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Z. Chair, P. Varshney (1986)
Optimal Data Fusion in Multiple Sensor Detection SystemsIEEE Transactions on Aerospace and Electronic Systems, AES-22
Dae-Man Han, Jae-Hyun Lim (2010)
Design and implementation of smart home energy management systems based on zigbeeIEEE Transactions on Consumer Electronics, 56
C. Schindelhauer (2006)
Mobility in Wireless Networks
N. Dlodlo
Adopting the internet of things technologies in environmental management in South Africa
D. Nguyen, Wook Choi, Minh Ha, Hyunseung Choo (2011)
Design and analysis of a multi-candidate selection scheme for greedy routing in wireless sensor networksJ. Netw. Comput. Appl., 34
R. Pazzi, A. Boukerche (2008)
Mobile data collector strategy for delay-sensitive applications over wireless sensor networksComput. Commun., 31
G. Lazarou, Jing Li, J. Picone (2007)
A cluster-based power-efficient MAC scheme for event-driven sensing applicationsAd Hoc Networks, 5
A. Abbasi, M. Younis (2007)
A survey on clustering algorithms for wireless sensor networksComput. Commun., 30
K. Parsopoulos, M. Vrahatis (2002)
Recent approaches to global optimization problems through Particle Swarm OptimizationNatural Computing, 1
IEEE Standard 802.15.4
IEEE Standard for Information Technology – Telecommunications and Information Exchange Between Systems – Local and Metropolitan Area Networks – Specific IJPCC 7,1 56 Requirements Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR‐WPANs)
Wen-Tsai Sung, Yu-Feng Liu, Jui-Ho Chen, Chia-Hao Chen (2010)
Enhance the efficient of WSN data fusion by neural networks training process2010 International Symposium on Computer, Communication, Control and Automation (3CA), 2
Wen-Tsai Sung, Y. Chiang (2012)
Improved Particle Swarm Optimization Algorithm for Android Medical Care IOT using Modified ParametersJournal of Medical Systems, 36
W. Heinzelman, A. Chandrakasan, H. Balakrishnan (2002)
An application-specific protocol architecture for wireless microsensor networksIEEE Trans. Wirel. Commun., 1
B. Jagyasi, B. Dey, S. Merchant, U. Desai (2006)
Weighted Aggregation Scheme with Lifetime-Accuracy Tradeoff in Wireless Sensor Network2006 Fourth International Conference on Intelligent Sensing and Information Processing
Wen-Tsai Sung, Yao-Chi Hsu (2011)
Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBeeExpert Syst. Appl., 38
Tossaporn Srisooksai, Kamol Keamarungsi, P. Lamsrichan, K. Araki (2012)
Practical data compression in wireless sensor networks: A surveyJ. Netw. Comput. Appl., 35
M. Ali, P. Kaelo (2008)
Improved particle swarm algorithms for global optimizationAppl. Math. Comput., 196
Ing-Jiunn Su, Chia-Chih Tsai, Wen-Tsai Sung (2012)
Area temperature system monitoring and computing based on adaptive fuzzy logic in wireless sensor networksAppl. Soft Comput., 12
Bo Liu, Ling Wang, Yihui Jin, F. Tang, Dexian Huang (2005)
Improved particle swarm optimization combined with chaosChaos Solitons & Fractals, 25
M. Clerc, J. Kennedy (2002)
The particle swarm - explosion, stability, and convergence in a multidimensional complex spaceIEEE Trans. Evol. Comput., 6
Wen-Tsai Sung, Ming-Han Tsai (2012)
Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithmsComput. Math. Appl., 64
Jinsoo Han, Chang-Sic Choi, Il-Woo Lee (2011)
More efficient home energy management system based on ZigBee communication and infrared remote controlsIEEE Transactions on Consumer Electronics, 57
Purpose – This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non‐linear weights with decreasing strategy to implement the improved PSO (IPSO) algorithm. Using various types of sensors, combined with ZigBee wireless sensor networks and the TCP/IP network. The GPRS/SMS long‐range wireless network will sense the measured data analysis and evaluation to create more effective monitoring and observation in a regional environment to achieve an Internet of Things with automated information exchange between persons and things. Design/methodology/approach – This study proposes a wireless sensor network system using ZigBee (PSoC‐1605A) chip, sensor and circuit boards to constitute the IOT system. The IOT system consists of a main coordinator (PSoC‐1605A), smart grid monitoring system, robotic arm detection warning system and temperature and humidity sensor network. The hardware components communicate with each other through wireless transmission. Each node collects data and sends messages to other objects in the network. Findings – This study employed IPSO to perform information fusion in a multi‐sensor network. The paper shows that IPSO improved the measurement preciseness via weight factors estimated via experimental simulations. The experimental results show that the IPSO algorithm optimally integrates the weight factors, information source fusion reliability, information redundancy and hierarchical structure integration in uncertain fusion cases. The sensor data approximates the optimal way to extract useful information from each fusion data and successfully eliminates noise interference, producing excellent fusion results. Practical implications – Robotic arm to tilt detection warning system: Several geographic areas are susceptible to severe tectonic plate movement, often generating earthquakes. Earthquakes cause great harm to public infrastructure, and a great threat to high‐tech, high‐precision machinery and production lines. To minimize the extent of earthquake disasters and allow managers to deal with power failures, vibration monitoring system construction can enhance manufacturing process quality and stability. Smart grid monitoring system: The greenhouse effect, global energy shortage and rising cost of traditional energy are related energy efficiency topics that have attracted much attention. The aim of this paper is that real‐time data rendering and analysis can be more effective in understanding electrical energy usage, resulting in a reduction in unnecessary consumption and waste. Temperature and humidity sensor network system: Environmental temperature and humidity monitoring and application of a wide range of precision industrial production lines, laboratories, antique works of art that have a higher standard of environmental temperature and humidity requirements. The environment has a considerable influence on biological lifeforms. The relative importance of environmental management and monitoring is acute. Originality/value – This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. An illustration in the paper indicates that IPSO applies the Internet of Things (IOT) system in monitoring a system via adjusted weight factors better than other existing PSO methods in computing a precise convergence rate for excellent fusion results.
Sensor Review – Emerald Publishing
Published: Jun 14, 2013
Keywords: SoC; ZigBee; Information fusion; IOT; Particle swarm optimization (PSO); Sensors; Monitors; Condition monitoring
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