RF harvesting circuits have been demonstrated for more than 50 years, but only a few have been able to harvest energy from freely available ambient (i.e., non-dedicated) RF sources. In this paper, our objectives were to realize harvester operation at typical ambient RF power levels found within urban and semi-urban environments. To explore the potential for ambient RF energy harvesting, a city-wide RF spectral survey was undertaken from outside all of the 270 London Underground stations at street level. Using the results from this survey, four harvesters (comprising antenna, impedance-matching network, rectifier, maximum power point tracking interface, and storage element) were designed to cover four frequency bands from the largest RF contributors (DTV, GSM900, GSM1800, and 3G) within the ultrahigh frequency (0.3-3 GHz) part of the frequency spectrum. Prototypes were designed and fabricated for each band. The overall end-to-end efficiency of the prototypes using realistic input RF power sources is measured; with our first GSM900 prototype giving an efficiency of 40%. Approximately half of the London Underground stations were found to be suitable locations for harvesting ambient RF energy using our four prototypes. Furthermore, multiband array architectures were designed and fabricated to provide a broader freedom of operation. Finally, an output dc power density comparison was made between all the ambient RF energy harvesters, as well as alternative energy harvesting technologies, and for the first time, it is shown that ambient RF harvesting can be competitive with the other technologies.

Ambient RF Energy Harvesting in Urban and Semi-Urban Environments

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.

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Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

A platform architecture combining energy from solar, thermal, and vibration sources is presented. A dual-path architecture for energy harvesting is employed that has a peak efficiency improvement of 11%-13% over the traditional two-stage approach. The system implemented consists of a reconfigurable multi-input, multi-output switch matrix that combines energy from three distinct energy-harvesting sources-photovoltaic, thermoelectric, and piezoelectric. The system can handle input voltages from 20 mV to 5 V and is capable of extracting maximum power from individual harvesters all at the same time utilizing a single inductor. A proposed time-based power monitor is used for achieving maximum power point tracking for the photovoltaic harvester. This has a peak tracking efficiency of 96%. The peak efficiencies achieved with inductor sharing are 83%, 58%, and 79% for photovoltaic boost, thermoelectric boost, and piezoelectric buck-boost converters, respectively. The switch matrix and the control circuits are implemented on a 0.35-μm CMOS process.

Platform Architecture for Solar, Thermal, and Vibration Energy Combining With MPPT and Single Inductor

A 0.35µm CMOS energy processor with multiple inputs from solar, thermal and vibration energy sources is presented. Dual-path architecture for energy harvesting is proposed that has up to 13% higher conversion efficiency compared to the conventional two stage storage-regulation architecture. To minimize the cost and form factor, a single inductor has been time shared for all converters. A novel low power maximum power point tracking (MPPT) scheme with 95% tracking efficiency is also introduced.

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Platform architecture for solar, thermal and vibration energy combining with MPPT and single inductor

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Wireless sensor nodes (WSNs) are employed today in many different application areas, ranging from health and lifestyle to automotive, smart building, predictive maintenance (e.g., of machines and infrastructure), and active RFID tags. Currently these devices have limited lifetimes, however, since they require significant operating power. The typical power requirements of some current portable devices, including a body sensor network, are shown in Figure 1.

Rudd J.M. Vullers

Papers

Energy Harvesting for Autonomous Wireless Sensor Networks