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The purpose of this paper is to perform large-scale environmental sensing with a lot of Internet of Things (IoT) devices, as typically seen in a Smart City, efficiently and for multiple applications. In this paper, we propose a novel sensing method, called mission-oriented sensing, which accepts multiple and dynamic sensing purposes on a single infrastructure.

The proposed method achieves the purpose by dealing sensing configuration (application’s purpose) as a mission. It realizes sharing single infrastructure by accepting multiple missions in parallel, and it accepts missions’ update anytime. In addition, the sensing platform based on military analogy can command and control a lot of IoT devices in good order, and this realizes mission-oriented sensing above.

Introducing mission-oriented sensing, multiple purpose large-scale sensing can be conducted efficiently. The experimental evaluation with a prototype platform shows the practical feasibility. In addition, the result shows that it is effective to update sensing configuration dynamically.

The proposed method focuses aggregating environmental sensor value from a lot of devices, and, thus, it can treat stream data, such as video or audio or control a specific device directly.

In proposed method, a single-sensing infrastructure can be used by multiple applications, and it admits heterogeneous devices in a single infrastructure. In addition, the proposed method has less technical restriction and developers can implement actual platform with technologies for context.

The purpose of this paper is to establish an application platform that addresses expensive development cost and effort of indoor location-aware application (InL-Apps) problems caused by tightly coupling between InL-App and indoor positioning systems (IPSs).

To achieve this purpose, in this paper, the authors proposes a Web-based integration framework called Web-based Integration Framework for Indoor Location (WIF4InL). With a common data model, WIF4InL integrates indoor location data obtained from heterogeneous IPS. It then provides application-neutral application programming interface (API) for various InL-Apps.

The authors integrate two different IPS (RedPin and BluePin) using WIF4InL and conduct a comparative study which is based on sufficiency of essential capabilities of location-dependent queries among three systems: RedPin, BluePin and WIF4InL. WIF4InL supports more capabilities for the location-dependent queries. Through the data and operation integration, WIF4InL even enriches the existing proprietary IPS.

As WIF4InL allows the loose coupling between IPS and InL-Apps, it significantly improves reusability of indoor location information and operation.

The purpose of this paper is to develop a facade for seamlessly using locating services and enabling easy development of an application with indoor and outdoor location information without being aware of the difference of individual services. To achieve this purpose, in this paper, a unified locating service, called KULOCS (Kobe-University Unified LOCating Service), which horizontally integrates the heterogeneous locating services, is proposed.

By focusing on technology-independent elements [when], [where] and [who] in location queries, KULOCS integrates data and operations of the existing locating services. In the data integration, a method where the time representation, the locations and the namespace are consolidated by the Unix time, the location labels and the alias table, respectively, is proposed. Based on the possible combinations of the three elements, an application-neutral application programming interface (API) for the operation integration is derived.

Using KULOCS, various practical services are enabled. In addition, the experimental evaluation shows the practical feasibility by comparing cases with or without KULOCS. The result shows that KULOCS reduces the effort of application development, especially when the number of locating services becomes large.

KULOCS works as a seamless facade with the underlying locating services, the users and applications consume location information easily and efficiently, without knowing concrete services actually locating target objects.