Energy Queue Research Articles

Event-triggered online energy flow control strategy for regional integrated energy system using Lyapunov optimization

Regional Integrated Energy System (RIES) provides an inspiring perspective for constructing the future-oriented Energy Internet. However, the integration of heterogeneous energy system increases the complexity of critical power infrastructures, which limits the feasibility of energy management in real practice. To mitigate this issue, we propose an online optimization strategy for RIES with heating, ventilation and air conditioning (HVAC) loads in this paper. Specifically, a stochastic problem is formulated to minimize the economic cost with the consideration of power to gas and combined heat and power techniques. In addition, considering the changes of specific events. i.e., essential load, electricity price, queue length of renewable energy sources (RES) and comfort level of end-users, an event-triggered mechanism is established to enable automatic execution of scheduling signals. Without relying on a priori knowledge, we use the Lyapunov optimization method to control energy queue of HVAC and the system stability in real time. The feasibility of the proposed online energy flow control algorithm has been validated by the theoretical deduction. The numeral simulation based on real-world traces demonstrates that the proposed method maximizes the economic profit by incenting the load transferring during peak period of electricity price and effectively improves the utilization ratio of RES. Moreover, this method can ensure the end-user’s comfort while dramatically reducing the dispatching frequency of HVAC with the event-triggered mechanism.

Resource Scheduling and Energy Cooperation in HetNet with Cross-Layer Interference Constraints

Facing more and more severe global warming problems, renewable energy, as an alternative to traditional fossil fuels, is attracting more and more attentions due to its capability of reducing carbon emission. This paper considers two-tier HetNets with orthogonal frequency division multiple access (OFDMA), where the macro base station (MBS) is powered by power grid and small base stations (SBSs) have hybrid energy supplies. Through smart grid, SBSs can share their renewable energy with each other. We consider the problem of cross-layer interference caused by spectrum reuse, the burst of user data, and the randomness of renewable energy arrivals. Through energy cooperation, this paper investigates maximizing the time-average energy efficiency of SBSs. Based on user data queue and SBS energy queue, the optimal problem is decoupled into two subproblems by Lyapunov optimization: resource allocation subproblem and energy scheduling and energy cooperation subproblem. By solving two subproblems, the online solution to the optimization problem is obtained. Through theoretical analysis and simulations, both user data queues and energy queues have an upper bound, the network is stable, and the proposed algorithm performs better than the basic algorithm without energy cooperation.

Buffer-Aided Secure Relay Networks With SWIPT

In this paper, we propose the use of data and energy buffering in wireless powered relays, while we also design scheduling schemes, with the aim to increase the secrecy throughput. More specifically, it is assumed that Alice transmits information to Bob by using half-duplex randomize-and-forward relaying with simultaneous wireless information and power transfer, under the existence of a passive eavesdropper. First, considering a conventional system, i.e., without energy and data buffering, an optimal baseline policy is designed. Then, focusing on buffer-aided relaying, we maximize the average secrecy throughput under the stability requirement for data and energy queues by using the framework of Lyapunov optimization, according to which, we transform the initial stochastic optimization problem into a sequence of online subproblems. The proposed online adaptive transmission policy dynamically allocates the available resources, i.e., time and power, with respect to the channel state information, as well as the states of the buffers. In addition, our analysis reveals the existence of an interesting trade-off between the average secrecy throughput and the average queue delay. Simulation results validate the analysis and demonstrate that the proposed buffer-aided scheme significantly improves the average secrecy throughput compared to the baseline one.

