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![Figure 4. Power management module block diagram following [56].](publication/366100529/figure/fig3/AS:11431281105738656@1670503771766/Power-management-module-block-diagram-following-56_Q320.jpg)
Energy efficiency presents a significant challenge to the reliability of Internet of Things (IoT) services. Wireless Sensor Networks (WSNs) present as an elementary technology of IoT, which has limited resources. Appropriate energy management techniques can perform increasing energy efficiency under variable workload conditions. Therefore, this pap...
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Despite significant progress in wireless sensor network's (WSN) applications in recent years, major problems persist, notably the efficiency of the battery power use for data exchange. In fact, sensor nodes are known to be energy-limited, they are powered by a relatively low capacity, non-rechargeable battery, and in most cases, the nodes are deplo...
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... DVFS is particularly valuable in applications where energy efficiency improvements are present on a larger scale. Maximizing longevity on limited energy resources is also one of the advantages of DVFS, for example, in Internet of Things (IoT) devices [3,4], wireless sensor nodes [5,6], in mobile devices [7], On-Chip temperature sensors in [8], wearables in [9]. Wearable technologies such as smartwatches, fitness trackers, and medical monitoring devices also rely on ultra-low-power properties and DVFS. ...
... The article can serve as a reference for the selection of an appropriate hash algorithm for various communication and transmission systems. Authors in [6] presented an article on energy management techniques for wireless sensor networks (WSNs) in IoT applications with limited resources. Specifically, the paper experimentally implements a hybrid energy management solution using DVFS and Duty-Cycling techniques to optimize operating conditions during data processing and reduce energy consumption of the transceiver. ...
Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%.
... Liu et al. [24] discussed only management issues related to the proposed hypothesis. Khriji et al. [25] described only said work for WSN. ...
As advancements in technology domain have promoted with times, so have the amount of data generated. The amount of this data is so volumetric, that mainstream techniques fail to process and analyse the data in an efficient way and hence the requirement of dedicated processing techniques. This gigantic amount and processing of this sort of data often leads to over exploitation of computing resources. This results in a lot of power consumption. Many attempts have been made in this domain at hardware as well as software level and promising results have been achieved, but one issue that has been over looked is the impact of Big Data’s Variety on processing. To address this problem it contributes towards a novel technique which reduces energy consumption on processing of big-data with variety meeting the deadline as Quality of Service. The technique works by reading the whole dataset in chunks and then removing stop words with appropriate algorithm to save processing time. In the evaluation part, a set of datasets of approximately 100 GB aggregated from different sources and evaluated them using benchmark applications. The final outcomes with efficient approach by improving energy consumption and meeting deadline constraints.
... There is also another approach to control hardware performance similar to NVIDIA Jetson manufacturer power modes-dynamic voltage and frequency switching (DVFS) [31][32][33]. As part of the tests, an experiment was also carried out to run the algorithms image segmentation in the HSV color space and line detection using the Hough algorithm with Scharr mask filtering, as was the case with previous research. ...
In this paper, the comparison of the power requirements during real-time processing of video sequences in embedded systems was investigated. During the experimental tests, four modules were tested: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX, and NVIDIA Jetson Orin AGX. The processing speed and energy consumption have been checked, depending on input frame size resolution and the particular power mode. Two vision algorithms for detecting lines located in airport areas were tested. The results show that the power modes of the NVIDIA Jetson modules have sufficient computing resources to effectively detect lines based on the camera image, such as Jetson Xavier in mode MAXN or Jetson Orin in mode MAXN, with a resolution of 1920 × 1080 pixels and a power consumption of about 19 W for 24 FPS for both algorithms tested.
... A major factor that has fostered the development of IoT on a large scale is the projected growth in distributed communications and computing technologies [4,5]. The application of these technologies has led to IoT technologies fulfilling some key requirements, including low energy consumption [6][7][8] and, possibly, the development of hardware nodes that feature small batteries (or that even operate without batteries). As a consequence, a number of approaches aimed at overcoming this problem were envisioned in the last few years, including technical solutions such as the concepts of energy harvesting (EH) and wireless power transfer (WPT) [9]. ...
