Showing papers by "Zheng Chang published in 2018"

Multiobjective Optimization for Computation Offloading in Fog Computing

Abstract: Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.

Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era

Abstract: The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resource in future networks. The learning-based approaches for network edge caching are discussed, where a vast amount of data can be harnessed for content popularity estimation and proactive caching strategy design. An outlook of research directions, challenges, and opportunities is provided and discussed in depth. To validate the proposed solution, a case study and a performance evaluation are presented. Numerical studies show that several gains are achieved by employing learning- based schemes for edge caching.

Joint Radio and Computational Resource Allocation in IoT Fog Computing

Abstract: The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.

Socially Aware Dynamic Computation Offloading Scheme for Fog Computing System With Energy Harvesting Devices

Abstract: Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

SAGECELL: Software-Defined Space-Air-Ground Integrated Moving Cells

Abstract: Ultra-dense networks (UDNs) provide an effective solution to accommodate the explosively growing data traffic of multimedia services and real-time applications. However, the densification of large numbers of static small cells faces many fundamental challenges, including deployment cost, energy consumption and control, and so on. This motivates us to develop software-defined space-air-ground integrated moving cells (SAGECELL), a programmable, scalable, and flexible framework to integrate space, air, and ground resources for matching dynamic traffic demands with network capacity supplies. First, we provide a comprehensive review of state-of-the-art literature. Then the conceptual architecture of SAGECELL is elaborated in detail, and the technological benefits are emphasized. Next, we present four typical application cases of SAGECELL. A case study is conducted based on real-world road topology to validate the efficiency and flexibility of SAGECELL. Finally, we identify future research directions and challenges, and conclude this article.

BEGIN: Big Data Enabled Energy-Efficient Vehicular Edge Computing

Abstract: Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values. Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this work and outline future research open issues.

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Abstract: Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can offload those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to offload sets the price strategies for different SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme.

Energy-efficient workload offloading and power control in vehicular edge computing

Abstract: In this paper, an energy-efficient vehicular edge computing (VEC) framework is proposed for in-vehicle user equipments (UEs) with limited battery capacity. Firstly, the energy consumption minimization problem is formulated as a joint workload offloading and power control problem, with the explicit consideration of energy consumption and delay models. Queuing theory is applied to derive the stochastic traffic models at UEs and VEC nodes. Then, the original NP-hard problem is transformed to a convex global consensus problem, which can be decomposed into several parallel subproblems and solved subsequently. Next, an alternating direction method of multipliers (ADMM)-based energy-efficient resource allocation algorithm is developed, whose outer loop representing iterations of nonlinear fractional programming, while inner loop representing iterations of primal and dual variable updates. Finally, the relationships between energy consumption and key parameters such as workload offloading portion and transmission power are validated through numerical results.

Energy-Efficient and Secure Resource Allocation for Multiple-Antenna NOMA With Wireless Power Transfer

Abstract: Non-orthogonal multiple access (NOMA) is considered as one of the promising techniques for providing high data rates in the fifth generation mobile communication. By applying successive interference cancellation schemes and superposition coding at the NOMA receiver, multiple users can be multiplexed on the same subchannel. In this paper, we investigate resource allocation algorithm design for an OFDM-based NOMA system empowered by wireless power transfer (WPT). In the considered system, users who need to transmit data can only be powered by the WPT. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among multiple users by optimizing time, power and subchannel allocation. Moreover, we also take into consideration for the practical case that the statistics of the channel state information of the eavesdropper is not available. In order to address the optimization problem and its high computational complexity, we propose an iterative algorithm with guaranteed convergence to deliver an upper bound and a suboptimal solution in more general cases. For some special cases, we identify the optimality condition that ensures the global optimum in our algorithm. Extensive simulation studies demonstrate the competitiveness and effectiveness of the proposed algorithmic solution over conventional OFDMA systems as well as over other existing NOMA resource allocation schemes.

