Abstract
Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.
Highlights
Internet of ings (IoT) is an emerging technology that provides different devices to interconnect and communicate with each other
Inspired by many state-ofthe-art optimization-based machine learning models, we used a state-of-the-art dataset of the well-known UCI repository, which is the same as in [10], to evaluate its performance. e proposed model is evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM) using MATLAB. e KNN and SVM stay good performers achieving room level accuracy up to 98.7% and 98.3%, respectively, while the proposed model achieves high accuracy up to 99.7%
Different training and testing experiments were performed on three models, i.e., support vector machine (SVM), K-nearest neighbor (KNN), and cuckoo searchbased support vector machine (CS-SVM). ese models were evaluated in terms of precision, recall, and sccuracy
Summary
Internet of ings (IoT) is an emerging technology that provides different devices to interconnect and communicate with each other. The use of machine learning (ML) algorithms in various applications of IoT has attracted researchers from all over the world. For a very long time, location has been extremely useful for humans to navigate outdoor over the sea, air, and land using astrolabe, sextant, and octant to determine their location with respect to various celestial bodies [1]. In the 20th century, with the advancement in electronics and communication, new technologies are adapted such as Radio Detection and Ranging (RADAR), Long Range Navigation (LORAN), and Global Positioning System (GPS) for localization [1]. GPS remains one of the most dominant technologies among the available technologies to localize an object. People are spending most of their time in an indoor environment, needing the positioning system to trace people and objects in the indoor complex environment. erefore, many applications have
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