Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

A queueing model of an energy harvesting sensor node with data buffering

Battery lifetime is a key impediment to long-lasting low power sensor nodes and networks thereof. Energy harvesting—conversion of ambient energy into electrical energy—has emerged as a viable alternative to battery power. Indeed, the harvested energy mitigates the dependency on battery power and can be used to transmit data. However, unfair data delivery delay and energy expenditure among sensors remain important issues for such networks. We study performance of sensor networks with mobile sinks: a mobile sink moves towards the transmission range of the different static sensor nodes to collect their data. We propose and analyse a Markovian queueing system to study the impact of uncertainty in energy harvesting, energy expenditure, data acquisition and data transmission. In particular, the energy harvesting sensor node is described by a system with two queues, one queue corresponding to the battery and the other to the data buffer. We illustrate our approach by numerical examples which show that energy harvesting correlation considerably affects performance measures like the mean data delay and the effective data collection rate.     

压电发电微电源国外研究进展

微加工技术极大地促进了各类传感器系统的微型化、集成化,使微机电系统(MEMS)功能越来越强,功耗、体积越来越小,而微能源部分却日益成为MEMS微型化设计的瓶颈。该文系统介绍了一种在MEMS应用中有极具发展潜力的能源供应方式——压电微能源。压电微能源可通过收集环境能量来发电,具有长寿命、高能量密度、与MEMS工艺兼容等优点,在微系统中具有广泛的应用前景。     

Energy harvesting and battery power based routing in wireless sensor networks

Wireless sensor networks (WSNs) are a collection of several small and inexpensive battery-powered nodes, commonly used to monitor regions of interests and to collect data from the environment. Several issues exist in routing data packets through WSN, but the most crucial problem is energy. There are a number of routing approaches in WSNs that address the issue of energy by the use of different energy-efficient methods. This paper, presents a brief summary of routing and related issues in WSNs. The most recent energy-efficient data routing approaches are reviewed and categorized based on their aims and methodologies. The traditional battery based energy sources for sensor nodes and the conventional energy harvesting mechanisms that are widely used to in energy replenishment in WSN are reviewed. Then a new emerging energy harvesting technology that uses piezoelectric nanogenerators to supply power to nanosensor; the type of sensors that cannot be charged by conventional energy harvesters are explained. The energy consumption reduction routing strategies in WSN are also discussed. Furthermore, comparisons of the variety of energy harvesting mechanisms and battery power routing protocols that have been discussed are presented, eliciting their advantages, disadvantages and their specific feature. Finally, a highlight of the challenges and future works in this research domain is presented.     

A generic model for optimizing single-hop transmission policy of replenishable sensors

Energy harvesting from the working environment has received increasing attention in the research of wireless sensor networks. Recent developments in this area can be used to replenish the power supply of sensors. However, power management is still a crucial issue for such networks due to the uncertainty of stochastic replenishment. In this paper, we propose a generic mathematical framework to characterize the policy for single hop transmission over a replenishable sensor network. Firstly, we introduce a Markov chain model to describe different modes of energy renewal. Then, we derive the optimal transmission policy for sensors with different energy budgets. Depending on the energy status of a sensor and the reward for successfully transmitting a message, we prove the existence of optimal thresholds that maximize the average reward rate. Our results are quite general since the reward values can be made application-specific for different design objectives. Compared with the unconditional transmit-all policy, which transmits every message as long as the energy storage is positive, the proposed optimal transmission policy is shown to achieve significant gains in the average reward rate.     

Wireless sensor networks with energy harvesting technologies: a game-theoretic approach to optimal energy management

Energy harvesting technologies are required for autonomous sensor networks for which using a power source from a fixed utility or manual battery recharging is infeasible. An energy harvesting device (e.g., a solar cell) converts different forms of environmental energy into electricity to be supplied to a sensor node. However, since it can produce energy only at a limited rate, energy saving mechanisms play an important role to reduce energy consumption in a sensor node. In this article we present an overview of the different energy harvesting technologies and the energy saving mechanisms for wireless sensor networks. The related research issues on energy efficiency for sensor networks using energy harvesting technology are then discussed. To this end, we present an optimal energy management policy for a solar-powered sensor node that uses a sleep and wakeup strategy for energy conservation. The problem of determining the sleep and wakeup probabilities is formulated as a bargaining game. The Nash equilibrium is used as the solution of this game.     

