis of cooperative PD-NOMA systems, resources allocation, and impact of relay selection. Additionally, this review article discusses the performance of cooperative PD-NOMA networks when they are integrated with other 5G technologies including cognitive radio, full duplex radio and wireless energy harvesting. Furthermore, some unaddressed issues are highlighted for future research in this area.

Greening internet of things for greener and smarter cities: a survey and future prospects

Abstract: Tremendous technological developments in the field of internet of things (IoT) have changed the way we live and work. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also contributes to toxic pollution, consumes energy and generates e-waste. These persistent issues place new stress on the smart world and environments. To enhance the benefits and reduce the harmful effects of IoT, there is an increasingly desired to move towards green IoT. Green IoT is seen as the environmentally friendly future of IoT. Therefore, it is necessary to put different desired measures to conserve environmental resources, reduce carbon footprints and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, sensors, communications, clouds, and internet operate in synergy towards the common goal of increased energy efficiency and reduced carbon emissions. This work presents a thorough survey of the current ongoing research and potential technologies of green IoT with an intention to provide some directions for future green IoT research.