Context-aware Computing Research Articles

Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities

Monitoring domestic activities helps us to understand user behaviors in indoor environments, which has garnered interest as it aids in understanding human activities in context-aware computing. In the field of acoustics, this goal has been achieved through studies employing machine learning techniques, which are widely used for classification tasks involving sound recognition and other objectives. Machine learning typically achieves better performance with large amounts of high-quality training data. Given the high cost of data collection, development datasets often suffer from imbalanced data or lack high-quality samples, leading to performance degradations in machine learning models. The present study aims to address this data issue through data augmentation techniques. Specifically, since the proposed method targets indoor activities in domestic activity detection, room transfer functions were used for data augmentation. The results show that the proposed method achieves a 0.59% improvement in the F1-Score (micro) from that of the baseline system for the development dataset. Additionally, test data including microphones that were not used during training achieved an F1-Score improvement of 0.78% over that of the baseline system. This demonstrates the enhanced model generalization performance of the proposed method on samples having different room transfer functions to those of the trained dataset.

DeepIOD: Towards A Context-Aware Indoor-Outdoor Detection Framework Using Smartphone Sensors.

Accurate indoor-outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98-99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor-outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.

Efficient context-aware computing: a systematic model for dynamic working memory updates in context-aware computing.

The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper's main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing.

WIFI based human activity recognition using multi-head adaptive attention mechanism

Human Activity Recognition (HAR) utilizing Channel State Information (CSI) extracted from WiFi signals has garnered substantial interest across various domains and applications. This field’s potential paths and applications extend beyond CSI-based HAR and include smart homes, assisted living, security, gaming, surveillance, and context-aware computing. The ability of deep learning algorithms to effectively process and interpret CSI data opens up new possibilities for accurate and robust human activity recognition in real-world scenarios. However, traditional Recurrent Neural Networks (RNN) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), rely solely on their internal memory cells to maintain information over time. Important details might be diluted or lost within the memory cells in complex CSI sequences. To address this limitation, we propose a lightweight approach that incorporates a multi-head adaptive attention weight mechanism MHAAM into the HAR framework. The multi-head attention mechanism allows the model to attend to different informative patterns within the CSI data simultaneously, capturing fine-grained temporal dependencies and improving the model’s ability to recognize complex activities. The implemented models effectively filter out noise and irrelevant information by assigning higher weights to informative CSI features, further enhancing activity classification accuracy. Experimental evaluations and comparative analyses of HAR for seven activities demonstrate that attention-based RNN models with multi-head attention consistently outperform traditional RNN models. The multi-head attention mechanism achieves improved generalization and testing for seven common human activities and environments, leading to a higher complex human activity classification accuracy of up to 98.5%.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Advanced Context-aware Computing for Human Machine Interaction in Consumer Electronics

Advanced Context-aware Computing for Human Machine Interaction in Consumer Electronics

TextureSight

Objects engaged by users' hands contain rich contextual information for their strong correlation with user activities. Tools such as toothbrushes and wipes indicate cleansing and sanitation, while mice and keyboards imply work. Much research has been endeavored to sense hand-engaged objects to supply wearables with implicit interactions or ambient computing with personal informatics. We propose TextureSight, a smart-ring sensor that detects hand-engaged objects by detecting their distinctive surface textures using laser speckle imaging on a ring form factor. We conducted a two-day experience sampling study to investigate the unicity and repeatability of the object-texture combinations across routine objects. We grounded our sensing with a theoretical model and simulations, powered it with state-of-the-art deep neural net techniques, and evaluated it with a user study. TextureSight constitutes a valuable addition to the literature for its capability to sense passive objects without emission of EMI or vibration and its elimination of lens for preserving user privacy, leading to a new, practical method for activity recognition and context-aware computing.