A battery-less BLE smart sensor for room occupancy tracking supplied by 2.45-GHz wireless power transfer

Wireless power transfer (WPT) has emerged as a solution for supplying smart sensors for long-term battery-less deployment. Because the amount of power harvested by the smart sensor is limited due to WPT path loss, the optimization objective is twofold: achieving ultra-low-power operation for the sensing task and improving the harvesting efficiency even at low incident power. In this paper, we focus on the use case of a Bluetooth LE-connected motion detection system supplied by 2.45-GHz RF power. The full system (RF energy harvester, power management, sensor transducer and interface, control, data processing and wireless transmission) is implemented using low-power off-the-shelf components. In the sensing sub-system, ultra-low-power operation is achieved by the duty-cycling of the sensor interface and by an event-driven scheme for communication. In the harvesting sub-system, the design of the matching network and rectifier, combined with maximum power point tracking (MPPT), is optimized for increasing the power harvesting efficiency (PHE) at low incident power. Measurements show a total reduction in the power consumption for the sensing sub-system by a factor 20. When using custom WPT waveform with high peak-to-average power ratio, the RF energy harvester is functional with an incident RF power starting from −20 dBm. The smart sensor is able to perform its motion-detection task with an incident power as low as −17.3 dBm.     

Simultaneous spectrum sensing,data transmission and Energy harvesting in multi‐channel cognitive sensor Networks with imperfect signal cancellation

In multichannel cognitive sensor networks, the sensor users which have limited energy budgets sense the spectrum to determine the activity of the primary user. If the spectrum is idle, the sensor user can access the licensed spectrum. However, during the spectrum sensing, no data transmits. For improving the network throughput and saving more energy consumption, we propose the simultaneous spectrum sensing and data transmission scheme where the sensor receiver decodes the received signal, and from the remaining signal, the status of the channel (idle/busy) is determined. We also consider that the sensor users are powered by a radio‐frequency (RF) energy harvester. In this case, energy harvesting, data transmission, and spectrum sensing are done simultaneously. On the other hand, we select the proper sensor users for spectrum sensing and energy harvesting. We also allocate the best channels for data transmission simultaneously so that the network throughput maximizes and the constraints on the energy consumption and the detection performance are satisfied for each band. We formulate the problem and model it as a coalition game in which sensors act as game players and decide to make coalitions. Each coalition selects one of the channels to sense and transmit data, while the necessary detection probability and false alarm probability and also the energy consumption constraints are satisfied. The utility function of a coalition is proposed based on the energy consumption, false alarm probability, detection probability, and the network throughput. This paper proposes an efficient algorithm to reach a Nash‐stable coalition structure. It is demonstrated that the proposed method maximizes the network throughput and reduces the energy consumption while it provides sufficient detection quality, in comparison to other existent methods.     

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

The Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low-energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB-based IoT system can outperform the conventional non-EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade-off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved.     

一体式无线无源振动传感器设计

现阶段无线传感器的供电主要依靠的依然是一次电池或可充电电池,无论哪种电池,其寿命都是有限且短暂的,大量的更换及废弃势必会对生态环境造成极大的污染。由此提出一种基于振动能量采集的自供电传感器,基于电磁感应对振动能量进行采集利用;低功耗单片机利用所收集的能量对发电单元所产生的信号进行采集,作为振动传感信号。实验结果证明,该无源传感器可在2 Hz^200 Hz的振动环境下稳定的工作,所采集的信号同样与实际振动环境一致。     