Green-Oriented Dynamic Resource-on-Demand Strategy for Multi-RAT Wireless Networks Powered by Heterogeneous Energy Sources

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high ynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter $V$ and the decrease of grid power consumption cost is at the speed of $1/V$ . Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

Analysis of Buffer-Aided Energy Harvesting Device-to-Device Communications With Complex Boundary Behavior

In this paper, we investigate the performance analysis of the buffer-aided energy harvesting device-to-device (D2D) communication system. A joint data and energy resource scheduling problem is proposed considering the data packet and energy packet Poisson arrival but interactive departure. Considering the coupling relationship between data and energy, the joint resource scheduling problem is modeled as a data and energy coupled queuing model. We raise the expressions of carrier-to-interference and noise-ratio (CINR) interval division, which contains the dynamic interferences from cellular users and the other D2D users. To compromise between data delay and energy consumption, we propose a novel adaptive power transfer (APT) strategy that allows more energy consumption to improve data delay. Studying user mobility and APT strategies will lead to complex boundary behavior of the system transition state matrix. By random geometry theory and quasi-birth and death method to obtain steady-state transition probability, the expressions of throughput, delay and packet drop rate for both data queue and energy queue are derived. Simulations are demonstrated to verify the accuracy of the theoretical derivation results.

Energy Efficient Buffer-Aided Transmission Scheme in Wireless Powered Cooperative NOMA Relay Network

In this paper, we consider a wireless powered cooperative non-orthogonal multiple access (NOMA) relay network, in which one source is supposed to send independent messages to two users with the assistance of one energy-constrained relay that harvests energy from the source. Firstly, we study the minimum power consumption at the source node to fulfill the least required transmission rates by two users in both time switching relaying (TSR) strategy and power splitting relaying (PSR) one. Secondly, when the relay is provisioned with data buffer and energy storage, the long-term average power consumption minimization problem is formulated to take into account of the data and energy queue causality, peak transmit power constraint, and transmission mode selection. By using Lyapunov optimization framework, a novel buffer-aided transmission scheme (BATS) is proposed to asymptotically approach the optimal solution. Our analysis shows that, the PSR outperforms the TSR in terms of the realized energy efficiency, and BATS can be utilized to further improve the energy efficiency. It is disclosed that, there is an inherent trade-off between the long-term power consumption and the average queuing delay. In addition, larger user rates or less power consumption can be realized if a larger delay can be tolerated.

Sustainability in Body Sensor Networks With Transmission Scheduling and Energy Harvesting

The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the Internet of Things (IoT) technology. In BSN, prolonging the service cycle of the network is a major challenge due to the limited battery capacity and energy supply for sensors. To this end, improving energy efficiency and harvesting energy are the keys for the network to maintain sustainability. In this paper, we propose a transmission scheduling and energy harvesting strategy to manage energy supply and consumption, and build several dynamic models to capture the stochastic processes in BSN. Besides, a system utility maximization problem is formulated. Since this problem is a multiobjective mixed-integer optimization problem (MMOP) which is difficult to solve directly, we provide a solution framework where MMOP is decomposed into several subproblems by the Lyapunov optimization method. Based on this framework, we propose an online energy sustainability optimization algorithm to solve these subproblems, such as the matching problem and convex optimization problem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the appropriate sizes of the data buffer and battery capacity are derived, which can give a guidance to determine the sizes of these components. Simulation results show the impact of the system parameter on the utility and data and energy queues, and verify that the proposed strategy and methods can maintain the sustainable operation of BSN effectively.

Parallel Offloading in Green and Sustainable Mobile Edge Computing for Delay-Constrained IoT System

Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in time-constraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms.

Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

Combined Data Rate and Energy Management in Harvesting Enabled Tactile IoT Sensing Devices

The Tactile Internet is envisioned to be characterized by ultra low latency, ultra reliability with high availability, and security. The next fifth generation is expected to underpin this emerging technology at the wireless edge. The stringent low latency and high reliability requirements of the Tactile Internet render the design more challenging in the presence of energy constraints on Internet of Things device in the underlying device-to-device network. In this paper, to deal with the stringent delay requirements of the tactile communication in the presence of energy constraints on devices and nodes, we pose the combined energy management and rate control problem as a queue control problem, where the objective is to control the energy and data queues to predefined reference levels. The intelligently controlled energy queues make the communication reliable and guarantee that nodes of the network are always alive and have some amount of energy close to predefined reference values for emergency and critical operations. In addition, tight control of the data queue ensures low queuing delays. We have considered both linear and nonlinear model of queues and have designed controllers based on model predictive control and nonlinear control theory. Convergence properties of the controllers are established analytically and the effectiveness of the proposed methods is also demonstrated through simulations.

