One of the key prerequisites to successfully empower these services is estimating the position of a subject of interest. Knowledge about location information becomes the key to numerous location-based services (LBS) in various application domains including healthcare and safety, search and rescue, assisted living, robotics, shopping and museum assistance, context awareness and social networking, advertising, and marketing. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Finally, the unknown location is estimated using the nearest neighbour method. After that, a compensation model is proposed to correct the RSSI. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. Then, an angle estimation method is proposed by leveraging the concept of landmark. Firstly, this study examines the effect of the user’s body on RSSI. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance.

However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications.

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