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

In recent years, safety accidents in university laboratories have occurred frequently. Not only do the accidents result in property damage, but also in injuries. Real-time environmental monitoring of the laboratory through IoT enables early detection of potential safety risks such as high temperatures, high humidity and gas leaks, and timely action to reduce the likelihood of accidents. To ensure laboratory safety, in the paper, an emergency treatment mechanism for laboratory safety accidents was proposed based on IoT and context perception. The mechanism uses sensors to collect environmental information and fill a feature characterization architecture for unified safety management. Subsequently, the meta-rule algorithm is used to discover services in the prior knowledge model to form a workflow engine, so as to drive the security business management. Additionally, based on the standard measurement model, we normalize the fuzzy uncertainty measurement model with different granularities and define the fuzzy uncertainty of different emergency decision-making knowledge. Based on this, a knowledge fusion method for emergency decision-making under different fuzzy uncertainties is proposed, which improves laboratory safety emergency response performance based on situational awareness. The implementation of the proposed mechanism in a chemical laboratory demonstrates its efficacy in optimizing operational processes and discovering operational flow through multi-dimensional information analysis. This capability significantly aids safety administrators in their daily laboratory safety management.

A futuristic green service computing approach for smart city: A fog layered intelligent service management model for smart transport system

With the widespread adoption of technologies like the Internet of Things (IoT), context awareness, and decentralized computing at the edge of the network, service delivery in smart city parlance has been rapidly evolved. Management of Information and Communication Technologies (ICT) infrastructure for such dynamic environment at scale brings about new challenges. Existing fog layer resource management involves context-sharing and migration for real-time vertical and cross-vertical services. However, improper context migration might affect performance negatively. In this paper, the authors have envisioned improving the Quality of Service (QoS) of smart transportation while employing context-aware computing and Artificial Intelligence, which helps alleviate massive data transfers among Fog nodes and intelligent vehicles in real-time. The proposed Context-Aware Intelligent Transportation System (CAITS) manages the services of intelligent vehicles and manages the road traffic of traditional vehicles in an effective manner, with a three-layered learning model that accounts for on-vehicle, on-co-vehicle, and on-fog-and-vehicle learning by means of a platoon control algorithm as well as federated learning at the Fog level. Simulations are carried out on CloudSim simulator under different scenarios and the results demonstrate that the proposed scheme improves prediction efficacy of contexts at the Fog layer by approximately 8%–24% than existing models which in turn reflects in reduced service time and energy consumption of EVs and reduces the CO2 of environment.

A Secure Convergence of Data Transmission and Intelligent Authentication Using a Sensing Approach for the Internet of Thing

The IoT’s millions of sensor-equipped gadgets upload data to computers for management and use in smart grids, intelligent transportation networks, healthcare networks, and smart cities. Sensor data needs a safe server-sensor connection. Studying altered data could have catastrophic consequences. Internet-of-Things sensors must authenticate with the reader and base station before sending data. IoT sensors can securely and effectively communicate data. Correction evidence in the suggested technique reveals that the required data is created at the receiver using the normal Euclidean parameters of IoT sensors. The proposed approach is compatible with most assaults, making it a good security option. A framework for edge IoT devices can optimize latency, security, and other performance characteristics. Constraints Application Protocol (COAP) and The Stream Control Transmission Protocol (AESCQTTP) combine cooperative resource management, machine learning, context-aware computing, and flexible infrastructure to handle communication and computing difficulties. To reduce end-to-end latency by (7.13–7.35)%, raise security by 98.99%, and increase efficient pocket distribution to 98%. This study examined existing research issues and edge-computing technologies and proposed a novel strategy for optimizing edge-IoT system performance metrics. This arrangement could let future networks communicate securely.

Impacts on Context Aware Systems in Evidence-Based Health Informatics: A Review.