Stochastic Computation Offloading and Scheduling Based on Mobile Edge Computing

To improve the quality of service (QoS) for mobile users (MUs) and the quality of experience (QoE) of mobile devices (MDs), mobile edge computing (MEC) is a promising approach that offloads a part of the computing task from MDs to nearby MUs. In this paper, we study computation offloading involving multiple users and multiple base stations (BSs), where the MD that is connected to the MU is wirelessly charged and BSs are available to be selected for computation offloading. We model the process of solving an optimal computation offloading policy into a Markov decision process (MDP), in which our goal is to maximize the long-term utility performance. Therefore, a computation offloading policy is obtained based on the energy queue state, the task queue state, and the channel states between the MUs and BSs. To address the problem of high dimensionality in the state space, we decompose the MDP into a series of single-agent MDPs with reduced state spaces and apply an online local learning algorithm to learn the optimal state value functions. Inspired by the structure of the utility function, we propose an algorithm based on combining Q-function reconstruction with the post-decision state. It is proved that the proposed algorithm can converge to an optimal computation offloading policy. The experimental results show that our algorithm achieves significant performance in computation offloading and schedule compared with the other three basic policies.

Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting.

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.

Cooperative Secure Communication in Two-Hop Buffer-Aided Networks

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.

Queue‐aware energy minimisation through sparse beamforming in C‐RAN

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l 0 -norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system.

Performance Analysis of Buffer-Aided Relaying System Based on Data and Energy Coupling Queuing Model for Cooperative Communication Networks

We focus on the performance analysis of the buffer-aided relaying system which allows data and energy packets to arrive independently and depart interactively. First, we profile the cooperative relaying system model as a data arrival and energy arrival coupling queuing model. Considering the influence of channel condition on the data departure rate, a new relay transmit protocol which permits exhausting more energy packet to send one data packet in the bad channel environment is proposed. Second, the joint data packet and energy packet handling problem is ascribed to a Coupled Processor Queuing Model which could achieve its steady state transition probability by Quasi-Birth and Death method. Third, the expressions of throughput, delay, and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the analytical results under different data arrival rate, energy arrival rate, and relaying strategy.

Utility-Optimal Resource Management and Allocation Algorithm for Energy Harvesting Cognitive Radio Sensor Networks

In this paper, we study resource management and allocation for energy harvesting cognitive radio sensor networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate the self-sustainability of the power-limited sensors. Furthermore, cognitive radio enables access to the underutilized licensed spectrum to mitigate the spectrum-scarcity problem in the unlicensed band. We develop an aggregate network utility optimization framework for the design of an online energy management, spectrum management, and resource allocation algorithm based on Lyapunov optimization. The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading. However, a priori knowledge of any of these processes statistics is not required. Based on the framework, we propose an online algorithm to achieve two major goals: first, balancing sensors’ energy consumption and energy harvesting while stabilizing their data and energy queues; second, optimizing the utilization of the licensed spectrum while maintaining a tolerable collision rate between the licensed subscriber and unlicensed sensors. The performance analysis shows that the proposed algorithm achieves a close-to-optimal aggregate network utility while guaranteeing bounded data and energy queue occupancy. The extensive simulations are conducted to verify the effectiveness of the proposed algorithm and the impact of various network parameters on its performance.

