Adaptive thermal comfort computation with Zigbee wireless sensor networks

Abstract

The rapid development of wireless communications, scalable technology enabling large scale mass production of System-On-Chip boards, and low cost and low power sensors have made wireless sensor networks easy accessible and usable. Zigbee (802.15.4) operated wireless sensor networks have been commonly used in home automation, building automation, personal health care and fitness, consumer electronics, telecom services etc. In our thesis we investigate Zigbee's application in computing thermal comfort in indoor environments as an extension of home automation systems. Maintenance of thermal comfort consumes a large majority of energy costs. In our home automation system we interfaced a Honeywell based humidity sensor. The Zigbee system consists of a central unit called a coordinator which acts as a control unit. The coordinator is responsible for configuring the network and the start of the network. The end-devices which also act as routers are interfaced with a humidity sensor, an inbuilt temperature sensor, light sensor and accelerometer. The end-device periodically reports data like temperature, humidity, light and accelerometer readings. From these readings, a thermal comfort index is calculated by an index called Predicted Mean Vote. Thermal comfort is dependent on variation of 6 factors like clothing, Metabolism, air temperature, air velocity, mean radiant temperature and relative humidity. A series of simulations are performed with MATLAB to illustrate variation of PMV with the above mentioned 6 factors. Finally prediction and opinion is given about cost variation with energy usage variation. Also the thesis gives advice on achieving thermal comfort by taking into consideration different factors and also to change those factors to achieve thermal comfort at expense of the cost