Wi-Fi Fingerprinting Based Room Level Indoor Localization Framework Using Ensemble Classifiers

Over the past decennium, Wi-Fi fingerprinting based indoor localization has seized substantial attention. Room level indoor localization can enable numerous applications to increase their diversity by incorporating user location. Real-world commercial scale deployments have not been realized because of difficulty in capturing radio propagation models. In case of fingerprinting based approaches, radio propagation model is implicitly integrated in the gathered fingerprints providing more realistic information as compared to empirical propagation models. We propose ensemble classifiers based indoor localization using Wi-Fi fingerprints for room level prediction. The major advantages of the proposed framework are, ease of training, ease to set up framework providing high room-level accuracy with trifling response time making it viable and appropriate for real time applications. It performs well in comparison with recurrently used ANN (Artificial Neural Network) and kNN (k-Nearest Neighbours) based solutions. Experiments performed showed that on our real-world Wi-Fi fingerprint dataset, our proposed approach achieved 89% accuracy whereas neural network and kNN based best found configurations achieved 85 and 82% accuracy respectively

CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study Visual Internet of Things

International audienceThe VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule’s Coefficient) and JC (Jaccard’s Coefficient), execution time and memory consumption. Experimental results showed that Kapur’s algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes

LocSwayamwar: Finding a suitable ML algorithm for Wi-Fi fingerprinting based indoor positioning system

Indoor localization has been a challenging problem for over a decennium. Wi-Fi Fingerprinting based solutions stand out in comparison with Angle-Of-Arrival (AOA), Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA) approaches as they inherently incorporate radio propagation models in fingerprints (FP) which provide more realistic information than radio signal propagation models as well as do not need extra hardware. Diverse Location Based Services (LBS) heavily rely on the performance of localization algorithms used for pattern matching with the collected FP database. This work investigates the performance of several machine learning algorithms as a multiclass classifier for room-level indoor localization including K*, k-NN, Random Forest, FURIA, Multi-Layer Perceptron, and J48. We report results of top five algorithms along with five algorithms selected from various algorithmic categories obtaining an accuracy greater than 95%. Data was generated by collecting 14,080 fingerprints from 20 Access Points at 180 reference points in 1209 m2 area of Software Engineering Center, University of Engineering and Technology (UET), Lahore to construct real-world FP dataset. The results obtained indicate that the best performance is achieved by K* followed by k-NN, Random Forest, FURIA, Multilayer Perceptron, J48 with accuracies 99.52, 99.06, 98.76, 97.26, 97.05, and 95.91% respectively

Assessing the Effectiveness of Copper Oxide Nanoparticles (Cuo-Nps) as Larvicidal Agents against Aedes Aegypti Larvae in a Laboratory Setup

The control of Aedes aegypti (A. aegypti), the primary vector for dengue virus, requires effective larvicidal agents to target larvae breeding in various water sources. This study aimed to assess the effectiveness of Copper Oxide Nanoparticles (CuO-NPs) as larvicidal agents against A. aegypti larvae in a laboratory setup. Larvae were exposed to CuO-NPs at five contrasting concentrations (0, 5, 10, 20, and 30 mg/l). Twenty-five fourth instar larvae were subjected to each concentration with three replicates and one control with the same number (n=25) of larvae under optimum laboratory conditions. The larval mortality was calculated after 24 hours. Mortality rates increased concentration-dependently, with an average of 75 % mortality rates at 30 % concentration showing the highest rate, and LC50 and LC90 were 7.9 mg/l and 27.8 mg/l, respectively. The findings of this study endorse the application of CuO-NP to control mosquito larvae as an environmentally friendly and cost-effective method. Further studies are warranted to explore the long-term effects, environmental impact, and potential application of CuO-NPs in field settings