Propagation Channels for mmWave Vehicular Communications: State-of-the-art and Future Research Directions

Abstract: Vehicular communications essentially support automotive applications for safety and infotainment. For this reason, industry leaders envision an enhanced role of vehicular communications in the fifth generation of mobile communications technology. Over the years, the number of vehicle-mounted sensors has increased steadily, which potentially leads to more volume of critical data communications in a short time. Also, emerging applications such as remote/autonomous driving and infotainment such as high-definition movie streaming require data-rates on the order of multiple Gbit/s. Such high data-rates require a large system bandwidth, but very limited bandwidth is available in the sub-6 GHz cellular bands. This has sparked research interest in the millimeter wave (mmWave) band (10 GHz-300 GHz), where a large bandwidth is available to support the high data-rate and low-latency communications envisioned for emerging vehicular applications. However, leveraging mmWave communications requires a thorough understanding of the relevant vehicular propagation channels, which are significantly different from those investigated below 6 GHz. Despite their significance, very few investigations of mmWave vehicular channels are reported in the literature. This work highlights the key attributes of mmWave vehicular communication channels and surveys the recent literature on channel characterization efforts in order to provide a gap analysis and propose possible directions for future research.

Towards Service-Oriented 5G: Virtualizing the Networks for Everything-as-a-Service

Abstract: It is widely acknowledged that the forthcoming 5G architecture will be highly heterogeneous and deployed with high degree of density. These changes over the current 4G bring many challenges on how to achieve an efficient operation from the network management perspective. In this paper, we introduce revolutionary vision of the future 5G wireless networks, in which operating the wireless networks is no longer limited by hardware or even software. Specifically, by the idea of virtualizing the wireless networks, which has recently gained increasing attention, we introduce the everything-as-a-service (XaaS) taxonomy to light the way towards designing the service-oriented wireless networks. The concepts and challenges along with the research opportunities for realizing XaaS in wireless networks are overviewed and discussed.

Latency-Oblivious Distributed Task Scheduling for Mobile Edge Computing

Abstract: Mobile Edge Computing (MEC) is emerging as one of the effective platforms for offloading the resource- and latency-constrained computational services of modern mobile applications. For latency- and resource-constrained mobile devices, the important issues include: 1) minimize end-to-end service latency; 2) minimize service completion time; 3) high quality-of-service (QoS) requirement to offload the complex computational services. To address the above issues, a latency-oblivious distributed task scheduling scheme is designed in this work to maximize the QoS performance and goodput for the MEC services. Unlike most of the existing works, we consider the latency-oblivious property of different services in order to achieve the optimized goodput and service latency. Furthermore, we design an optimal decision engine for efficiently offloading the computational services. Simulation results are presented to demonstrate the effectiveness of the proposed offloading scheme over other existing state-of-the-art solutions, in terms of service latency, goodput, service completion time and fairness.

Collaborative Mobile Clouds: An Energy Efficient Paradigm for Content Sharing

Abstract: On the way toward enabling efficient content distribution, reducing energy consumption and prolonging battery life of mobile equipment, an emerging paradigm, i.e., mobile cloud, which is based on content distribution, was proposed. As a mobile platform that is oriented toward content distribution, mobile cloud is also foreseen as an energy-efficient solution for future wireless networks. The benefits of using CMC for content distribution or distributed computing from social networking perspectives have been studied earlier. In this article, we first present the concepts of CMC and then discuss the energy-efficiency benefits from the system-level point-of-view as well as open challenges in designing a green CMC. Moreover, we investigate the problem of forming mobile clouds for the purpose of content distribution and energy-efficiency. Specifically, given a group of users interested in downloading the same content from an operator, an energy-aware based user selection and scheduling algorithm is proposed. Simulation examples show that a significant energy-saving performance can be achieved without depleting the battery of any user equipment by the proposed scheme. Also, the research potential is discussed in the context of CMC.

Reliable and Privacy-Preserving Task Recomposition for Crowdsensing in Vehicular Fog Computing

Abstract: The advancement in vehicles has enabled crowdsensing in vehicular fog computing (VFC), where vehicles are recruited to be assigned different subtasks and participate sensing activities that may disclose their sensitive information. To stimulate more participants, VFC systems should be able to provide reliable and privacy-preserving data transmission and processing mechanisms for the sensing report. To ensure the report process, we present a reliable and privacy- preserving task recomposition (REPTAR) for multiple subtasks sensing in VFC. Modified homomorphic Paillier encryption and superincreasing sequence are employed for aggregating hybrid subtasks into one ciphertext. Reliability is verified by means and variances of each aggregated subtasks from different vehicular fog nodes. Detailed security analysis and performance evaluation are provided to demonstrate the security, privacy-enhancement, efficiency and low complexity of the proposed REPTAR.

