Adaptive Rate Technique Research Articles

Signal Piloted Processing of the Smart Meter Data for Effective Appliances Recognition

The installation of smart meters is fast growing to effectively support various smart grid stack holders. Collection and processing of fine-grained metering data is important for proper analysis and decision support. The traditional smart meters are based on standardized and time-invariant tactics to acquire and process the data. This results in the collection, storage, and processing of a huge amount of unneeded data. The focus of this paper is to enhance the contemporary smart meters data acquisition and processing chains. The objective is to attain real-time compression and computational effectiveness to enhance the system performance in terms of data analysis, storage and transmission and to diminish its consumption overhead. In this framework, the signal-piloted event-driven sampling and processing tactics are exploited. The novel adaptive rate techniques are used for data segmentation and extraction of features. Household appliances consumption patterns related features are being classified subsequently. It is realized by employing the mature K-Nearest Neighbor and the Artificial Neural Network classifiers. Results demonstrate a 3.8-fold compression gain and computational effectiveness of the designed solution over traditional counterpart while securing the best classification accuracy of 94.4% for the 6-class appliances dataset.

Event-Driven System For Proficient Load Recognition by Interpreting the Smart Meter Data

Abstract The technological advancements have evolved the deployment of smart meters. A fine-grained metering data collection and analysis is necessary to bring benefits to multiple smart grid stakeholders. The classical sensing mechanism is time-invariant. Therefore, it results in the collection, transmission, and processing of a large amount of unnecessary data. This work employs the event-driven sensing mechanism to achieve real-time data compression. Afterward, the novel adaptive rate techniques are employed for the data conditioning, segmentation, and extraction of features. The pertinent features regarding the appliances’ consumption patterns are afterward used for their identification. It is realized by employing the mature Support Vector Machine and k-Nearest Neighbor classifiers. Results confirm a 3.4 times compression gain and the computational effectiveness of the suggested solution while securing 95.4% classification precision. It shows the benefits of integrating the proposed method in the realization of current energy efficiency services like enumerated consumption billing, effective load identification, and dynamic load management.

Packet Transmission over a Fixed Wireless Loop Using Adaptive Rate Techniques

In this paper a rate-adaptive transmission scheme is used to ensure reliable and efficient transmission of packets over a fixed wireless link. The scheme is based on a stationary, memoryless, and blockfading channel model with perfect knowledge of channel fading state, available at transmitters. Information, in our proposed system, is transmitted in the form of packets that each may potentially consist of several conventional ATM cells along with a rate-identifier preamble that contains the information needed by a receiver, allowing for proper demodulation/decoding of packets. In this application, because of the short physical length of a radio link, a simple go-back-N ARQ protocol can provide a reliable communications system with an acceptable throughput. Analytical results for spectral efficiency, throughput and queuing delay show that adaptive-rate systems can achieve significant improvements over those using fixed-rate schemes.