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Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks
A. M. Zungeru, Li-Minn Ang, S. R. S. Prabaharan, Kah Phooi Seng
TL;DR
Wireless sensor networks face battery-life and energy-consumption challenges, particularly when nodes are deployed in inaccessible settings. The chapter combines practical Powercast RF energy harvesting with IEEABR-based energy management and reports that IEEABR balances node power consumption and increases network lifetime. The approach is bounded by limited RF energy recovery in real environments and battery-performance constraints.
Problem
Wireless sensor networks require energy management because energy consumption affects system cost and lifetime, while battery charging is difficult for inaccessible deployments.
Method
The chapter implements Powercast RF energy harvesting and manages harvested and available energy using the Improved Energy Efficient Ant Based Routing algorithm.
Results
IEEABR balances wireless-sensor-network node power consumption and increases network lifetime in the reported experimental results.
Takeaways & Limitations
IEEABR provides the chapter’s energy-management technique for improving sensor-network lifetime through reduced node energy consumption.
Takeaways & Limitations
Only a small portion of transmitted RF energy can be scavenged in real environments, and batteries perform below their rated conditions.
Abstract
from arXiv · showhide
Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.
1.0 INTRODUCTION
The introduction frames battery dependence and energy consumption as major challenges for wireless sensor networks, especially when nodes are numerous or inaccessible. It proposes RF energy harvesting with Powercast harvesters and IEEABR-based management to optimize available power and extend network lifetime.
- Motivation: Battery charging is difficult for wireless sensor nodes deployed across wide areas or inaccessible environments.The challenge increases when many devices are distributed over large areas.
- Motivation: RF energy harvesting is presented as a technique for addressing battery-related challenges in wireless sensor networks.The chapter focuses specifically on radio-frequency electromagnetic energy harvesting.
- RF harvesting context: Only a small portion of transmitted RF energy can be scavenged in real environments, while the remainder is dissipated or absorbed.The passage contrasts transmitted power of up to 30W at 10GHz with limited recoverable energy.
- Proposed approach: Ant-based routing uses pheromone-inspired path selection to optimize wireless-sensor-network routing and provide maximum network lifetime.The introduction positions IEEABR within prior ant-based energy-management approaches.
- Proposed approach: The chapter proposes practical Powercast RF harvesting and IEEABR-based management of harvested and available sensor-network energy.The stated objective is efficient powering with or without batteries while maintaining network lifetime without reducing performance.
IN WSNs
This section reviews energy-harvesting options for wireless sensor networks and notes that small-scale harvesting has received less attention than large-scale renewable-energy research. It compares available sources and summarizes prior RF-harvesting work.
- Research context: Small-scale energy harvesting has received less attention than large-scale energy research, despite existing work on energy scavenging for wireless sensor networks.The section identifies this imbalance before reviewing selected harvesting techniques.
- Prior RF harvesting: Prior RF-harvesting work includes a passive RF-DC circuit operating at 906MHz and achieving up to 5.5µW.The cited system is described as efficient far-field energy harvesting.
- Prior RF harvesting: Related studies consider limited available RF energy and its use for powering sensor networks, including broadband and narrowband recovery systems.The reviewed systems focus on recovering small amounts of ambient RF energy.
- Energy-harvesting sources: Wireless sensor networks can harvest thermal, radiant, mechanical, and other forms of ambient energy.Examples include solar, electromagnetic, thermal, wind, kinetic, biomedical, piezoelectric, and electrostatic sources.
Comparison of Energy Harvesting Sources for WSNs (Cont’d)
The section compares sensor-node consumption and RF-harvesting conditions, then describes conversion, storage, intentional energy sources, and the Powercast transmitter used in the chapter. Its reported Powercast measurements decrease sharply with distance.
- RF power availability: Powercast P2110 availability is 3.5mW before conversion and 1.93mW after conversion at 0.6m, falling to 1µW at 11m.The receiver operates at 915MHz.
- RF harvesting operation: RF harvesting converts electromagnetic radiation received by an antenna into usable DC voltage for devices such as RFID tags and sensor nodes.The described receiving circuits can convert propagated waves to low-voltage DC power at distances up to 100 meters.