This work presents a compact batteryless node architecture suitable with the backscattering communication (BackCom) approach. The key functional blocks are demonstrated at 5.8 GHz, making use of commercially available components involving a DC/DC step-up converter, a 3.3 V data generator, and an ASK backscattering modulator based on a single GaAs HEMT in a cold-FET configuration. The node integrates a patch antenna exhibiting a non-50 Ω optimal port impedance; the value is defined by means of a source pull-based optimization technique aimed at maximizing the DC/DC input current supplied by the RF to DC converter. This approach maximizes the node compactness, as well as the wireless power conversion efficiency. A prototype was optimized for the −5 dBm power level at the input of the RF to DC converter. Under this measurement condition, the experimental results showed a 63% increase in the harvesting current, rising from 145 to 237 μA, compared to an identical configuration that used a microstrip matching network coupled with a typical 50-Ω patch antenna. In terms of harvested power, the achieved improvement was from −13.2 dBm to −10.9 dBm. The conversion efficiency in an operative condition improved from 15% to more than 25%. In this condition, the node is capable of charging a 100 μF to the operative voltage in about 27 s, and operating the backscattering for 360 ms with a backscattering modulation frequency of about 10 MHz.
В роботі досліджено виклики, що виникають при розробці та застосуванні технологій віртуальної реальності (VR). Незважаючи на революційний потенціал VR у різних галузях, його повноцінній реалізації перешкоджають технічні, фінансові та етичні бар'єри. Існують технічні проблеми, пов'язані з розробкою високоякісного апаратного та програмного забезпечення, безперешкодною інтеграцією з існуючою інфраструктурою та потребою в значних обчислювальних ресурсах. Фінансові обмеження виникають через високу вартість виробництва та обслуговування, що обмежує доступність. З етичної точки зору, занурення у віртуальну реальність створює потенціал для звикання, антисоціальної поведінки та порушення конфіденційності даних. У статті також обговорюються компромісні рішення, складність, технологічні обмеження, спільна оптимізація апаратного та програмного забезпечення, перспективність, тестування та валідація, пов'язані з розробкою віртуальної реальності. Крім того, представлено огляд рішень цих задач, запропонованих у сучасній науковій літературі. Стаття підкреслює необхідність подальших досліджень, інновацій та вдосконалення для подолання цих перешкод і розкриття трансформаційного потенціалу технології віртуальної реальності.
Cloud providers place a high value on reducing energy consumption in cloud computing since it reduces operating costs and improves service sustainability. Cloud services are frequently replicated across providers to ensure high availability and dependability, which increases provider resource utilization and overhead. Finding the right balance between service replication and consolidation to lower energy usage and boost service uptime can be challenging for cloud resource management decision-makers. This chapter addresses this problem by presenting a ground-breaking technique known as “CRUZE,” which is based on cuckoo optimization and considers energy efficiency, dependability, and comprehensive resource management in cloud computing, encompassing cooling systems, servers, networks, and storage. Using cloud resources, effectively illuminating and executing a range of jobs, CRUZE has significantly reduced energy usage by 20.1% while improving dependability and CPU utilization by effectively illustrating and executing a variety of workloads on cloud resources that have been allocated.
Wireless sensor networks (WSNs) are widely utilized in various fields, including environmental monitoring, healthcare, and industrial automation. Optimizing energy consumption is one of the most challenging aspects of WSNs due to the limited capacity of the batteries that power the sensors. This chapter explores using Python libraries to optimize the energy consumption of WSNs. In WSNs, various nodes, including sensor, relay, and sink nodes, are introduced. How Python libraries such as NumPy, Pandas, Scikit-Learn, and Matplotlib can be used to optimize energy consumption is discussed. Techniques for optimizing energy consumption, such as data aggregation, duty cycling, and power management, are also presented. By employing these techniques and Python libraries, the energy consumption of WSNs can be drastically decreased, thereby extending battery life and boosting performance.
