Abstract
Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient’s health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based sematic ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the message queuing telemetry transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.
Highlights
Internet of Things is a trending technology contributing several solutions providing various applications and resources, leading to the rapid growth of industrial support in a smart way, by interconnecting devices to the internet
In order to address this challenging issue, this paper proposes a novel semantic-based service framework, Sensor-based Semantic (SeSem) framework, in which an end-user can subscribe for the intended physiological parameter of interest, for better understanding and monitoring of patient’s health status
In order to address the sensor-based semantification problem and heterogeneous sensed values, our work proposes a framework called Sensor-based Semantification (SeSem) framework, where Message Queuing Telemetry Transport (MQTT) protocol is used for sensor devices and end-user communication
Summary
Internet of Things is a trending technology contributing several solutions providing various applications and resources, leading to the rapid growth of industrial support in a smart way, by interconnecting devices to the internet. Few subscribers might not specify date and time, while subscribing for a particular sensor data, which might lead to the same issue of populating tremendous data of particular physiological parameter, with various date and time To overcome this issue, a deep neural network, with recursive type is applied to the matching subscribed data, which helps in providing the Last updated date and time service, to the end-users, making our health-care system an intelligent system. Their framework, overcomes the domain-specific methodologies improving the adaptability of classification algorithms in various applications.Jean Paul Bambanza [15], developed the iSEE system, a semantic sensor selection system, consisting of four layers namely, the application layer, User layer, ontology layer and systems layer This system enables the enduser to select IoT devices for a specific health-based scenario using Smart HealthCare (SHO) prototype along with Disease Ontology (DO). This sensor-based semantic interoperability issue can be solved by constructing ontology, which makes the sensed raw data into a meaningful format, providing interoperability among any kind of sensors under consideration
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