The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

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Internet of Things (IoT) is a current technology in the communication and computing field. With the help of this technology many application can be developed almost in every fields like health care, agriculture, education manufacturing, factories, automobile etc. In this fields the applications can be like smart home, smart office, smart farms, smart utilities, smart boards, smart vehicles, smart machines etc. To develop this kind of applications already many IOT startup companies are evolved. The current paper proposed a novel IOT product development cycle which will make easy for the developers in the IOT startup companies/individuals and in this paper few developed products based on the proposed development cycle were discussed.

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