Energy Harvesting Sensor Research Articles

Basic Performance Limits and Tradeoffs in Energy-Harvesting Sensor Nodes With Finite Data and Energy Storage

As many sensor network applications require deployment in remote and hard-to-reach areas, it is critical to ensure that such networks are capable of operating unattended for long durations. Consequently, the concept of using nodes with energy replenishment capabilities has been gaining popularity. However, new techniques and protocols must be developed to maximize the performance of sensor networks with energy replenishment. Here, we analyze limits of the performance of sensor nodes with limited energy, being replenished at a variable rate. We provide a simple localized energy management scheme that achieves a performance close to that with an unlimited energy source and at the same time keeps the probability of complete battery discharge low. Based on the insights developed, we address the problem of energy management for energy-replenishing nodes with finite battery and finite data buffer capacities. To this end, we give an energy management scheme that achieves the optimal utility asymptotically while keeping both the battery discharge and data loss probabilities low.

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.

Transmission Policies for Energy Harvesting Sensors with Time-Correlated Energy Supply

This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain (GOOD and BAD), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves near-optimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.

Agent-based decentralised coordination for sensor networks using the max-sum algorithm

In this paper, we consider the generic problem of how a network of physically distributed, computationally constrained devices can make coordinated decisions to maximise the effectiveness of the whole sensor network. In particular, we propose a new agent-based representation of the problem, based on the factor graph, and use state-of-the-art DCOP heuristics (i.e., DSA and the max-sum algorithm) to generate sub-optimal solutions. In more detail, we formally model a specific real-world problem where energy-harvesting sensors are deployed within an urban environment to detect vehicle movements. The sensors coordinate their sense/sleep schedules, maintaining energy neutral operation while maximising vehicle detection probability. We theoretically analyse the performance of the sensor network for various coordination strategies and show that by appropriately coordinating their schedules the sensors can achieve significantly improved system-wide performance, detecting up to 50 % of the events that a randomly coordinated network fails to detect. Finally, we deploy our coordination approach in a realistic simulation of our wide area surveillance problem, comparing its performance to a number of benchmarking coordination strategies. In this setting, our approach achieves up to a 57 % reduction in the number of missed vehicles (compared to an uncoordinated network). This performance is close to that achieved by a benchmark centralised algorithm (simulated annealing) and to a continuously powered network (which is an unreachable upper bound for any coordination approach).

Dynamically Reconfigurable Hardware With a Novel Scheduling Strategy in Energy-Harvesting Sensor Networks

Renewable energy, such as solar radiation, can be used to extend the lifetime of a wireless sensor network (WSN) node. Such systems fundamentally change the problem of power scheduling. Instead of maximizing system lifetime with a fixed amount of energy as in battery-powered systems, the purpose of scheduling becomes the prevention of energy depletion at any given time. On the other hand, partial dynamic reconfiguration is demonstrated as an efficient technique for intensive applications, such as video and encryption processing. Unlike software (SW)-based implementation, reconfigurable hardware (HW) requires time and energy for reconfiguration before enabling the application, which causes a trade-off between the SW and the reconfigurable HW. Therefore, when reconfigurable HW is applied in an energy-harvesting system, the problem lies in the manner by which to schedule the dynamic reconfiguration, such that the dynamically harvested energy is efficiently used. In this paper, a novel methodology is presented for the scheduling of the dynamic reconfiguration for a WSN node under energy-harvesting conditions based on statistical information on tasks and available energy. To evaluate our method, an HW-reconfigurable WSN node is prototyped, and related experimental data are collected. Our experiments demonstrate that by implementing our method, more than 50% of the energy costs can be saved with a 50% performance improvement.