- Storage and conversion: A capacitor can buffer harvested energy and provide a constant required voltage when incoming energy is insufficient.The conversion circuitry can use transistor-based diode and capacitor arrangements to increase output voltage after charging.
- Energy-source categories: Ambient energy availability is categorized as intentional, anticipated, or unknown, and this work relies on intentional harvesting with Powercast.Intentional harvesting uses an active source that supplies energy when needed.
- Powercast implementation: The TX91501 transmits 3 watts EIRP at a 915MHz center frequency using vertically polarized DSSS and ASK signaling.It supplies power and data to Powercast P2110 or P1110 receivers.
3.3 The Powercast Power Harvester Receiver
The section describes Powercast receivers for converting RF energy into usable DC power, storing it, and supporting battery-free or low-power devices. It also outlines Friis-based calculations for estimating received power and power density.
- Powercast receivers harvest directed or ambient RF energy and convert it into DC power for remotely recharging batteries or battery-free devices.
- The receivers are presented as two modules, P1110 and P2110, with similarities and differences in their application areas.
- The P2110 converts RF energy to DC, stores it in a capacitor, boosts voltage to a set output level, and supports power management for micropower devices.
- The study used RF power meters and a simplified Friis equation to estimate received power and power available for use.
- The Friis transmission equation relates received power to transmitted power, antenna gains, wavelength, and antenna separation.
- Power density decreases with the square of distance because radiated power spreads over the surface area of a sphere.
3.5 Energy Storage
The section presents batteries and rechargeable storage as energy reservoirs for sensor nodes, while emphasizing capacity losses, deployment constraints, and energy-aware management in wireless sensor networks. It also introduces IEEABR as a routing approach that considers node power and path consumption.
- Batteries store harvested energy and regulate its supply to sensor nodes, with rechargeable batteries used widely in energy-harvesting systems.
- Large-scale deployments face a serious cost constraint because they may require hundreds or thousands of batteries.
- A 1000mAh battery ideally provides 1000mA for 1 hour at a 1C discharge rate, while capacity changes with discharge rate.
- The Peukert number describes battery performance under continuous heavy current: values near 1 indicate better performance, while higher values indicate greater capacity loss.
- Wireless sensor networks have limited energy, small nodes, difficult battery replacement, and vulnerability to fragmentation when nodes fail.
- IEEABR selects routes using available node power and path energy consumption, seeking optimal and multiple candidate paths while preserving connectivity and network lifetime.
4.1 Algorithm Operations
IEEABR routes divided event data through ant agents, selecting neighbors using pheromone and residual-energy information. Forward and backward ants update routing state until delivery to the sink and source.
- Data preparation: Raw event data is divided into M data parts, with M also representing the number of ant agents in the routing task.Each part carries source, event, time, and event-data information.
- Packet structure: Each packet includes code identification, destination node identification, packet or ant number, sequence number, visited-node count, and a data part.The first four fields form the data header.
- Data delivery: After all data packets arrive, the base combines their parts to reconstruct the raw data.Neighbor nodes disseminate information toward the sink as repeaters.
- Forward routing: A participating node chooses the next destination using the routing decision rule, with pheromone level followed by neighboring-node energy as key factors.The selected node’s NID is updated before broadcasting.
- Forward routing: Nodes hearing a broadcast discard it after checking the NID when the packet is not addressed to them.The packet continues through repeated next-node selection until reaching the sink.
- Backward routing: The visited-node count increments after confirming that the sequence number is absent from the chosen node’s tabu routing table.A backward ant then performs the reverse operation and returns acknowledgements toward the source.
5.0 EXPERIMENT AND SIMULATION RESULTS
The study evaluates RF harvesting and energy management through circuit measurements and simulations. The experiments vary harvester distance and examine energy use and IEEABR-based management.
- Experimental scope: The experiments measure circuit parameters and assess how the RF power source influences system behavior.The work combines practical measurements with simulation-based analysis.