应用于能量收集的DC-DC电路研究综述

随着物联网技术(IoT)的发展,越来越多的IoT传感器被应用于环境检测、物流管理、生产制造和医疗保健等领域。IoT传感器的使用年限取决于内部电池的使用时间,而内部电池是不可拆卸的,因此一旦电池损坏或者电量耗尽,则需要更换新IoT传感器,这大大提高了成本。能量收集技术的发展为解决该难题提供了新方向,该技术将环境中的微弱能量收集起来,通过相关技术转换成电能,为IoT传感器供电,摆脱了其对电池的依赖。文章综述了能量收集系统的关键技术,比较了不同电路结构的优缺点,并且总结了最大功率追踪技术(MPPT),表明MPPT技术从一维MPPT技术发展到三维MPPT技术,现在已有更多的辅助技术来提高MPPT电路的跟踪精度。为了实现更广泛的应用,多输入多输出的转换器已然成为研究重点,文章综述了多能量源输入多输出电压的转换器结构,静态功耗和动态范围等性能指标成为关注的重点。     

Energy adaptive MAC for wireless sensor networks with RF energy transfer: algorithm,analysis, and implementation

Radio frequency energy transfer (RET) has been proposed as a promising solution to power sensor nodes in wireless sensor networks (WSNs). However, RET has a significant drawback to be directly applied to WSNs, i.e., unfairness in the achieved throughput among sensor nodes due to the difference of their energy harvesting rates that strongly depend on the distance between the energy emitting node and the energy harvesting nodes. The unfairness problem should be properly taken into account to mitigate the drawback caused from the features of RET. To resolve this issue, in this paper, we propose a medium access control (MAC) protocol for WSNs based on RET with two distinguishing features: energy adaptive (EA) duty cycle management that adaptively manages the duty cycle of sensor nodes according to their energy harvesting rates and EA contention algorithm that adaptively manages contentions among sensor nodes considering fairness. Through analysis and simulation, we show that our MAC protocol works well under the RET environment. Finally, to show the feasibility of WSNs with RET, we test our MAC protocol with a prototype system in a real environment.     

Alternating Magnetic Field-Enhanced Triboelectric Nanogenerator for Low-Speed Flow Energy Harvesting

Low-speed flow energy, such as breezes and rivers, which are abundant in smart agriculture and smart cities, faces significant challenges in efficient harvesting as an untapped sustainable energy source. This study proposes an alternating magnetic field-enhanced triboelectric nanogenerator (AMF-TENG) for low-speed flow energy harvesting, and demonstrates its feasibility through experimental results. AMF-TENG's minimum cut-in speed is 1 m s⁻¹, thereby greatly expanding its wind energy harvesting range. When the wind speed is 1–5 m s⁻¹, the open-circuit voltage (V_OC) is 20.9–179.3 V. The peak power is 0.68 mW at 5 m s⁻¹. In a durability test of 100 K cycles, the V_OC decreases from 188.4 to 174.2 V but remain at 92.5% of the initial value. furthermore, the AMF-TENG can harvest low-speed flow energy from the natural environment to power temperature and humidity sensors and wireless light intensity sensor in smart agriculture. This study provides a promising method for low-speed flow energy harvesting in distributed applications.     

Accuracy,latency, and energy cross‐optimization in wireless sensor networks through infection spreading

In this paper, cross‐optimization of accuracy, latency, and energy in wireless sensor networks (WSNs) through infection spreading is investigated. Our solution is based on a dual‐layer architecture for efficient data harvesting in a WSN, in which, the lower layer sensors are equipped with a novel adaptive data propagation method inspired by infection spreading and the upper layer consists of randomly roaming data harvesting agents. The proposed infection spreading mechanisms, namely random infection (RI) and linear infection (LI), are implemented at the lower layer. The entire sensor field is dynamically separated into several busy areas (BA) and quiet areas (QA). According to the BA or QA classification, the level of importance is defined, on which, the optimal number of infections for a particular observation is evaluated. Therefore, the accessed probability for observations with a relatively higher importance level is adaptively increased. The proposed mechanisms add further value to the data harvesting operation by compensating for its potential lack of coverage due to random mobility and tolerable delay, thus a relatively higher accuracy and latency requirements can be guaranteed for the optimization of energy consumption in a dynamically changing environment. Further, with the cost of processing simple location information, LI is proved to outperform RI. Copyright © 2010 John Wiley & Sons, Ltd.     