Energy Efficiency of MIMO Cooperative Networks with Energy Harvesting Sensor Nodes

Energy Efficiency of MIMO Cooperative Networks with Energy Harvesting Sensor Nodes

Energy Efficiency of MIMO Cooperative Networks with Energy Harvesting Sensor Nodes

This paper addresses the maximizing network lifetime problem in wireless sensor networks (WSNs) taking into account the total Symbol Error rate (SER) at destination. Therefore, efficient power management is needed for extend network lifetime. Our approach consists to provide the optimal transmission power using the orthogonal multiple access channels between each sensor. In order to deeply study the properties of our approach, firstly, the simple case is considered; the information sensed by the source node passes by a single relay before reaching the destination node. Secondly, global case is studied; the information passes by several relays. We consider, in the previous both cases, that the batteries are nonrechargeable. Thirdly, we spread our work the case where the batteries are rechargeable with unlimited storage capacity. In all three cases, we suppose that Maximum Ratio Combining (MRC) is used as a detector, and Amplify and Forward (AF) as a relaying strategy. Simulation results show the viability of our approach which the network lifetime is extended of more than 70.72%when the batteries are non rechargeable and 100.51% when the batteries are rechargeable in comparison with other traditional method.

Optimal transmission policy for cooperative transmission with energy harvesting and battery operated sensor nodes

In this paper, we consider a scenario where one energy harvesting and one battery operated sensor cooperatively transmit a common message to a distant base station. The goal is to find the jointly optimal transmission (power allocation) policy which maximizes the total throughput for a given deadline. Initially, we address the case in which the storage capacity of the energy harvesting sensor is infinite. In this context, we identify the necessary conditions for such optimal transmission policy. On their basis, we first show that the problem is convex. Then we go one step beyond and prove that (i) the optimal power allocation for the energy harvesting sensor can be computed independently; and (ii) it unequivocally determines (and allows to compute) that of the battery operated one. Finally, we generalize the analysis for the case of finite storage capacity. Performance is assessed by means of computer simulations. Particular attention is paid to the impact of finite storage capacity and long-term battery degradation on the achievable throughput.

Control, Regulation and Command System of Hydronic Radiant Floors Heating by Wireless and Energy Harvesting Sensors and Actuators

This paper presents the development of an innovative control, regulation and command system for hydronic radiant floors, more flexible and efficient that guarantees a better thermal comfort to the user and simultaneously improves the energy efficiency of this type of heating system. The majority of the actual control of hydronic radiant floors is done by thermostats that measure the air temperature and control the actuators (pumps and valves) in order to maintain the room at the specified temperature. These systems requires the frequent adjustment of thermostats set-point in order to obtain thermal comfort as it depends on other factors than just the air temperature, such as, the air humidity, external environmental conditions, radiant temperature, among others. This paper presents a control, regulation and command solution that requires minimum user intervention, as the user only has to choose the desirable thermal comfort level. The control algorithm is based on the calculation of PMV (Predicted Mean Vote) index as defined on Thermal Comfort Standard ISO 7730. Another advantage of the proposed system is related to the wireless and energy harvesting sensors and actuators that provide much more flexibility to the system.

Frontiers in Applied Atomic Layer Deposition (ALD) Research

Atomic layer deposition (ALD) has been a key player in advancing the science and technology of nanomaterials synthesis and device fabrication. The monolayer (ML) control of growth rate obtained with ALD combined with its ability to self-limit growth reactions at the gas-substrate interface can be exploited in fundamentally new ways to produce novel composite nanomaterials or precisely tailored 3D nanostructures. Fueling the rapid popularity of ALD in nanotechnology research is the relative simplicity of the hardware and exciting new chemistries that allow researchers to deposit a host of new materials including pure metals, metal oxides, sulphides and nitrides and organic thin films with relative ease and superb accuracy. In this review article, we present four impact areas - microelectronics, energy harvesting and energy storage devices and sensors and photonic devices that have benefitted from such an approach. While many excellent review articles are available on the fundamental chemistry of ALD processes, we focus here on the applied science and engineering aspects of cutting edge ALD research

A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies

Wireless sensor networks (WSNs) require an extremely energy-efficient design. As sensor nodes have limited power sources, the problem of autonomy is crucial. Energy harvesting provides a potential solution to this problem. However, as current energy harvesters produce only a small amount of energy and their storage capacity is limited, efficient power management techniques must also be considered. In this article we address the problem of modeling and simulating energy harvesting WSN nodes with efficient power management policies. We propose furthermore a framework that permits to describe and simulate an energy harvesting sensor node by using a high level modeling approach based on power consumption and energy harvesting. The node architectural parameters as well as the on-line power management techniques will also be specified. Two new power management architectures will be introduced, taking into account energy-neutral and negative-energy conditions. Simulations results show that the throughput of a sensor node can be improved up to 50% when compared to a state of the art power management algorithm for solar harvesting WSN. The simulation framework is then used to find an efficient system sizing for a solar energy harvesting WSN node.

