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.

In preparation for the upcoming home delivery services that rely on Unmanned Aerial Vehicles (UAVs), we developed a new multi-hop radio network that is laid over a smart meter network transferring electric energy information only. In this network, a UAV follows, for navigation purposes, the topology of a virtual network overlaid on the physical smart meter network. We established a service management control method which does not rely on image analysis or map information processing, i.e. processes that consume precious power resources of the UAV. Instead, navigation is based on the routing technology. The current distance between the UAV and a node of the smart meter network is measured by means of the radio transmission loss value, therefore determining the position of the UAV. A two-layer network model has been proposed. One layer consists of a network of nodes in a residential area with scattered buildings – a location that is safer to navigate – while the other is an access network of nodes in a densely populated area. Then, we proposed methods to determine the direction of movement towards the next hop node on the data-link layer and the end node on the network layer, which is the target destination. We implemented a software-based test system and verified the effectiveness of the proposed methods.

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.

Future smart cities are expected to provide intelligent services such as predictions, detections, and automation through digital twins. However, the creation of digital twins requires the processing of an enormous amount of data, thereby leading to an increase in mobile network traffic. This traffic is produced by applications in user devices and city services, which engage in local consumption at the city scale through sensor and camera devices using mobile networks. Such increased traffic can compromise the communication speed and stability. To alleviate this burden, traffic offloading becomes a crucial consideration in the beyond-5G era. This paper presents a scheme known as Area-Controlled Mobile Ad-Hoc Networking (AMoND). AMoND uses a hierarchical structure of a location layer and an ad-hoc layer to construct area-controlled mobile ad-hoc networks (MANETs) for mutual support of the digital twin and MANETs. AMoND effectively suppresses mobile network traffic by harnessing the digital twin to assist the MANETs during data collection for the digital twin construction. Importantly, the digital twin used in AMoND focuses on the management of node location information and does not need to reproduce the real space on a computer fully. AMoND is not dependent on a specific MANET protocol and can be used as an add-on. AMoND exhibits the ability to reduce traffic volumes by up to approximately 65%, while maintaining arrival rates that are comparable to existing MANET protocols under certain conditions.

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.

Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments.