Figure - available from: Peer-to-Peer Networking and Applications
This content is subject to copyright. Terms and conditions apply.
Source publication
In Body Area Networks, for energy harvesting-powered, managing the renewable energy to provide delay-sensitive services is of significant importance. In this paper, we propose an online algorithm to allocate resources, i.e., energy and channel, to maximize the user utility while guaranteeing the worst-case delay. To this end, we first formulate a u...
Similar publications
This paper describes a two-layer control and coordination framework for distributed energy resources. The lower layer is a real-time model predictive control (MPC) executed at 10 s resolution to achieve fine tuning of a given energy set-point. The upper layer is a slower MPC coordination mechanism based on distributed optimization, and solved with...
Citations
... Despite the great effort of the above works on the hybrid of WPC, BackCom and NOMA transmission, quite a few of them assume deterministic scenarios and obtain offline solutions within individual time block, which cannot adapt to the dynamics of the network environment. Recently, reinforcement learning and Lyapunov techniques have gained much interest in optimizing the longterm average performance of the RF-powered communication networks [25][26][27][28][29]. In [25], the operation mode of a hybrid HTT-BackCom ST is scheduled by a policy gradient method to maximize its longterm average throughput. ...
... In [28], a hierarchical DDPG-based algorithm is proposed to jointly optimize the beamforming and relay strategies of multiple wireless powered devices such that the longterm average throughput at the receiver is maximized. In [29], the authors consider an EH body area network consisting of multiple sensors. Lyapunov optimization techniques are applied to optimize data collecting rate, dropping rate and transmit power of each sensor such that the longterm average sum-throughput is maximized. ...
In a typical wireless powered cognitive radio network, secondary transmitters (STs) perform energy harvesting when the channel is busy and perform active transmission using the harvested energy when the channel is idle. In order to further enhance the throughput of the network, we enable time-division-multiple-access-based backscatter communication (BackCom) when the channel is busy and non-orthogonal-multiple-access (NOMA)-based active transmission when the channel is idle. We aim to maximize the longterm average sum-throughput of all the STs by allocating each ST’s BackCom time, energy harvesting time and transmit power for the NOMA-based active transmission. With consideration of limited battery capacity and time-varying channel, we formulate the problem as a Markov decision process. Both Q-learning and deep Q-learning (DQL) algorithms are proposed to solve the problem to obtain fully online policies. Simulation results show that the proposed DQL algorithm not only efficiently deals with the dynamics of the environment but also improves the average throughput up to 27.5% compared with Q-learning and up to 4 times compared with random policy.
... Although cognitive radio (CR) technology [12] has been proposed to improve the quality of data communication, and it does enhance the stability of data communication, it does not solve the problem completely. Data communication problems are related to real time and integrity [13]. Real-time performance is linked to data delay, and integrity involves data collection efficiency and transmission efficiency. ...
... Proof. Squaring both sides of Equation (13), and obtaining the system parameter B. The details are: ...
In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance.
... In other words, the network should ideally be self-sustainable. The recent innovation in energy harvesting technology has provided the opportunity to develop energy harvesting wireless sensor networks (EH-WSNs) [12]. With the introduction of EH-WSNs, the energy issues and the limited lifetime of the BP-WSNs can be resolved. ...
The lifetime of Wireless Sensor Networks (WSN) is a significant constraint since they are powered by non-rechargeable batteries with limited capacity. A promising solution to the energy issue is energy harvesting (EH). One of the most popular hierarchical routing protocols (RP) is the low-energy adaptive clustering hierarchy (LEACH). Most of the available RPs based on LEACH for EH-WSNs have employed the traditional cluster head (CH) selection based on taking turns, which is unsuitable. Moreover, most of them have not considered the remaining energy and harvested energy, nor investigated the weightages of these energies in selecting the CH. This work proposes the energy balanced and nodes aware (EBNA) routing protocol for EH-WSNs. It considers both the remaining energy and harvested energy along with the number of active nodes in selecting the CH which improves throughput. In addition, the weightages of the energies are investigated. EBNA is evaluated using the network simulator, GreenCastalia in OMNET. It uses the actual solar irradiance data with a resolution of 1 s. The performance is compared with energy-aware distributed clustering (EADC) and clustering routing algorithm of self-energized (CRAS) for EH-WSNs. The results show that EBNA outperforms EADC and CRAS in throughput by up to 58% and 113% and by number of CHs by up to 148% and 541%, respectively, during the high irradiance scenarios. In the low irradiance scenario, the improvement in throughput is up to 52% and 98%, and the number of CHs is up to 146% and 569%, as compared to EADC and CRAS, respectively.
... Also, this model optimizes system performance to overcome error rates, energy consumption problems, data accuracy. In [20], investigated an online algorithm for resource allocation, namely energy, and channels, to maximize user utilization while guaranteeing the worst-case delay. Lyapunov has investigated optimization techniques to improve energy consumption in steady-state motion and heat generation. ...
In recent years, Wireless Body Area Networks (WBANs) are attracting a lot of interest in the research community. Increased lifetime and treatment costs have affected cost-effective health management solutions. A location-based routing protocol is becoming significant for WBANs especially because of energy consumption and lifetime. Therefore, location-based routing requires the correct location information, which reduce energy consumption. We propose a hybrid routing algorithm based on genetic algorithm and fuzzy logic in the WBANs. The main proposed algorithm is reduced energy. Results of the simulation depicted that the fuzzy logic algorithm has a significant impact on reducing the error rate. In the next phase, when the genetic algorithm is combined with fuzzy logic, the results show that the network lifetime is increased. In other words, the proposed protocol using fuzzy logic and genetic algorithm for improved performance in error rate and management of energy and network lifetime.
... more important than system throughput. It remains a challenging research work to investigate efficient scheduling algorithms for data transmission with delay guarantees due to limited resources [23,24]. For example, the observation data about typhoon disaster with strict delay demand needs to be transmitted to the earth within a deadline. ...
In recent years, data scheduling in satellite networks has drawn much attention. The nodes in the satellite network change dynamically, and there is no continuous connectivity between nodes. Due to the intermittent connection between satellites, data transmission is based on a storage-carry-forward mode. In such a network, it will inevitably result in transmission delay. It is challenging to schedule a large amount of observation data with delay guarantees in resource-limited satellite networks. In this paper, we propose a downlink aware data scheduling algorithm based on a decomposition strategy of rolling horizon mechanism. The data flows with deadlines are allocated to ground stations orderly through feasible routes during each sliding horizon. Considering the issues of time-varying node centrality and downlink capacity, a link metric is provided to solve the problem of downlink contact conflict. Then a heuristic routing algorithm is exploited to design inter-satellite link contact. Further simulation results demonstrate the effectiveness of the proposed algorithms in network profit improvement while ensuring the transmission delay demand.
In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.
The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the 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 multi-objective 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.