Device characterization and cross-layer protocol design for RF energy harvesting sensors

Energy harvesting from ambient radio frequency waves has the potential for realizing long lived wireless sensor networks, by reducing their dependence on the limited and irreplaceable on-board batteries. We propose two cross-layer approaches, called device-agnostic (DA) and device-specific (DS) protocols, for such networks composed of energy harvesting boards connected to off-the-shelf available sensors. These protocols determine the routing paths and the harvesting-transmission duty cycle at each hop under different conditions. The DA scheme relies purely on the local measurements on the harvesting capability of a node after the sensors are deployed, and is useful for single-flow networks. The DS scheme provides a joint hardware–software optimization by allowing the selection of the energy storing capacitor, apart from the route and duty cycle determination. Both schemes rely on a rich set of device-level experimental studies that help provide exact performance characteristics in practical scenarios, and results reveal significant performance improvement over other existing schemes.

Energy Management Policies for Energy-Neutral Source-Channel Coding

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a maximal average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and delay is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

Energy-aware lazy scheduling algorithm for energy-harvesting sensor nodes

The main problem in dealing with energy-harvesting (EH) sensor nodes is represented by the scarcity and non-stationarity of powering, due to the nature of the renewable energy sources. In this work, the authors address the problem of task scheduling in processors located in sensor nodes powered by EH sources. Some interesting solutions have appeared in the literature in the recent past, as the lazy scheduling algorithm (LSA), which represents a performing mix of scheduling effectiveness and ease of implementation. With the aim of achieving a more efficient and conservative management of energy resources, a new improved LSA solution is here proposed. Indeed, the automatic ability of foreseeing at run-time the task energy starving (i.e. the impossibility of finalizing a task due to the lack of power) is integrated within the original LSA approach. Moreover, some modifications have been applied in order to reduce the LSA computational complexity and thus maximizing the amount of energy available for task execution. The resulting technique, namely energy-aware LSA, has then been tested in comparison with the original one, and a relevant performance improvement has been registered both in terms of number of executable tasks and in terms of required computational burden.

Power Harvesting Capabilities of SHM Ultrasonic Sensors

The aim of this work is to show that classical Structural Health Monitoring ultrasonic sensors may provide some power harvesting capabilities from a wide variety of vibration sources. In other words, the authors developed an integrated piezoelectric energy harvesting sensor capable of operating a dual mode, that is, carrying out vibration power harvesting and Structural Health Monitoring. First, vibrations signals of an A380 aircraft recorded during different phases of flight are presented to show the need of a wideband piezoelectric energy harvester. Then, the voltage response of a piezoelectric power harvester bonded onto an aluminium cantilever plate and excited by an electromechanical shaker is measured. A finite element model of the energy harvester system is also presented. This model provides the voltage response of the harvester due to a mechanical excitation of the host structure and allows a better understanding of the energy harvesting process. In many cases, a good agreement with the experimental results is obtained. A power measurement also showed the ability of piezoelectric SHM sensors to harvest power over an extended frequency range present in spectra collected in aircrafts. This result could lead to numerous applications even though this kind of power harvester sensor has been initially designed to operate onboard aircrafts.