一种基于新型欠压闭锁电路的能量收集方法

目前自供电无线压电传感器网络已被广泛应用在智能家居及环境监测等领域.组成网络的每个压电传感器节点需要完成不同功耗的任务,如数据采集、存储等低功耗任务和数据无线传输的高功耗任务.针对低功耗和高功耗任务,该文设计了基于新型欠压闭锁电路的双模组能量收集电路,电路具有蓄能周期短和容量大的特点,可分别用于低功耗任务的低功耗级蓄能...     

面向压电能量收集的传感器自供电电源设计

随着物联网传感器网络的快速发展,微弱能量收集电路因其诸多优越性而备受关注。该文设计了一种基于压电能量收集技术的电路,其通过收集环境中的低频机械振动能量,经压电陶瓷(PZT)换能器产生交流电压,再经四倍压电路放大,并通过LTC3588-1电源管理电路整流变换,最终产生一个可供低功耗传感器工作的可切换的标准电压。实验结果表明,该电路可有效支持低功耗传感器正常工作。     

Adaptive algorithms for sensor activation in renewable energy based sensor systems

Upcoming sensor networks would be deployed with sensing devices with energy harvesting capabilities from renewable energy sources such as solar power. A key research question in such sensor systems is to maximize the asymptotic event detection probability achieved in the system, in the presence of energy constraints and uncertainties. This paper focuses on the design of adaptive algorithms for sensor activation in the presence of uncertainty in the event phenomena. Based upon the ideas from increase/decrease algorithms used in TCP congestion avoidance, we design an online and adaptive activation algorithm that varies the subsequent sleep interval according to additive increase and multiplicative decrease depending upon the sensor’s current energy level. In addition, the proposed algorithm does not depend on global system parameters, or on the degree of event correlations, and hence can easily be deployed in practical scenarios. We analyze the performance of proposed algorithm for a single sensor scenario using Markov chains, and show that the proposed algorithm achieves near-optimal performance. Through extensive simulations, we demonstrate that the proposed algorithm not only achieves near-optimal performance, but also exhibits more stability with respect to sensor’s energy level and sleep interval variations. We validate the applicability of our proposed algorithm in the presence of multiple sensors and multiple event processes through simulations.     

Cooperative and Reliable ARQ Protocols for Energy Harvesting Wireless Sensor Nodes

One class of wireless sensor networks makes use of sensor nodes that recharge their batteries by harvesting energy from the surrounding environment. Being continuously recharged, the battery does not need to be replaced regularly and the sensor node is maintenance-free. A key module in such sensor network solutions is the data link automatic repeat request (ARQ) protocol, which must be designed to reliably deliver sensor nodes data at the minimum energy cost. With this objective in mind, two ARQ protocol classes are compared. In one class, each sensor node operates individually. In the other, the concept of cooperative communications is adopted, whereby neighboring sensor nodes help each other during the retransmission process. It is shown that the use of cooperative ARQ protocols in energy harvesting sensor networks enables sensor nodes to balance their energy consumption to match their own battery recharge rate. In turn, a balanced energy consumption-to-recharge rate ratio has the potential to improve the network throughput. Both classes of ARQ protocols are analyzed and compared. Estimated throughput gains are discussed under various network scenarios.     

无线传感器网络中基于能量的成簇协议

无线传感器网络采用能量有效方式传输数据对于延长传感器网络寿命十分重要。LEACH是一种基于簇的协议,它采用本地簇头随机轮转机制将能量负载均匀分布到网络中的所有传感器节点,簇头节点将收集到数据进行融合后发送给基站。提出一种改进的方案,采用随机成簇算法让网络中传感器节点成簇,成簇的过程考虑传感器节点剩余能量和簇头与非簇头结点之间的距离。通过分析评价和仿真结果,说明新算法比LEACH更能有效利用能量且发送更多的数据。     

基于新型电源供电的无线传感网络节点设计

基于新型集成式压电能量收集模块LTC3588-1,结合压电自发电单元特性,进行了面向环境监测的无线传感网路节点的设计.实验结果表明,与改进后的电荷捕获电路相比,系统具有能量收集效率高、传输距离较远等优点,有效地解决了无线传感网络节点能源供电的问题,达到了节能环保的目的.