Solar Harvested energy prediction algorithm for wireless sensors
10 Jul 2012-pp 178-181
TL;DR: This paper is presenting efficient algorithm for solar energy prediction based on additive decomposition (SEPAD) model, which is individually considering both seasonal and daily trends along with Sun's diurnal cycle.
Abstract: Recently, wireless sensing nodes are being integrated with ambient energy harvesting capability to overcome limited battery power budget constraint and extending effective operational time of sensor network. Solar panels are more frequently used to collect light energy for wireless sensing node. In order to efficiently utilize solar harvested energy in design, precise solar harvested energy prediction is a challenging task due to irregularity in solar energy patterens because of continually changing weather conditions. In this paper, we are presenting efficient algorithm for solar energy prediction based on additive decomposition (SEPAD) model. In this model, we are individually considering both seasonal and daily trends along with Sun's diurnal cycle. The performance of this algorithm is compared with existing solar energy prediction approaches and results show that our algorithm performance is better than existing approaches.
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TL;DR: This work presents energy-harvesting and sub-systems for IoT networks, and highlights future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.
Abstract: An increasing number of objects (things) are being connected to the Internet as they become more advanced, compact, and affordable. These Internet-connected objects are paving the way toward the emergence of the Internet of Things (IoT). The IoT is a distributed network of low-powered, low-storage, light-weight and scalable nodes. Most low-power IoT sensors and embedded IoT devices are powered by batteries with limited lifespans, which need replacement every few years. This replacement process is costly, so smart energy management could play a vital role in enabling energy efficiency for communicating IoT objects. For example, harvesting of energy from naturally or artificially available environmental resources removes IoT networks’ dependence on batteries. Scavenging unlimited amounts of energy in contrast to battery-powered solutions makes IoT systems long-lasting. Thus, here we present energy-harvesting and sub-systems for IoT networks. After surveying the options for harvesting systems, distribution approaches, storage devices and control units, we highlight future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.
98 citations
TL;DR: The review work presented is categorized into energy management and renewable energy harvesting techniques, which discusses various methods to save energy consumption of the energy harvesting sensor networks and the different energy harvesting mechanisms, especially their protocol design strategies for maximizing energy harvesting.
94 citations
TL;DR: In this article, the authors review recent advances in energy harvesting techniques for IoT and discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.
Abstract: The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.
73 citations
TL;DR: A comprehensive survey on task scheduling algorithms for the emerging class of energy harvesting-based sensors (i.e., energy positive sensors) to achieve the sustainable operation of IoT and outlines future research directions toward the implementation of autonomous and self-powered IoT.
Abstract: The Internet of Things (IoT) has important applications in our daily lives, including health and fitness tracking, environmental monitoring, and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resulting from their finite energy availability. A promising solution is to harvest energy from environmental sources, such as solar, kinetic, thermal, and radio-frequency (RF) waves, for perpetual and continuous operation of IoT sensor nodes. In addition to energy generation, recently energy harvesters have been used for context detection, eliminating the need for conventional activity sensors (e.g., accelerometers), saving space, cost, and energy consumption. Using energy harvesters for simultaneous sensing and energy harvesting enables energy positive sensing —an important and emerging class of sensors, which harvest more energy than required for context detection and the additional energy can be used to power other components of the system. Although simultaneous sensing and energy harvesting is an important step forward toward autonomous self-powered sensor nodes, the energy and information availability can be still intermittent, unpredictable, and temporally misaligned with various computational tasks on the sensor node. This article provides a comprehensive survey on task scheduling algorithms for the emerging class of energy harvesting-based sensors (i.e., energy positive sensors) to achieve the sustainable operation of IoT. We discuss inherent differences between conventional sensing and energy positive sensing and provide an extensive critical analysis for devising revised task scheduling algorithms incorporating this new class of sensors. Finally, we outline future research directions toward the implementation of autonomous and self-powered IoT.
49 citations
TL;DR: Novel statistical models of the harvested energy from renewable energy sources considering harvest-store-consume (HSC) architecture are developed and interesting insights are revealed related to the optimal flight time and transmit power of the UAV as a function of the harvesting energy.
Abstract: We develop novel statistical models of the harvested energy from renewable energy sources considering harvest-store-consume (HSC) architecture. We consider three renewable energy harvesting scenarios, i.e., (i) harvesting from the solar power, (ii) harvesting from the wind power, and (iii) hybrid solar and wind power. In this context, we first derive the closed-form expressions for the density functions and moments of the harvested power solar and wind power. Then, we calculate the probability of energy outage at UAVs and signal-to-noise ratio (SNR) outage at ground cellular users. The energy outage occurs when the UAV is unable to support the flight consumption and transmission consumption from its battery power and the harvested power. Due to the intricate distribution of the hybrid solar and wind power, we derive novel closed-form expressions for the moment generating function (MGF) of the harvested solar power and wind power. Then, we apply Gil-Pelaez inversion to evaluate the energy outage at the UAV and SNR outage at the ground users. In addition, we formulate the SNR outage minimization problem and obtain closed-form solutions for the transmit power and flight time of the UAV. Furthermore, we demonstrate the application of moments in computing novel metrics such as the probability of charging the UAV battery within the flight time, average UAV battery charging time, probability of energy outage at UAVs, and the probability of eventual energy outage (i.e., the probability of energy outage in a finite duration of time) at UAVs. Numerical results validate the analytical expressions and reveal interesting insights related to the optimal flight time and transmit power of the UAV as a function of the harvested energy.
48 citations
Cites background from "Solar Harvested energy prediction a..."
...A plethora of research works consider solar energy harvesting in wireless sensor networks [27], [28], [29]; however, to the best of our knowledge, there are no concrete statistical models for energy harvested from solar or wind sources and their applications to...
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References
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28 Sep 2002
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.
4,623 citations
TL;DR: The goal of this paper is not to suggest that the conversion of vibrations is the best or most versatile method to scavenge ambient power, but to study its potential as a viable power source for applications where vibrations are present.
2,727 citations
"Solar Harvested energy prediction a..." refers background in this paper
...Solar energy harvesting is the most attractive and suitable technology for wireless sensors [5] because of increased energy density and low cost of solar panels as compared to existing harvesting technologies such as vibration [6],wind [7]and thermal [8]....
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TL;DR: In this paper, the authors have developed abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues.
Abstract: Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.
1,535 citations
"Solar Harvested energy prediction a..." refers methods in this paper
...Exponential weighted moving average (EWMA) filter is used in [11] to predict solar energy ....
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Journal Article•
TL;DR: In this article, the authors describe key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and present the design, implementation, and performance evaluation of Heliomote, their prototype that addresses several of these issues.
Abstract: Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery nonidealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.
1,063 citations
01 Jan 2006
TL;DR: Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs.
Abstract: AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs
504 citations
"Solar Harvested energy prediction a..." refers background in this paper
...Solar energy harvesting is the most attractive and suitable technology for wireless sensors [5] because of increased energy density and low cost of solar panels as compared to existing harvesting technologies such as vibration [6],wind [7]and thermal [8]....
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