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Ragone plot for comparing the energy storage technologies and their power density versus energy density characteristics Tester (2005)
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An investigation by the Cooperative University of Colombia [1], related to the location of telecommunications antennas in a city in Colombia is analyzed. In the same way, important elements such as radio frequency (RF) waves are present, emitted by different devices located in different communes. Then there is the possibility of designing a system...
Citations
... Energy harvesting is an alternative solution to energy demand, which possesses great potential to achieve the self-powered functioning of devices along with a longer lifespan [2][3][4]. Energy harvesters are quite different from conventional chemical batteries, which work endlessly by continuously harvesting energy from outside surrounding [5][6][7][8]. Energy harvesters mitigate the critical issues associated with conventional batteries including shorter life, high cost of maintenance, bigger size, and environmental pollution, etc. [5,9,10]. To meet the energy requirements a variety of harvestable energy sources exist, such as flowing water, waste heating, electromagnetic waves, and vibration [11,12]. ...
... Energy harvesters are quite different from conventional chemical batteries, which work endlessly by continuously harvesting energy from outside surrounding [5][6][7][8]. Energy harvesters mitigate the critical issues associated with conventional batteries including shorter life, high cost of maintenance, bigger size, and environmental pollution, etc. [5,9,10]. To meet the energy requirements a variety of harvestable energy sources exist, such as flowing water, waste heating, electromagnetic waves, and vibration [11,12]. ...
The extreme consumption of non-renewable energy sources poses serious concerns of environment pollution and energy crisis across the globe, which stimulate the research on exploration of alternative energy technologies capable of harvesting available energy in the ambient environment. Mechanical energy is ubiquitously available in the ambient environment, which can be converted into electrical energy using piezoelectric energy harvesters (PEH) based on piezoelectric effect. PEH have evolved as a non-conventional, feasible and clean solution to meet energy requirement worldwide and played an important role in powering of several portable electronic devices, wireless sensor nodes, and medical implants. PEH enables self-powered functioning of devices along with a longer lifespan. The merits of this technology lies in its easy implementation, miniaturization, and high energy conversion efficiency. The utilization of waste mechanical energy available from the human body (e.g., natural movements of humans) in piezoelectric energy harvesters is one of the prime interests of researchers. The footwear equipped with piezoelectric material is one such novel innovation in the area of piezoelectric energy harvesting which utilizes the vibration generated during human body movements, thereby converting direct mechanical impacts into useful energy. This review article starts with providing the basic fundamental information on piezoelectric effect, piezoelectric materials and piezoelectric energy harvesting technology. The prime objective of this article is to provide the comprehensive review of recent developments made in designing footwear prototypes for piezoelectric energy harvesting and their emerging applications. Interestingly, this review also discusses the important patented technologies based on piezoelectric footwear energy harvesting. At last, this review discusses the merits and limitations of available footwear prototypes for piezoelectric energy-harvesting and provides the new directions for researchers in this innovative area of energy harvesting.
... Therefore, researchers used energy conservation mechanisms such as the sleeping mechanism, clustering, and improved the performance of protocols in order to reduce energy consumption [5]. On the other hand, collecting renewable energies from the environment and converting it into electrical energy to feed devices with energy, is considered a viable solution to the power shortage problem [6,7]. resolve this issue, since it allows for continuous feeding of the device components. ...
During charging-discharging operations, the batteries of the Internet of Things (IoT) devices are subject to a depletion that should be considered when predicting their lifetime. This paper proposes a new modeling for the IoT autonomous devices (AD) using Colored Generalized Stochastic Petri Nets (CGSPN). The ADs we consider are equipped with an energy harvesting system, and use a wireless link to connect with their neighbors. The CGSPN formulation models AD functionalities, and evaluates their impact on the battery lifetime by considering its state of health (SoH). The conducted analysis shows the ability of the proposed model to predict the ADs' lifetime which is very critical for medical applications.
... Wireless sensor networks (WSNs) suffer from the problem of limited energy stored in a small battery at the level of each sensor. [15].These batteries can save only a small amount of energy. The researchers proposed several solutions to face this limit. ...
In this paper, we introduce a Generalized Stochastic Petri Net (GSPN) to model the sensor nodes (SNs) interaction in solar Energy Harvesting Wireless Sensor Networks (EHWSNs). Our GSPN formalism models the energy stored in the SN battery by using the quantization principle and takes into account the fact that the EHWSNs deployment territory is susceptible to different sunshine levels. Moreover, each SN uses the channel polling schedule to conserve its energy. The conducted experimental analysis shows that the model can predict the configuration parameters of the SNs that ensure consistency between good performances and saving energy in long-lasting EHWSNs.KeywordsWireless sensor networkGSPN modellingSolar energy harvestingChannel polling scheduleSunshine levels
... The energy management unit is responsible for converting the energy retrieved from either the battery or the energy-harvesting circuit into a suitable energy level, which can be used to supply the electronics of the node. Using energy harvesting helps to reduce the dependency on the battery power by extending the lifetime of the node itself [78]. The communication unit contains the radio transceiver module used for wireless communication. ...
Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided.
... Using renewable energies (REs) and convert them to electrical power as an alternative resource to feed SNs is called Energy harvesting (EH). RE refers to an energy whose reservoir does not exhaust when it is used like thermal sources, photo-voltaic sunlight, bio-energy, hydro-energy, wind, wave, and many others [1]. ...
... Moreover, another reason that explains why SEH is the most attractive alternative for WSNs stems from the fact that it is relatively more predictable and from its higher power density in comparison to other renewable energy sources. Indeed, SEH provides an amount of energy equal to 100 mW for each cm 3 in direct sunlight [1]. The solar panel that carries the photovoltaic cells has to be put outdoor in a position that has direct sun exposure. ...
... In fact, when the solar panel is installed indoor, such as in an illuminated building, the light intensity is largely reduced and the solar energy source drops to almost 100µW/cm 3 . For embedded SNs to be deployed indoors such as homes, or in poorly exposed areas such as forests, using an SEH source may not be a suitable alternative [1], [6]. ...
... The architecture of a WSN typically consists of various scattered sensor nodes, a base station (or sink), external network and enduser. The need of a base station in WSNs is essentially due to the limited power and computing capacity of the sensors [2]. Figure 1 illustrates such a typical WSN architecture. ...
... Commonly, WSNs are deployed in regions that are difficult to access. Therefore, SNs must be energetically autonomous and they must not need to be renewed even if they work for a life-long operation [2]. Several possible solutions to address the problems stemming from these requirements have been suggested like: ...
... Further more, energy harvesting provides numerous benefits to the end user. EH technology can [2]: ...
... Depending on the type of their duty, SNs may not be easily accessible. A relatively modern solution consists of equipping SNs with harvesters to get energy from the environment (sun, wind, heat, pressure and others) and converting it to electrical energy [3]. Using ambient energy sources and transforming it to an electrical energy by SNs is called energy-harvesting [2], [4], [5] and wireless networks equipped with such SNs are called energy harvesting wireless sensor networks (EH-WSN) [6]- [9]. ...
Abstract—In this paper, we use Generalized Stochastic Petri
Nets (GSPN) formalism to model the communication between
an SN and its neighbors in wireless sensor networks (WSN).
This modelling considers several actual considerations such as
sensor vacations and retrial calls phenomenon. Furthermore,
given that sensor nodes (SN) consume almost all their energy in
the transmission process that varies according to the distance
of the neighbors, our model considers different levels of vicinity
for communicating neighbors. Our study proves that our
modelling, which provides a more realistic approach to
describe the actual behavior of the WSN, can identify the input
parameter scenario to have a network with a good compromise
between longevity and performance.