Diphase: Characterizing Packet Delay in Multi-Source Energy Harvesting Systems

We consider multi-source energy harvesting communication systems, where the energy harvested from two independent processes is used for the transmission of the data packets. The data packets arrive randomly and wait in a queue for accumulation of sufficient energy and for service completion of previously arrived packets. Thus, the data queue dynamics are influenced jointly by the energy arrival process, the data arrival process, and the data service process. This coupling between the data and energy queues makes an exact system analysis extremely hard, and has led researchers to resort to either computationally intensive numerical solutions or to make simplifying approximations. In this paper, we employ Diphase , a two phase queueing formulation, which decouples the wait stages for the energy arrival process and the service process, to derive closed-form expressions for the average packet delay and the probability of data packet loss due to buffer overflow. These expressions are shown to be exact when the service time is negligible, and robust for a relatively wide range of values of the average service time. We show that these expressions are useful in selecting system design parameters, which maximize the throughput while meeting the required quality of service constraints.

Throughput of a cooperative energy harvesting secondary user in cognitive radio networks

AbstractThis paper investigates the maximum throughput of a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU). The SU is assumed to be an energy harvesting node, which harvests energy packets from the environmental energy sources (e.g. solar, wind and acoustics) with a certain harvesting rate. The PU is equipped with a data buffer to store the randomly arrived data packets at its receiver. In addition to its own data buffer, the SU is equipped with another data buffer for storing the unsuccessfully decoded packets at the primary destination in a given time slot due to channel fading. Moreover, it has an energy buffer for storing the energy packets harvested from the environment. We propose a new cooperative cognitive relaying protocol that allows the SU to gain channel access when it relays a fraction of the undelivered primary packets. We consider an interference channel model [or a multipacket reception (MPR) channel model], where simultaneous transmissions can be decoded correctly at the corresponding destinations with certain decoding probabilities characterised by the complement of channel outages under interference. Our proposed protocol exploits the PU's data burstiness because of the random and sporadic packet arrival at its queue. Moreover, the proposed protocol takes advantage of the receivers' MPR capabilities. In addition, it efficiently expends the secondary energy packets under the objective of secondary throughput maximisation. Our numerical results show the benefits of cooperation, receivers' MPR capabilities and secondary energy queue arrival rate on the system's performance from a network layer standpoint. Copyright © 2016 John Wiley & Sons, Ltd.

MIMO Precoding for Networked Control Systems with Energy Harvesting Sensors

In this paper, we consider a MIMO networked control system with an energy harvesting sensor, where an unstable MIMO dynamic system is connected to a controller via a MIMO fading channel. We focus on the energy harvesting and MIMO precoding design at the sensor so as to stabilize the unstable MIMO dynamic plant subject to the energy availability constraint at the sensor. Using the Lyapunov optimization approach, we propose a closed-form dynamic energy harvesting and dynamic MIMO precoding solution, which has an event-driven control structure. Furthermore, the MIMO precoding solution is shown to have an eigenvalue water-filling structure, where the water level depends on the state estimation covariance, energy queue and the channel state, and the sea bed level depends on the state estimation covariance. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.

Achievable Throughput Analysis of Opportunistic Spectrum Access in Cognitive Radio Networks With Energy Harvesting

In this paper, we consider a cognitive radio (CR) network that consists of multiple secondary users (SUs), each of which is equipped with an energy harvesting capability. We consider the opportunistic use of the idle spectrum, unused by primary users (PUs), through a mechanism of two-step opportunistic spectrum access for the SUs, consisting of random channel sensing followed by random channel access. Unlike the majority of previous studies, which have considered CR networks with energy harvesting under a single user setting, the goal of this paper is to investigate the joint impact of sensing probability, access probability, and energy queue capacity on the maximum achievable throughput in a multiuser CR network incorporating energy harvesting. For two extreme cases, those where the energy queue capacity is either infinite or extremely small, we show that the maximum achievable throughput is not affected by the channel access probability if the channel sensing probability is chosen appropriately and the optimal sensing probability is derived as a function of network parameters such as the energy arrival rate, channel availability probability, and number of contending SUs.