Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare (mHealthcare) services. A patient within the hospital is monitored by several devices. Moreover, upon leaving the hospital, the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters. This raises potential challenges for intelligent monitoring of patient's health. In this paper, an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles. The HCI also provides the support for the User-Centric Design (UCD) for smart mHealthcare models. Furthermore, the HCI along with IoT`s (Internet of Things) 5-layered architecture has the potential of improving User Experience (UX) in mHealthcare design and help saving lives. The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical, behavioral, and health-related information. Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant. The HCI design focuses on the ease-of-use, including user experience and safety, alarms, and error-resistant displays of the end-user, and improves user's experience and user satisfaction.

No abstract

WSN (Wireless Sensor Network) comprises of small-sized and constraint-capability SN (Sensor Nodes) which record, send and receive data, sensed to a sink. The network lifetime and energy usability are important challenges to be dealt with. During the working of the SN, the maximum amount of energy is consumed than sensing and processing of data. Therefore, an efficient transmission of the data is required so that the energy can be saved. In this paper, a novel routing and scheduling method for WSNs using GA (Genetic Algorithm) is presented, where the sinks employed on four sides of the sensor field. These sinks collect the data from the SNs having the optimal distance towards the respective sink. The proposed scheme finds the optimized path using GA, during transmission of data from SN to the nearest sink. First, we run the GA for determination of routing paths, where a source SN finds the possible number of optimal hops. Second, the hops or intermediate relay SNs are assumed to relay the data towards the sink, efficiently. The performance is experimented and evaluated using MATLAB R2016b. The simulations have carried out through comparing the proposed scheme with TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol). The results of simulation comprise of 10 and 20 number of SNs, discretely. Additionally, the direct distance of each node is calculated and the distance through multiple hops from/to the nearest sink is also evaluated. The achievements of the proposed technique are to save both energy and distance for the sake of network longevity and optimal and precise data delivery by multiple hops.