The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT), our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

Most surveillance systems only contain CCTVs. CCTVs, however, provide only limited maneuverability against dynamic targets and are inefficient for short term surveillance. Such limitations do not raise much concern in some cases, but for the scenario in which traditional surveillance systems do not suffice, adopting a fleet of UAVs can help overcoming the limitations. In this paper, we present a surveillance system implemented with a fleet of unmanned aerial vehicles (UAVs). A surveillance system implemented with a fleet of UAVs is easy to deploy and maintain. A UAV fleet requires little time to deploy and set up, and removing the surveillance is also virtually instant. The system we propose deploys UAVs to the target area for installation and perform surveillance operations. The camera mounted UAVs act as surveillance probes, the server provides overall control of the surveillance system, and the fleet platform provides fleet-wise control of the UAVs. In the proposed system, the UAVs establish a network and enable multi-hop communication, which allows the system to widen its coverage area. The operator of the system can control the fleet of UAVs via the fleet platform and receive surveillance information gathered by the UAVs. The proposed system is described in detail along with the algorithm for effective placement of the UAVs. The prototype of the system is presented, and the experiment carried out shows that the system can successfully perform surveillance over an area set by the system.

As the era of IoT comes, drones are in the spotlight as a mobile medium of Internet of Things (IoT) devices and services. However, drones appear to be vulnerable to physical capture attacks since they usually operate far from operators. If a drone is illegally captured, some important data will be exposed to the attacker. In this paper, we propose a saveless-based key management and delegation system for a multi-drone control system. The proposed system enables a multi-drone control system to highly resist physical capture by minimizing exposure of confidential data. In addition, when the drone leaves the formation for performing another mission or by a natural environment, the system can allow the drone to securely re-participate in the formation with the help of the ground control station (GCS) when it comes back. The analysis result shows that the proposed system can reduce storage space usage and require less computational overhead. From the result, we expect that the system can guarantee the resistance of physical capture and secure key management to the drones as well as many mobile IoT devices.

Vast applications and services have been enabled as the number of mobile or sensing devices with communication capabilities has grown. However, managing the devices, integrating networks or combining services across different networks has become a new problem since each network is not directly connected via back-end core networks or servers. The issue is and has been discussed especially in wireless sensor and actuator networks (WSAN). In such systems, sensors and actuators are tightly coupled, so when an independent WSAN needs to collaborate with other networks, it is difficult to adequately combine them into an integrated infrastructure. In this paper, we propose drone-as-a-gateway (DaaG), which uses drones as mobile gateways to interconnect isolated networks or combine independent services. Our system contains features that focus on the service being provided in the order of importance, different from an adaptive simple mobile sink system or delay-tolerant system. Our simulation results have shown that the proposed system is able to activate actuators in the order of importance of the service, which uses separate sensors’ data, and it consumes almost the same time in comparison with other path-planning algorithms. Moreover, we have implemented DaaG and presented results in a field test to show that it can enable large-scale on-demand deployment of sensing and actuation infrastructure or the Internet of Things (IoT).

Much research has been conducted in the area of face and gesture recognition in order to classify one’s emotional state. Surprisingly, utilizing computerized algorithms which recognize emotional conditions based on body postures has not yet been systematically developed. In this paper, we propose a novel method, Computerized Emotion Perception based on Posture (CEPP), to determine the emotional state of the user. This method extracts features from body postures and estimates the emotional state by computing a similarity distance. With the proposed algorithm, we will provide new insights into automatically recognizing one’s emotional state.