Outage Analysis of Relay-Aided Non-Orthogonal Multiple Access with Partial Relay Selection

Abstract: Non-Orthogonal multiple access (NOMA) holds promise as a spectrally efficient multiple access scheme for 5G communication networks. This work investigates the performance of NOMA in a dual-hop amplify-and-forward (AF) relaying network, which is subject to Nakagami-$m$ fading. Specifically, we obtain a novel closed-form expression of the outage probability for the near and far users when the partial relay selection (PRS) scheme is used for selecting the best among $N$ intermediate relays. The users are considered to employ selection combining technique in order to combine the relayed and the direct transmission signals for an increased reliability of detection. Then, we evaluate the impact of the number of intermediate relays, the NOMA power allocation factor, and the Nakagami-$m$ fading severity parameter on the outage performance of the NOMA users. The analytical results are validated by performing Monte Carlo Simulations.

Intercept Probability Analysis of Wireless Powered Relay System in kappa-mu Fading

Abstract: Energy harvesting relays are predicted to play a pivotal role in large scale networks This paper evaluates the secrecy performance of a system that employs a two-way decode-and-forward relay assisting transmission between two nodes The relay has RF energy harvesting capability and it can receive energy from the RF signal and the transmission of the system can be overheard by an eavesdropper More specifically, we derive an exact expression for the interception probability when the main and wiretap links experience generalized κ-μ fading The impacts of the power- splitting factor at the relay and the fading parameters on the secrecy performance of the considered system are also assessed Numerical and simulation results are presented to verify the derived results

Resource allocation for wireless virtualized hetnet with caching and hybrid energy supply

Abstract: In this work, we propose a distributed user association and resource allocation scheme for the wireless virtualized information-centric networks with hybrid energy supply, where the base station (BS) equipped with caching and energy harvesting capabilities to reduce the COPEX and OPEX cost. In particular, with the objective to obtain the utility maximization for the network operators, a joint user association, caching, spectrum and power problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem with low complexity, the original problem is divided into two subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

Bidirectional Wireless Information and Power Transfer With an Energy Accumulating Relay

Abstract: In this paper, a bidirectional wireless information and power transmission model with an energy accumulating relay is studied. To implement wireless information and power transmission simultaneously, a time-switching protocol is adopted. In addition, two energy harvesting (EH) protocols are proposed. In the bidirectional continuous-time EH (BI-CT) protocol, the whole transmission block is divided into two parts: EH and information transmission. In the bidirectional discrete-time EH (BI-DT) protocol, the EH time is either 0 or 1. The relay first harvests energy and then forwards information from the user node to the source node. Notably, the relay node stores energy during poor channel conditions, increasing the energy efficiency. Theoretical analyses on the performance of the two proposed protocols are given. It is found that the throughput efficiency depends on various system parameters, such as the transfer power at the relay, distance between the nodes, noise power, and SNR detection threshold. Furthermore, it is observed that BI-DT outperforms BI-CT when the noise power at the relay is high or the distance between the source and the relay is large.

Operator Revenue Analysis for Device-to-Device Communications Overlaying Cellular Network

Abstract: Device-to-device (D2D) communications has recently gathered significant research interest due to its efficient utilization of already depleting wireless spectrum. In this article, we considered a scenario where D2D users communicate in the presence of cellular users in an overlay network setup. In order to analyze the revenue of service providers in monetary terms, the paper provides exact expressions of operator profit for both D2D and cellular users. More specifically, we take into account different network parameters including user density, transmit power and channel variations to understand their impact on the total revenue of the operator. Finally, we derive the balancing value of frequency partitioning factor and provide relevant discussion on the analytical expression. Our findings show that D2D communications outperform the conventional cellular communications in terms of revenue generation capability. Our results have been verified by performing extensive simulations.