Background: The application of Context Aware Computing (CAC) can be an effective, useful, feasible, and acceptable way to advance medical research and provide health services. Methods: This review was conducted in accordance with the principles of the development of a mixed methods review and existing knowledge in the field via the Synthesis Framework for the Assessment of Health Information Technology to evaluate CAC implemented by Evidence-Based Health Informatics (EBHI). A systematic search of the literature was performed during 18 November 2021–22 January 2022 in Cochrane Library, IEEE Xplore, PUBMED, Scopus and in the clinical registry platform Clinicaltrials.gov. The author included the articles in the review if they were implemented by EBHI and concerned with CAC technologies. Results: 29 articles met the inclusion criteria and refer to 26 trials published between 2011 and 2022. The author noticed improvements in healthcare provision using EBHI in the findings of CAC application. She also confirmed that CAC systems are a valuable and reliable method in health care provision. Conclusions: The use of CAC systems in healthcare is a promising new area of research and development. The author presented that the evaluation of CAC systems in EBHI presents positive effects on the state of health and the management of long-term diseases. These implications are presented in this article in a detailed, clear, and reliable manner.

Survey on Context Aware Computing and IoT Environment

Context awareness enables the system to collect the information about the environment at any time and act according to it. It makes use of hardware as well as software to collect and analyze the data automatically. The mobile users particularly the smart phone users are mostly focused by context due to it’s pliability and is often used as a part of ubiquitous and wearable computing. The Internet and upcoming technologies combine to transform every object into smart objects. Where this can be applied to user’s activities on the device or to the physical environment in which the device is being use. Here we survey the context awareness from an IoT perspective to help users by automating the task of selecting the sensors according to the problems/tasks at hand.

Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.

Intelligent assistive technology devices for persons with dementia: A scoping review

ABSTRACT Assistive technology (AT) with context-aware computing and artificial intelligence capabilities can be applied to address cognitive and communication impairments experienced by persons with dementia (PwD). This paper aims to provide an overview of current literature regarding some characteristics of intelligent assistive technology devices (IATDs) for cognitive and communicative impairments of PwD. It also aims to identify the areas of impairment addressed by these IATDs. A multi-faceted systematic search strategy yielded records. Predefined criteria were applied for inclusion and data extraction. Thereafter data was thematically analysed and synthesised. This review demonstrates that almost all of the research involving IATDs has focused on cognitive impairments of PwD and has not yet evolved past the conceptual or prototype stages of development. Summaries of commercially available IATDs for PwD and relevant prototypes are provided at the end of this review. This research concluded that IATDs for PwD targeting cognition and communication problems primarily focus on social robots, and that they address cognitive impairments of attention, affect, and social-pragmatic communicative impairments. Future research endeavours concerning AT for PwD should explore collaboration between computer engineering and health practitioners to address the identified gaps. This may contribute to the available information for evidence-based decision making for PwD.

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.

Advancing Software-Defined Service-Centric Networking Toward In-Network Intelligence

Recent advances separately in edge intelligence, service-centric networking, and software-defined networking have profoundly impacted future networks and intelligent applications. Nevertheless, they have not been jointly considered. Jointly considering these three remarkable techniques will open up a nascent research direction. In this article, we propose a software-defined service-centric networking (SDSCN) framework to push the frontier of edge intelligence toward general in-network intelligence. We devise a three-plane architecture, including a data plane, a management plane, and a control plane. Specifically, in-network context-aware computing and caching capabilities are incorporated into the service-centric data plane. Network programmability and global orchestration provide the potential to design plenty of useful network applications to customize network behaviors flexibly. How in-network federated learning works in SDSCN can be observed from case studies and is validated by emulation. More simulation results show the outperformance of SDSCN. Several challenges in developing the proposed framework are presented and discussed.

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-Aware Computing and Nature-Inspired Computing

The part of this special issue with the title of “Context-aware Computing and Nature-inspired Computing: ICCASA and ICTCC 2020” edited by Prof. Phan Cong Vinh is presented to scientists, researchers, experts and students in the fields of context-aware computing and nature-inspired computing. Hopefully, they will find this part stimulating their research related to the hot topics of contextual cognitive computing and nature-inspired computing and being useful for their future work.