- Evaluation design: The study analyzes both harvested-energy behavior and network-management performance across practical and simulated settings.The two settings support evaluation of the RF harvesting setup and routing-based energy management.
- Experimental scope: Simulation results examine harvester distance, energy usage, and energy management using the proposed IEEABR protocol.The analysis also documents the harvesting setup and simulation environment.
5.1 Experimental Results
The experimental and simulation results examine harvested power under varying hardware and distances, then compare AODV, EEABR, and IEEABR using network-energy metrics. IEEABR reports better average and minimum node energy in the stated simulations.
- RF harvesting measurements: Powercast Calculator experiments vary transmitter distance, receiver, and antenna using P2110 at 1.2V-915 MHz and P1110 at 4.0V-915 MHz.Both configurations use a 1150 mAh battery capacity.
- RF harvesting measurements: The experiments record packet-reception behavior over time and compare packet reception over distance for different harvesters and antennas.The comparisons are presented in Figures 5 and 6 and the associated harvesting tables.
- Simulation design: The NS-2 simulations use a static wireless sensor network with randomly deployed nodes monitoring a static environment.Nodes transmit sensed data to a sink, and simulations run for 60 minutes.
- Simulation design: Average and minimum remaining energy are the reported metrics for comparing AODV, EEABR, and IEEABR.Energy is recorded for all nodes at the end of each simulation.
- Simulation results: IEEABR achieves better average and minimum node energy than the alternatives, while AODV performs worst in all reported cases.The comparison is shown in Figure 8 for the studied parameters.
- Simulation results: IEEABR’s average-energy difference relative to EEABR ranges from 2% to 8%, while the corresponding AODV range is 15% to 22%.The passage also applies the comparison to minimum node energy.
5.3 Real-Time Implementation of RF Powercast Energy Harvester
The real-time implementation combines Powercast RF harvesting with Waspmote energy management and measures packet timing, battery use, and recharge behavior. IEEABR uses residual node energy in routing and is reported to improve network lifetime without performance degradation.
- Real-time setup: The real-time setup uses Powercast harvesters, a Waspmote, a gateway, and a sink while varying harvester-transmitter distance.Measurements include available power and the time between packet deliveries.
- Battery performance: The node’s total current draw is 264.5mA for 1 hr, and the battery can power it for 4.35 hrs without recharging.The reported supply-energy share is 23%.
- Battery performance: At 15 feet, fully recharging an empty battery takes 429.4 hrs, while replenishing the 264.5mA drawn current takes 91.9 hrs.The passage states that constant harvesting keeps total battery energy from decreasing.
- Harvested energy: The harvested-energy measurements are reported in Table 4 and Figure 5 for the tested receivers and antennas.The hardware includes dipole and patch antenna configurations.
- Energy-management conclusions: The proposed routing method uses uniform routing-table probabilities and gives special consideration to neighboring sink nodes to reduce search time and node energy consumption.The authors identify direct battery charging, energy recovery, sink selection, and MPPT as future work.
- Energy-management conclusions: IEEABR considers residual node energy after each simulation period and is reported to balance node consumption and increase network lifetime without performance degradation.The stated focus is energy management and wireless-sensor-network lifetime.
1. Adamu Murtala Zungeru
The section identifies an individual associated with the University of Nottingham Malaysia campus and lists an affiliation in electrical and electronic engineering.
- The listed affiliation is the School of Electrical and Electronic Engineering within the Faculty of Engineering.
- The institutional affiliation is the University of Nottingham Malaysia campus.
- The address is Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
3. Professor. Dr. SRS Prabaharan
The section lists academic affiliations connected with the University of Nottingham Malaysia campus, including professorial and associate-professorial roles.
- One listed affiliation is a Professor in the School of Electrical and Electronic Engineering, Faculty of Engineering.
- The affiliations identify the University of Nottingham Malaysia campus.
- The section includes the numbered entry “4. Dr. Kah Phooi Seng.”
- Another listed affiliation is an Associate Professor in the School of Electrical and Electronic Engineering, Faculty of Engineering.