Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

Putting human behavior predictability in context

Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shopping and online behaviors. However, the existing researches have been often limited to a single or to the combination of few behavioral dimensions, and they have adopted the perspective of an outside observer who is unaware of the motivations behind the specific behaviors or activities of a given individual. The key assumption of this work is that human behavior is deliberated based on an individual’s own perception of the situation that s/he is in, and that therefore it should also be studied under the same perspective. Taking inspiration from works in ubiquitous and context-aware computing, we investigate the role played by four contextual dimensions (or modalities), namely time, location, activity being carried out, and social ties, on the predictability of individuals’ behaviors, using a month of collected mobile phone sensor readings and self-reported annotations about these contextual modalities from more than two hundred study participants. Our analysis shows that any target modality (e.g. location) becomes substantially more predictable when information about the other modalities (time, activity, social ties) is made available. Multi-modality turns out to be in some sense fundamental, as some values (e.g. specific activities like “shopping”) are nearly impossible to guess correctly unless the other modalities are known. Subjectivity also has a substantial impact on predictability. A location recognition experiment suggests that subjective location annotations convey more information about activity and social ties than objective information derived from GPS measurements. We conclude the paper by analyzing how the identified contextual modalities allow to compute the diversity of personal behavior, where we show that individuals are more easily identified by rarer, rather than frequent, context annotations. These results offer support in favor of developing innovative computational models of human behaviors enriched by a characterization of the context of a given behavior.

A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces

The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.

An upcycled IoT

An upcycled approach uses everyday objects as design material for IoT systems by enabling users to make their "dumb" objects "smart." Adopting this approach, IoT Codex realizes a new socially informed, context-aware computing and end-user programming.

CONTEXT STORAGE USING CLOUD-BASED MONGODB WITH RULE-BASED RETE ALGORITHM

In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day. The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..

Towards Dynamic Checklists

The goal of this in-the-wild study was to understand how different patient, provider, and environment contexts affected the use of a tablet-based checklist in a dynamic medical setting. Fifteen team leaders used the digital checklist in 187 actual trauma resuscitations. The measures of checklist interactions included the number of unchecked items and the number of notes written on the checklist. Of the 10 contexts we studied, team leaders’ arrival after the patient and patients with penetrating injuries were both associated with more unchecked items. We also found that the care of patients with external injuries contributed to more notes written on the checklist. Finally, our results showed that more experienced leaders took significantly more notes overall and more numerical notes than less experienced leaders. We conclude by discussing design implications and steps that can be achieved with context-aware computing towards adaptive checklists that meet the needs of dynamic use contexts.

Particle Swarm Optimization in the Presence of Malicious Users in Cognitive IoT Networks with Data

With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) is gaining importance. The study to efficiently exploit and manage a spectrum resources for industrial IoT (IIoT) applications is currently in the interest of research community. As increasing number of IIoT devices is heading towards the future-connected society with the cost of high system complexity, to meet the growing demands of wireless communication in future, cognitive IoT (CIoT) technology is considered as a choice. Reliable detection of the vacant spectrum holes is a vital task in the CIoT network with data. However, the performance of spectrum sensing severely degraded with the existence of malicious users (MUs) which falsifies the sensing results by reporting false data to the fusion center (FC). In this paper, we focus on the use of particle swarm optimization (PSO) to safeguard the cooperative spectrum sensing (CSS) from the negative effects caused by the MUs. The effectiveness of the proposed scheme is verified numerically in various scenarios with different types of MUs through analysis and simulations.