Abstract
Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods.
Highlights
Sensor arrays are groups of sensors working in the same aim, and usually deployed in some spatial pattern, including circular, planar, linear, spherical and cylindrical shapes
In order to obtain a broad view of the performance of different methods, we use several baselines: K-Nearest Neighbors (KNN) based on items [26] (ColKNN), KNN based on users [29] (RowKNN), Probabilistic Matrix Factorization (PMF) [34], Biased MF [35]
We have addressed the problem of predicting missing values from sensor arrays deployed in large Internet of Things (IoT) systems, produced due to unreliable sensors, extreme environmental situations, deficient Quality of Service (QoS) and many other problematic issues
Summary
Sensor arrays are groups of sensors working in the same aim, and usually deployed in some spatial pattern, including circular, planar, linear, spherical and cylindrical shapes. The sensor arrays main advantage is the new dimensions that they provide to the observation. Usual applications of array sensors are signal-to-noise ratio gain, estimation of the direction of the signal and parameter prediction. This later parameter estimation is a key application of the array sensors, since it takes advantage of the spatial or temporal properties of the incoming signals. Research in array sensors is currently on the rise and it covers a wide range of areas, such as medical, chemical, communication and Internet of Things (IoT). Current research in medical applications of sensor arrays makes use of several base sensors
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