Performance Evaluation of the Heating System Based on Internet of Things

2011 ◽  
Author(s):  
Siyuan Wang ◽  
Tingting Wu ◽  
Shuanghe Yu ◽  
Wenbiao Wang
Keyword(s):  
Performance Evaluation ◽  
Internet Of Things ◽  
Heating System

ANFIS based authentication performance evaluation for enhancing security in Internet of Things

2020 ◽  
Author(s):  
Tasmima Noushiba Mahbub ◽  
S. M. Salim Reza ◽  
Dilshad Ara Hossain ◽  
Mehedi Hasan Raju ◽  
Md Murshedul Arifeen ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Internet Of Things

Performance Evaluation of a Solar Water Heating System for Industrial Application

2012 ◽  
Vol 512-515 ◽  
pp. 130-136
Author(s):  
Keh Chin Chang ◽  
Wei Min Lin ◽  
Yi Mei Liu ◽  
Tsong Sheng Lee ◽  
Kung Ming Chung
Keyword(s):  
Performance Evaluation ◽  
Heating System ◽  
Economic Viability ◽  
Solar Collectors ◽  
Water Heating ◽  
Policy Makers ◽  
Solar Water Heating ◽  
Solar Water ◽  
Solar Water Heating System

The total area of solar collectors installed in Taiwan had exceeded 2 million square meters by the end of 2010. However, there were only 98 systems in operation with area of solar collectors installed exceeding 100 square meters from 2001 to 2010. To increase industrial awareness of solar water heating technologies, a nursery greenhouse was chosen as the case study to evaluate its thermal performance throughout the months of May 2010 to April 2011. The results showed that the solar energy collected and heat loss during the night hours would affect the thermal efficiency, economic viability and attractiveness of a SWH. This study would provide useful information for all parties related to this market, manufacturers, potential users and policy-makers.

Performance evaluation for solar combined gas heating system

2020 ◽  
Author(s):  
Wenxue Gao ◽  
Yan Wang ◽  
Lin Yang ◽  
Shaojie Xu ◽  
Weiye Zhou ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Heating System ◽  
Gas Heating

scholarly journals Performance Evaluation and Validation of QCM (Query Control Mechanism) for QoS-Enabled Layered-Based Clustering for Reactive Flooding in the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 283
Author(s):  
Fawad Ali Khan ◽  
Rafidah Md Noor ◽  
Miss Laiha Mat Kiah ◽  
Ismail Ahmedy ◽  
Mohd Yamani ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Control Mechanism ◽  
Performance Measure ◽  
The Internet ◽  
Control Mechanisms ◽  
Wide Range ◽  
The Internet Of Things ◽  
Network Flooding

Internet of Things (IoT) facilitates a wide range of applications through sensor-based connected devices that require bandwidth and other network resources. Enhancement of efficient utilization of a heterogeneous IoT network is an open optimization problem that is mostly suffered by network flooding. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. Several query control mechanisms in the literature claimed to cater to the issues related to bandwidth, cost, and Quality of Service (QoS). This research article presented a statistical performance evaluation of different query control mechanisms that addressed minimization of energy consumption, energy cost and network flooding. Specifically, it evaluated the performance measure of Query Control Mechanism (QCM) for QoS-enabled layered-based clustering for reactive flooding in the Internet of Things. By statistical means, this study inferred the significant achievement of the QCM algorithm that outperformed the prevailing algorithms, i.e., Divide-and-Conquer (DnC), Service Level Agreements (SLA), and Hybrid Energy-aware Clustering Protocol for IoT (Hy-IoT) for identification and elimination of redundant flooding queries. The inferential analysis for performance evaluation of algorithms was measured in terms of three scenarios, i.e., energy consumption, delays and throughput with different intervals of traffic, malicious mote and malicious mote with realistic condition. It is evident from the results that the QCM algorithm outperforms the existing algorithms and the statistical probability value “P” < 0.05 indicates the performance of QCM is significant at the 95% confidence interval. Hence, it could be inferred from findings that the performance of the QCM algorithm was substantial as compared to that of other algorithms.

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