Making houses more inclusive, safer, resilient and sustainable is an important requirement that must be achieved in every society. Gas leakage and fires in smart houses are serious issues that are causing people's death and properties losses. Currently, preventing and alerting systems are widely available. However, they are generally individual units having elementary functions without adequate capabilities of multi-sensing and interaction with the existing Machine-to-Machine (M2M) home network along with the outside networks such as Internet. Indeed, this communication paradigm will be clearly the most dominant in the near future for M2M home networks. In this paper, we are proposing an efficient system model to integrate the gas leakage and fire detection system into a centralized M2M home network using low cost devices. Then, through machine learning approach, we are involving a data mining method with the sensed information and detect the abnormal air state changes in hidden patterns for early prediction of the risk incidences. This work will help to enhance safety and protect property in smart houses.

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

The Internet of Things (IoT) is expected to count for a large part of the Internet traffic and its impact on the network is still widely unknown. It is therefore essential to study the IoT Traffic in order to characterize its properties and evaluate its performances. In this paper, we propose a novel IoT traffic generator called IoTTGen. We model the IoT traffic and we generate synthetic traffic for smart home and bio-medical IoT environments. We also extracted anomalous IoT traffic from a real dataset and study the IoT traffic properties by computing the entropy value of traffic parameters. Our generator succeeds in capturing the characteristics of the IoT traffic, which can be visually observed on Behavior Shape graphs. Our generator can also serve to describe the main IoT traffic properties and also to detect IoT traffic anomalies.

To collect and aggregate the enormous amounts of spatial information needed to maintain virtual three-dimensional (3D) spaces, such as digital twins, in real time, significant resources are required. Recently, light detection and ranging (LiDAR)-equipped mobile devices have become increasingly popular. Therefore, mobile sensing using such devices is a feasible solution for maintaining and updating virtual 3D city spaces by motivating people. In this study, we propose and develop a mobile sensing system based on an augmented reality coloring game using LiDAR-equipped mobile devices. Next, we verify the fundamental operation and effectiveness of the proposed system.