جستجوی مقالات مرتبط با کلیدواژه « energy consumption » در نشریات گروه « برق »

Earthquakes are usually associated with damage. Therefore, any action to predict it is necessary. In data monitoring systems, being real-time and accuracy of data play a key role. In this article, a monitoring system based on the Internet of Things was proposed for the messaging of seismic data. In the first solution, the lightweight protocol Message Queuing Telemetry Transfer (MQTT) was chosen for messaging. In the second solution, redundancy was applied in the sensor layer using the gray wolf algorithm, and in the third solution، redundancy was applied in the controller layer. The simulation results showed that the redundancy in the sensor and controller layer saved energy consumption by more than thirty percent. Also, the average end-to-end delay was significantly reduced in the second and third solutions. Finally، in the first solution, the rate of successful package delivery for different numbers of packages was a constant value of 78.98%. But by applying redundancy in the sensor and controller, the package delivery rate increased to over 92%, which can be the result of increasing the number of sensors and controllers and their proper placement.

By performing cooperative spectrum sensing in a cognitive radio network, although the network throughput increases with the increase in the number of secondary users, but at the same time, it also causes an increase in energy consumption. This makes it necessary to provide a system that is able to create a tradeoff between throughput and energy consumption. In contrast to the conventional method of spectrum sensing based on one detection threshold, spectrum sensing with double thresholds avoids reporting unreliable data to the fusion center, thus potentially leading to greater energy saving. In this paper, a double threshold spectrum sensing cognitive radio network with a non-ideal reporting channel is optimized. The values of the threshold and the sensing time are jointly optimized to maximize the throughput of the network, provided that the network energy consumption and the amount of interference with the primary users are limited. The optimization problem is formulated and a numerical method is presented to solve it. The simulation results show a flexible system that can simultaneously provide higher throughput and lower energy consumption than the conventional sensing method. These results, while confirming the higher tolerance against the error of the reporting channel, show a significant energy saving of up to 70% by guaranteeing the throughput efficiency greater than 1.

Implementation of neuromorphic computing systems (NCSs) using digital and analog circuits occup ies a high chip area and consumes high power. With the advancement of nanotechnology, the hybrid Magnetic tunnel junction/Complementary metal–oxide–semiconductor (MTJ/CMOS) circuits have made it possible to implement NCSs with higher density and lower power consumption. However, still there is a gap between the performance of the human brain and NCSs. To mitigate this gap, it is essential to further decrease the energy consumption and the delay of the NCS. The high energy consumption of the MTJ-based NCS is mostly related to the high current needed to switch the MTJ state. Hence, some previous methods tried to perform real-time tracking of the MTJ state by monitoring its voltage and cutting off its current immediately after switching. However, due to the small voltage changes after switching, these methods suffer from a high-power consumption (they need power-hungry amplifiers). In this paper, a new method based on the tracking of MTJ current (instead of voltage) and terminating the MTJ current after switching is proposed. Due to the large changes in the MTJ current after switching (about 40%), there is no need to use an amplifier in the proposed circuit. Therefore, the conventional voltage-mode sensing circuit is replaced with the proposed circuit, to improve the energy efficiency, speed and delay of the NCS. In all state-of-the-art designs, the voltage changes on nodes across the MTJ (PL, FL or both of them) have been used to detect the MTJ switching. However, the proposed circuit detects the MTJ switching by properly sensing the MTJ current and terminates its current immediately. The simulation results in 65-nm CMOS technology confirm that the proposed technique improves the energy consumption and speed of the NCS by 49% and 2.1X compared with the typical NCS.

Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are important issue in MEC. In this paper, we use a standard three-level system model of mobile devices, edge and cloud, and propose two offloading and scheduling algorithms. A decision-making algorithm for offloading tasks is based on the greedy Knapsack offloading algorithm (GKOA) on the mobile device side, which selects tasks with high power consumption for offloading and it saves energy consumption of the device. On the MEC side, we also present a dynamic scheduling algorithm with fuzzy-based priority task scheduling (FPTS) for prioritizing and scheduling tasks based on two criteria. Numerical results show that our proposed work compared to other methods and reduces the waiting time, latency and system overhead. Also, provides the balance of the system with the least number of resources. And the proposed system reduces battery consumption in the smart device by up to 90%. The results show that more than 92% of tasks are executed successfully in the edge environment.

Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are identified using k-means clustering algorithm as a data mining technique. In this regard, the energy consumption dataset are categorized in different clusters by k-means algorithm based upon variations of energy price and ambient temperature during peak hours of hot (Spring and Summer) and cold (Autumn and Winter) periods. Then, the clusters with the possibility of cooling and heating equipment’s commitment are selected. After that, the confidence interval diagram of energy consumption in elected clusters is provided based upon energy price variations. The nominal potential of DRRs, i.e. flexible load, will be obtained regarding the maximum and minimum differences between the average of energy consumption in upper and middle thresholds of the confidence interval diagram. The energy consumption, ambient temperature and energy price related to BOSTON electricity network over a six-year horizon time is utilized to evaluate the proposed model.

The use of energy-conscious solutions is one of the important research topics in the field of cloud computing. By effectively using virtual machine placement and aggregation algorithms, cloud suppliers will be able to reduce energy consumption. In this paper, a new model is presented that seeks to achieve the desired results by improving the algorithms and providing appropriate methods. Periodic monitoring of resource status, proper analysis of the data obtained, and prediction of the critical state of the servers using the proposed Markov model have reduced the number of unnecessary migrations as much as possible. The combination of genetic algorithm and simulated annealing in the replacement section along with the definition of the adsorbent Markov chain has resulted in better and faster performance of the proposed algorithm. Simulations performed in different scenarios in CloudSim show that compared to the best algorithm compared, at low, medium and high load, energy consumption has decreased significantly. Violations of service level agreements also fell by an average of 17 percent.

The current trend of rising energy consumption in the world has hit mankind with two major crises: first, environmental pollution, and second, the acceleration of finishing energy supplies. Energy storage and reduction of pollutants in the city plays a crucial role in the process of maintaining existing energy. Meanwhile, the transportation sector needs attention due to its importance and position, and can annually bring significant amounts of economic savings to the people and governments by reducing energy consumption and adverse environmental effects, and reducing travel time and unwanted delays brought up. Therefore, in the present research, the most important parameters of environmental pollutant emissions in urban transport sector were determined and system dynamics model of Tehran's urban transport was developed. Based on quantitative analysis, six scenarios, Business As Usual, Priority to the Development of Public Transport, Technical Progress, Administrative Rules and Regulations Management, Travel Demand Management and comprehensive policy are evolved. According to the results, CP scenario has the best performance, and by simultaneously implementing the scenarios, each of them will play a role in improving the situation and significantly reducing energy consumption and CO2 emissions.