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Communication and information access defines the basis to reach a personalized health end-to-end framework. Personalized health capability is limited to the available data from the patient. The data is usually dynamic and incomplete. Therefore, it presents a critical issue for mining, analysis and trending. For that reason, this work presents an interconnection framework for mobile Health (mHealth) based on the Internet of Things. It makes continuous and remote vital sign monitoring feasible and introduces technological innovations for empowering health monitors and patient devices with Internet capabilities. It also allows patient monitoring and supervision by remote centers, and personal platforms such as tablets. In terms of hardware it offers a gateway and a personal clinical device used for the wireless transmission of continuous vital signs through 6LoWPAN, and patient identification through RFID. In terms of software, this interconnection framework presents a novel protocol, called YOAPY, for an efficient, secure, and scalable integration of the sensors deployed in the patient's personal environment. This paper presents the architecture and evaluates its capability to provide continuous monitoring, ubiquitous connectivity, extended device integration, reliability, and security and privacy support. The proposed interconnection framework and the proposed protocol for the sensors have been exhaustively evaluated in the framework of the AIRE project, which is focused on patients with breathing problem. This evaluates for the proposed protocol the data aggregation mechanism level, Round-Trip delay Time, impact of the distance, and the impact of the security. It has been concluded that secure continuous monitoring is feasible with the use of the proposed YOAPY aggregation mechanisms and the capabilities from the proposed interconnection framework.

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IV. CONCLUSIONThis paper has examined the errors and their impact on the performance of a future-trajectory-based CCWS. This paper follows a statistical approach to characterize the prediction error, incorporate the communication-induced error, and then determine the probability of trajectory conflicts and the quality of the detection performance. Results with test data verify the Kalman-filter-based error statistics estimation and the statistical collision detection. Effects of communication reliability are also examined, and the system demonstrates the potentials of tolerating communication losses and delays. REFERENCES[1] P. Varaiya, "Smart cars on smart roads: Problems of control," IEEE Trans.Autom. Control, vol. 38, no. 2, pp. 195-207, Feb. 1993 IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS