I- Introduction

II- Related Work

III- Proposed Method

IV- Experimental Results

V- Conclusion

References

Abstract

Reliable data delivery in the Internet of Things (IoT) is very important in order to provide IoT-based services with the required quality. However, IoT data delivery may not be successful for different reasons, such as connection errors, external attacks, or sensing errors. This results in data incompleteness, which decreases the performance of IoT applications. In particular, the recovery of missing data among the massive sensed data of the IoT is so important that it should be solved. In this paper, we propose a probabilistic method to recover missing (incomplete) data from IoT sensors by utilizing data from related sensors. The main idea of the proposed method is to perform probabilistic matrix factorization (PMF) within the preliminary assigned group of sensors. Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm. Simulation results show that the proposed PMF model with clustering outperforms support vector machine (SVM) and deep neural network (DNN) algorithms in terms of accuracy and root mean square error. By using normalized datasets, PMF shows faster execution time than SVM, and almost the same execution time as the DNN method. This proposed incomplete data–recovery approach is a promising alternative to traditional DNN and SVM methods for IoT telemetry applications.

Introduction

UE to advancements in information technology, the Internet of Things (IoT) has been emerging as the next big thing in our daily lives. It is defined as a global network with an infrastructure that has self-configuring capabilities [1]. The IoT is an intelligent network that connects billions of things via the Internet by using a variety of communications technologies, such as conventional Long Term Evolution (LTE), Wi-Fi, ZigBee, wireless sensor networks (WSNs), Ethernet, as well as specially developed Internet Protocol Version 6 (IPv6) over low-power wireless personal area networks (6LoWPAN), the low-power wide area network from the LoRa Alliance (LoRaWAN), LTE machine type communications (LTE-MTC), narrowband IoT (NB-IoT), and many other communications technologies. Therefore, the IoT is rapidly transforming into a highly heterogeneous ecosystem that provides interoperability among different types of devices and communications technologies. The IoT achieves the goal of intelligent identification, location, tracking, monitoring, and managing of things [2]. It also creates additional value for a better life by sharing the information collected among different things, and it integrates and consolidates services at the edge using different IoT gateways. IoT implementation requires new solutions to integrate different physical objects (things) into a global IoT ecosystem so that all of them can be identified and recognized automatically. To achieve this, we need a reliable transmission medium to communicate among things, and an intelligent processing tool, such as cloud or fog computing, to generate additional value from IoT applications. According to recent analytics, we expect more than 100 billion IoT devices by 2025, whereas global financial revenue from the IoT will grow from US$3.9 trillion to US$11.1 trillion [3]. However, with its future implications, the IoT brings substantial challenges, such as security, privacy, and reliability, which need to be considered as well.