Index Terms—Wireless sensor network, GSPN, energy
harvesting, energy consumption, retrial call, sensor vacation.
... The main problem of these sensors is their short lifetime triggered by the limited capacity of their batteries [1]. A relatively new technique consists of using rechargeable batteries to get energy from the environment (sun, wind, heat, pressure, etc.) and converting it to electrical energy power [2]. Such kind of networks is called energy harvesting wireless sensor network (EH-WSN) [3]- [6]. ...
This paper proposes a new Generalised Stochastic
Petri Nets (GSPN) modeling for Energy Harvesting Wireless
Sensor Networks that takes into account buffer limitation and
message losses. By considering the relationship between a sensor
node and its neighbors, this model formulates actual circumstances such as the loss of messages caused by breakdowns. In
addition, the sensor nodes (SN) are supposed to be equipped
with limited size buffers. The proposed model allows to determine performance parameters and to extract some experimental
results to predict the behavior of the hole network before its
actual deployment.
Index Terms—wireless sensor network, GSPN, energy harvesting, messages losses, buffer limitation.
... Usually such comparisons are based on performance and costs. Liquid fossil fuels are relatively cheap and have an unparalleled energy density, compared with hydrogen and batteries [4]. ...
... 4 H + (aq) + O 2(g) + 4 e − → 2 H 2 O (g) E 0 = 1.229 V (4) Considering that ∆G 0 = nFE 0 (5) with n = 2 in the case of water, Equation (5) further demonstrates how the thermodynamic requirements of Equations (1)-(4) are linked together [7]. Due to its positive Gibbs energy variation, water photosplitting is a photosynthetic reaction. ...
It has been often reported that an efficient and green photocatalytic dissociation of water under irradiated semiconductors likely represents the most important goal for modern chemistry. Despite decades of intensive work on this topic, the efficiency of the water photolytic process under irradiated semiconductors is far from reaching significant photocatalytic efficiency. The use of a sacrificial agent as hole scavenger dramatically increases the hydrogen production rate and might represent the classic “kill two birds with one stone”: on the one hand, the production of hydrogen, then usable as energy carrier, on the other, the treatment of water for the abatement of pollutants used as sacrificial agents. Among metal oxides, TiO2 has a central role due to its versatility and inexpensiveness that allows an extended applicability in several scientific and technological fields. In this review we focus on the hydrogen production on irradiated TiO2 and its fundamental and environmental implications.
... In this context, wireless power transfer is a benign solution towards catering the energy requirements of charging devices. Wireless power transfer refers to a technique in which the power source does not need to use electric wires, however, transmits energy in a rechargeable device from a certain distance through electromagnetic induction principle or other related induction technology [18,19]. One of the most typical and most imperative representatives is Nikola Tesla. ...
Present research in the domain of wireless sensor network (WSN) has unearthed that energy restraint of sensor nodes (SNs) encumbers their perpetual performance. Of late, the encroachment in the vicinity of wireless power transfer (WPT) technology has achieved pervasive consideration from both industry and academia to cater the sensor nodes (SNs) letdown in the wireless rechargeable sensor network (WRSNs). The fundamental notion of wireless power transfer is to replenish the energy of sensor nodes using a single or multiple wireless charging devices (WCDs). Herein, we present a jointly optimization model to maximize the charging efficiency and routing restraint of the wireless charging device (WCD). At the outset, we intend an unswerving charging path algorithm to compute the charging path of the wireless charging device. Moreover, Particle swarm optimization (PSO) algorithm has designed with the aid of a virtual clustering technique during the routing process to equilibrate the network lifetime. Herein clustering algorithm, the enduring energy of the sensor nodes is an indispensable parameter meant for the assortment of cluster head (CH). Furthermore, compare the proposed approach to corroborate its pre-eminence over the benchmark algorithm in diverse scenarios. The simulation results divulge that the proposed work is enhanced concerning the network lifetime, charging performance and the enduring energy of the sensor nodes.
