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Wireless Sensor Networks (WSNs) are extensively being used in various environments to implement different monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. Various applications of wireless sensor networks need information about the physical location of each sensor node. So therefore selforganisation and localisation capabilities are one of the most significant requirements in sensor networks. In this paper, we proposed a distance based cooperative localization algorithm called Curvilinear Component Analysis mapping (CCA-MAP) for nodes position estimation within a WSN. This algorithm is based on distance measurements using Received Signal Strength Indicator (RSSI) technique. The performance of our approach is evaluated through simulations using MATLAB simulator and we also implemented it in a real system deployment in an indoor environment by performing an empirical measurement using Crossbow IRIS sensor motes. The simulation results obtained revealed that our approach delivers improved position accuracy.

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