Recently, many disasters have occurred in indoor places. In order to rescue or detect victims within disaster scenes, vital information regarding their existence and location is needed. To provide such information, some studies simply employ indoor positioning systems to identify each mobile device of victims. However, their schemes may be unreliable, since people sometimes drop their mobile devices or put them on a desk. In other words, their methods may find a mobile device, not a victim. To solve this problem, this paper proposes a novel individual monitoring system based on edge intelligence. The proposed system monitors coexisting states with a user and a smart mobile device through a user state detection mechanism, which could allow tracking through the monitoring of continuous user state switching. Then, a fine-grained localization scheme is employed to perceive the precise location of a user who is with a mobile device. Hence, the proposed system is developed as a proof-of-concept relying on off-the-shelf WiFi devices and reusing pervasive signals. The smart mobile devices of users interact with hierarchical edge computing resources to quickly and safely collect and manage sensing data of user behaviors with encryption by cipher-block chaining, and user behaviors are analyzed via the ensemble paradigm of three machine learning technologies. The proposed system shows 98.82% prevision for user activity recognition, and 96.5% accuracy for user localization accuracy is achieved.

Industrial Internet of Things (IIoT) is based on data acquisition and data analytics technologies. A variety of and a large amount of data is collected at management nodes with computing and storage capacities. Recently, the computing ability, denoted by the edge, has been located closer to the service fields to achieve faster and more reliable data-driven service provisioning. So, edge computing is a useful resource to facilitate smart manufacturing based on IIoT. Since the current Industrial Wireless Sensor Network (IWSN) technologies for IIoT do not perfectly cover all the demands of industries with smart manufacturing such as agile flexibility with asset movement. A major future demand for IWSN should be to support the mobility of assets in a wireless environment. This article investigates the shortages of current technologies such asWireleeHART and proposes a novel wireless networking scheme based on edge-based cognitive computing in order to support reliable and low latency communication of mobile assets, which is involved in the smart manufacturing processes. We devise a two-tier geographical networking (TTGN) system that supports position-based mobility detection and networking. Also, resource allocation for the reliable and real-time has been obtained based on the game theory. Evaluation results demonstrate that TTGN can guarantee a high data transmission success ratio, as well as a fast delivery ratio for link path establishment.

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.

Parking is an everyday ordinary activity in urban lives. In other words, it is the most common and essential requirement of all drivers in a car park to fast search a preferred parking spot. Recently most parking lots provide a sensing-based display system to notify available parking spots. However, such systems are still unable to tell drivers exact parking spots and make any recommendation to improve the traffic conditions and driver experiences. In this paper, a novel machine-learning-aided smart parking system based on Internet of Things, smart mobile devices and edge computing is proposed. This novel parking system aims at providing customized parking experience to users through highly accurate positioning and user activity sensing computed as the edge intelligence. The proposed system mainly focuses on increasing user experience for parking through the concatenated autoencoder design for improving performances of the user positioning mechanism.

Social Network Services(SNS) such as Facebook, Friendster, MySpace and Orkut have established themselves as very popular and powerful tools for making and finding friends and for identifying other people who share similar interests. Search behavior of Web users often reflects that of others who have similar interests or similar information profiles in social networks. Therefore, if we locate web users interested in certain topics or areas and then keep track of their preferences in terms of Web search results, we can pinpoint relevant and reliable information about these people in a timely manner In this paper, we present an efficient search system called SMART Finder. This system supports efficient search results for locating people whose social relationships are highly ranked according to specific topics. It can also identify people who are highly associated with each other with regard to Web search topics. Our results are supported by experiments on Orkut and Facebook.

In urban monitoring systems, mobile sensing is imperative to acquire data from sensors and relay them to a cloud server. Mobile devices can be used anytime and anywhere, enabling communication with pervasive sensing in various conditions to obtain the data. Reliable data acquisition has been required in urban monitoring systems from the macroscale to the microscale. However, a broadcast method for the data acquisition process may lead to the increased battery power consumption of mobile devices. Managing the battery power consumption of mobile devices is essential for reliable data acquisition. In this paper, we propose an urban monitoring system with an optimization algorithm in which a cloud server broadcasts a communication request that includes battery power consumption and the data acquisition quantity of mobile devices. Game theoretic optimization is formulated with a decision process. We derive a best response and Nash equilibrium for mobile communication with sensors and a cloud server. Evaluation results demonstrate that the proposed system can guarantee a low battery power consumption, as well as acquire the desired data quantity.