A contract-based resource allocation mechanism in wireless virtualized network

Abstract: The rapidly increasing mobile traffic demand poses both new communication requirements and challenges on existing communication networks in terms of technologies and business models. Wireless network virtualization is a promising technology to provide service-based architecture and contract theory is a powerful framework from microeconomics for providing tools to model incentive mechanisms. In this work, a novel contract theoretic incentive mechanism is proposed to study how to provide services to multiple users in the wireless virtualized networks. Infrastructure providers (InPs) is considered to own the physical networks and mobile virtual network operator (MVNO) has the information of the users and needs to lease the physical radio resources for providing services to subscribed users. In particular, a contract theoretic approach is utilized to model the trading process between the MVNO and multiple InPs. Subsequently, the corresponding optimal contract is derived respectively to maximize the payoff of the MVNOs while maintaining the benefits of the InPs in the trading process. With numerical results, it can be observed that the proposed contract theoretic approach can effectively stimulate InPs' participation, improve the payoff of the MVNO and outperform other schemes.

Security and Privacy in Wireless IoT

Abstract: The 13 articles in this special section focus on security and privacy in wireless Internet of Things (IoT). IoT is a paradigm that involves networked physical objects with embedded technologies to collect, communicate, sense, and interact with the external environment through wireless or wired connections. With rapid advancements in IoT technology, the number of IoT devices is expected to surpass 50 billion by 2020, which has also drawn the attention of attackers who seek to exploit the merits of this new technology for their own benefits. There are many potential security and privacy threats to IoT, such as attacks against IoT systems and unauthorized access to private information of end users. As IoT starts to penetrate virtually all sectors of society, such as retail, transportation, healthcare, energy supply, and smart cities, security breaches may be catastrophic to the actual users and the physical world. To tackle the security challenges in the design of future wireless IoT systems, we have organized this Special Issue focusing on the security, privacy, and performance of future wireless IoT.

Energy efficient optimisation for large-scale multiple-antenna system with WPT

Abstract: In this study, an energy-efficient optimisation scheme for a large-scale multiple-antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non-linear fractional programming-based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes.

Enabling Technologies for Smart Internet of Things

Abstract: The twelve articles in this special section focus on new and enabling technologies for smart Internet of Things (IoT). IoT supports ubiquitous information exchange and content sharing among smart devices with little or no human intervention, is a key enabler for various applications. Smart IoT cannot be simply regarded as an upgrade of current IoT by just adding to or replacing sensors/actuators/RFID tags in smart devices. It should be redesigned from the physical layer to the application layer in a bottom-up way. While the traditional-sense IoT paradigm, including the present narrowband IoT (NB-IoT) proposal, aims to provide low-rate, short-range, and relatively stationary connections to the wireless sensors and/or RFID tags, the new architecture is expected to support higher data rates, longer communication ranges, and more flexible mobility for smart devices, with the assistance of some new caching, communication, and computing technologies. These also enable smart IoT to be applied to a broader application area including crowdsensing, crowdsourcing, AR/VR, UAV, and so on, to realize smarter cities, grid, and health, and more intelligent transportation systems. Fully utilizing the communication, computing, caching, and security technologies can essentially complement the current development of IoT.The resulting new structure may have a wider application over current infrastructure-based cellular networks and traditional sensor-based networks by adopting all these features.

Wireless Caching Aided 5G Networks

Abstract: With the arrival of 5G mobile cellular networks and the proliferation of smart mobile devices, the wireless mobile data increase unprecedentedly, which will inevitably impose great pressure on the backhaul and degrade the QoE of mobile users. Edge computing or fog computing can provide enhanced service quality with increased network capacity and low latency, by utilizing elastic resources of edge or fog nodes, for example, computation, storage, and networking. Wireless caching, as an important technology for edge computing, has been attractingmore andmore focuses from both industry and academia. In caching-aided networks, popular data files can be proactively cached at the edge of mobile networks. These cached contents will be delivered to users directly from the edge of the networks. Meanwhile, the shift of wireless traffic from centrally generated voice service to locally created data also provides significant opportunities for caching, which is known as the trend of information-centric networks. In this special issue, we have invited a few papers to give insights on wireless caching aided 5G networks. One paper of this special issue addresses an energyefficient caching problem based on shot noise model in backhaul-aware cellular networks, with both the cache hit rate and the optimal cache considered. A distributed caching policy is proposed to enhance the cache hit rate, and an optimization is formulated to analyze the tradeoff between energy efficiency and cache capacity. Another paper presents the performance analysis for Internet-of-things system under Nakagami channels, with both the direct link and the multihop relaying caching considered. Its main contribution is that the outage probability and bit error rate of the system are derived analytically without any approximation. Another paper investigates the energy efficiency in cacheenabled cellular networks with the limited backhaul, where the successful content delivery probability is calculated by the stochastic geometry method, and the analytical expressions of throughput, power consumption, and energy efficiency are derived as well for various cases. Another paper of this special issue proposes a cooperative strategy to improve both the caching replacement and efficiency at wireless edges in IP-based networks. User’s QoE is introduced to estimate the caching efficiency, and various caching allocation methods are adopted for better user’s quality of experience. Another paper analyzes the system performance of a two-hop decode-and-forward relaying network without and with cache, respectively. The analytical expressions of outage probability and symbol error rate are derived, and the system diversity order is improved fast when the cache is adopted. Another paper considers a signal detection problem in spatial modulation 3D-MIMO systems, and the normalization preprocessing and structured sparsity of sparse signals are exploited to avoid the overamplified noise and reduce computation, respectively. Simulation results prove that the proposed algorithm in this paper surpasses the conventional signal detectors.

Energy-Efficient Resource Optimization with Wireless Power Transfer for Secure NOMA Systems

Abstract: In this paper, we investigate resource allocation algorithm design for secure non-orthogonal multiple access (NOMA) systems empowered by wireless power transfer. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among multiple users by optimizing time, power and subchannel allocation. Moreover, we also take into consideration for the practical case that the statistics of the channel state information of the eavesdropper is not available. In order to address the optimization problem and its high computational complexity, we propose an iterative algorithm with guaranteed convergence to deliver a suboptimal solution for general cases. For some special cases, the solution ensures the global optimum. Numerical studies demonstrate the competitiveness of the proposed algorithmic solution over conventional orthogonal multiple access systems as well as over other existing NOMA resource allocation schemes.

Autonomous Power Line Inspection Based on Industrial Unmanned Aerial Vehicles: An Energy Efficiency Perspective

Abstract: In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

Socially-aware content delivery for device-to-device communications underlay cellular networks

Abstract: The emerging Device-to-Device (D2D) communications paradigm has received increasing attentions from both research and industry communities. Most of the previous works on D2D communications, however, mainly concentrated on interference management and throughput optimization. Some recent works have observed that by properly caching the data at the device level, the Quality of Experience (QoE) can be enhanced. In this paper, we propose a novel socially-aware data content delivery scheme for D2D underlay wireless networks by taking social ties and common interests among users into consideration. In particular, we jointly consider the features of the D2D transmissions in the physical layer, social characteristics, and interest similarity impact to formulate the D2D content delivery problem. Accordingly, an accurate and efficient three-dimensional matching-based scheme is presented to address the formulated problem. The performance evaluations are conducted to show the potential gain of the proposed approach.

Energy Accumulating Based Wireless Information and Power Transfer

Abstract: In this paper, a bidirectional wireless information and power transmission model with an energy accumulating relay is studied. To implement wireless information and power transmission simultaneously, a time-switching (TS) protocol is adopted. In addition, two energy harvesting (EH) protocols are proposed, named bidirectional continuous time EH (BI-CT) protocol and bidirectional discrete time EH (BI-DT) protocol. The relay node stores energy during poor channel conditions, increasing the energy efficiency. Theoretical analyses on the performance of the two proposed protocols are given. It is found that the throughput depends on various system parameters, such as the transfer power at the relay, distance between and so on. Furthermore, it is observed that BI-DT outperforms BI-CT when the noise power at the relay is high or the distance between the source and the relay is large.

Reducing Power Consumption of Wireless Networks Through Collaborative DMC Mobile Clusters

Abstract: Reducing the energy consumption of the wireless networks is significantly important for the economic and ecological sustainability of the ICT industry, as high energy consumption may limit the performance of wireless networks, and is one of the main network costs. To solve the energy consumption problem, especially on the terminal side, a scheme known as distributed mobile cloud (DMC) is considered to be a potential solution. Multiple mobile terminals (MTs) can cooperatively take advantage of good quality links among the MTs to save energy when receiving from the Base Station. In this paper, we aim to find the optimal transmit power to further reduce the energy consumption of DMC. From simulation studies, it is shown that up to 80% energy savings can be accomplished when using optimal transmit power, compared to using the standard DMC without exploring the optimal transmit power.