Context-Aware Indoor Environment Monitoring and Plant Prediction Using Wireless Sensor Network

Author(s):  
Sadia Mughal ◽  
Fahad Razaque ◽  
Mukesh Malani ◽  
Muhammad Raheel Hassan ◽  
Saqib Hussain ◽  
...  
2013 ◽  
Vol 380-384 ◽  
pp. 635-638
Author(s):  
Chen Chen

With advance of our human beings science and technology and enhance of the living standards, more and more people have addressed higher requirements on the environmental conditions in a hospital, therefore, the traditional and no-intelligent monitoring devices are being replaced by the automated and networked monitoring systems gradually. In this case, application of the wireless sensor network just fits this need. This paper proposes the Tianjin First Central Hospital indoor environment monitoring & control system of distributed acquisition and execution, and centralized management by focusing on the needs for the technical indicators of the hospital indoor environment. During design of the system, an universal design concept was put forward, and also a non-standard communication protocol for the wireless sensor network designed independently in combination with the OSI open standard. In this paper, realization of the communication protocol among the nodes with embedded software and the operation mechanism of the modes themselves are discussed, also a console panel has been developed for the data center. Several software design algorithms are proposed with respect to the network layout. This paper also describes the test platform of the Tianjin First Central Hospital indoor environment monitoring & control system established with the network components designed, and provides the test and verification results, including the monitored data of the various gases, corresponding automatic control functions, and underlay BER analysis. The results show that this system can basically realize automatic monitoring on the Tianjin First Central Hospital indoor environment. At present, the sensitive gases include CO, CO2, O2, NH3 and formaldehyde, sensitive environments temperature, humidity and light intensity, and controlled targets ventilation and lighting. This paper offers an optional solution for environment monitoring and has certain theoretical value and engineering significance.


Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error


2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yan Wang ◽  
Yang Yan ◽  
Zhengjian Li ◽  
Long Cheng

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.


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