Dual-Stage Power Management Algorithms for Energy Harvesting Sensors

In this paper, we propose power management algorithms for maximizing the utility of energy harvesting sensors (EHS) that operate purely on the basis of energy harvested from the environment. In particular, we consider communication (i.e., transmission and reception) power management issues for EHS under an energy neutrality constraint. We also consider the fixed power loss effects of the circuitry, the battery inefficiency and its storage capacity, in the design of the algorithms. We propose a two-stage structure that exploits the inherent difference in the timescales at which the energy harvesting and channel fading processes evolve, without loss of optimality of the resulting solution. The outer stage schedules the power that can be used by an inner stage algorithm, so as to maximize the long term average utility and at the same time maintain energy neutrality. The inner stage optimizes the communication parameters to achieve maximum utility in the short-term, subject to the power constraint imposed by the outer stage. We optimize the algorithms for different transmission schemes such as the truncated channel inversion and retransmission strategies. The performance of the algorithms is illustrated via simulations using solar irradiance data, and for the case of Rayleigh fading channels. The results demonstrate the significant performance benefits that can be obtained using the proposed power management algorithms compared to the energy efficient (optimum when there is no storage) and the uniform power consumption (optimum when the battery has infinite capacity and is perfectly efficient) approaches.

Adaptive energy-harvest profiling to enhance depletion-safe operation and efficient task scheduling

Forecasting the expected energy harvest enables small-sized energy-harvesting sensor nodes to schedule tasks or adapt the radio duty cycle. This ability ensures depletion-safe and efficient operation. Most energy sources exhibit cyclic patterns of intensity, e.g., the Sun. These patterns show periods with unequal—low versus high and stable versus varying—energy production and heavily depend on a node's location as well as seasonal and environmental changes. Existing forecast algorithms do not exploit these patterns, but create and update forecasts at static and arbitrary points in time, the main knob being the number of updates per cycle. We present a method enabling sensor nodes to adapt to harvesting patterns at runtime. It is designed for seamlessly replacing the static scheme to improve the accuracy of a wide range of existing forecast algorithms. In our evaluation, we show that (i) the adaptive method traces the energy pattern in real-world deployments accurately, (ii) reacts to seasonal and environmental changes, (iii) increases forecast accuracy, and (iv) reduces the number of prediction updates. These achievements enhance depletion-safe operation and efficient task scheduling with fewer recalculations and adjustments of the duty cycle. They also facilitate the exchange of harvesting forecasts for collaborative node tasks, since less information has to be shared.

Delay-Bounded Data Forwarding in Low-Duty-Cycle Sensor Networks

Abstract In many sensor network applications, sink node needs to actively communicate with other sensor nodes in order to perform data forwarding operations. For those applications, there is usually adelay-bounded associated with them and require the messages sent to be received within a designated time bound. In energy harvesting sensor networks, limited energy from environment necessitates sensor nodes to operate at alow-duty-cycle. Sensor nodes work active briefly and stay asleep most of time. Such low-duty-cycle operation leads to communication delays in comparison with the always-active networks. In this paper, we address the data forwarding problems in an energy harvesting sensor network where energy efficiency and data freshness need to be balanced. To solve this problem, we propose autility-based delay bounded scheme for data forwarding MaxOpUtility-based scheme. MaxOpUtility scheme offers the ability to increase reliability through relay nodes selection as well as to ensure the timeliness. In add...

Perpetual and Fair Data Collection for Environmental Energy Harvesting Sensor Networks

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is nontrivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in the presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate and routing paths for each node such that no node will ever run out of energy. We propose a centralized algorithm and two distributed algorithms. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, the first distributed algorithm computes the optimal rate when the routing structure is a given tree, and the second distributed algorithm, although heuristic, jointly computes a routing structure and a high lexicographic rate assignment that is nearly optimum. We prove the optimality for the centralized and the first distributed algorithm, and use real test-bed experiments and extensive simulations to evaluate both of the distributed algorithms.

Remote corrosion monitoring of the RC structures using the electrochemical wireless energy-harvesting sensors and networks

Essentially, the corrosion process of the steel bar in reinforcing concrete structures is a series of electrochemical reactions. Therefore, the released energy during these reactions provides the opportunities to identify the corrosion status and power the wireless corrosion monitoring sensors. Furthermore, the recognition of the corrosion status has been realized with active monitoring techniques (AMTs) and passive monitoring techniques (PMTs). Additionally, the sensor mote platform that harvests the corrosion energy has been designed for corrosion monitoring, and then how to network these sensors to remotely access the corrosion data has been discussed. The preliminary experiment has been conducted to validate the micro corrosion energy.