Pervasive Computing Applications Research Articles

Comparative Analysis of Fall Detection Using Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) Algorithms

Fall detection is a critical area of research due to its implications for public health, particularly among older adults more vulnerable to fall-related injuries. This study explores the application of pervasive computing and machine learning algorithms in detecting falls using accelerometer and gyroscope sensors. Leveraging a dataset obtained from smartphone sensors, the study employs data processing techniques and classification algorithms, including K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN), to differentiate fall events from other activities. Evaluation metrics such as accuracy, precision, recall, and F1-score are utilized to assess the performance of the models. The results demonstrate the effectiveness of both the ANN and KNN models in accurately predicting fall detection with high precision and recall scores. The study highlights the potential of pervasive computing systems and machine learning methods to enhance fall detection capabilities, contributing to the development of proactive measures for ensuring the safety and well-being of individuals at risk of falls. Future research directions include exploring alternative algorithms and sensor modalities to improve fall detection systems further.

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes’ performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete’s Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes’ feedback interventions. This framework has the potential to revolutionize how athletes’ performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine.

From programming-to-modeling-to-prompts smart ubiquitous applications

Since its introduction by Mark Weiser, ubiquitous computing has received increased interest in the dawn of technological advancement. Supported by wireless technology advancement, embedded systems, miniaturization, and the integration of various intelligent and communicative devise, context-aware ubiquitous applications actively and intelligently use rich contextual information to assist their users. However, their designs are subject to continuous changes imposed by external factors. Nowadays, software engineering, particularly in the fields of Model-Driven Engineering, displays a strong tendency towards developing applications for pervasive computing. This trend is also fueled by the rise of generative artificial intelligence, paving the way for a new generation of no-code development tools and models specifically trained on open-source code repositories to generate applications from their descriptions. The specificities of our approach lies in starting with a graphical model expressed using a domain-specific language (DSL) composed of symbols and formal notations. This allows for graphically instantiating and editing applications, guiding and assisting experts from various engineering fields in defining ubiquitous applications that are eventually transformed into peculiar models. We believe that creating intelligent models is the best way to promote software development efficiency. We have used and evaluated recurrent neural networks, leveraging the recurrence of processing the same contextual information collected within this model, and enabling iterative adaptation to future evolutions in ubiquitous systems. We propose a prototype instantiated by our meta-model which tracks the movements of individuals who were positive for COVID-19 and confirmed to be contagious. Different deep learning models and classical machine learning techniques are considered and compared for the task of detection/classification of COVID-19. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach used a RNN architecture produced up to 92.1% accuracy. With the recent development of OpenAI Codex, optimized for programming languages, we provided the same requirements to the Codex model and asked it to generate the source code for the COVID-19 application, comparing it with the application generated by our workshop.

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.

Grand Challenges

The articles in this special section focus on new applications for pervasive computing.

A low-cost privacy preserving user access in mobile edge computing framework

The computational offloading from conventional cloud datacenter towards edge devices sprouted a new world of prospective applications in pervasive and Mobile Edge Computing (MEC) paradigm, leading to substantial gains in the form of increased availability, bandwidth with low latency. The MEC offers real-time computing and storage facility within the proximity of mobile user-access network, hence it is imperative to secure communication between end user and edge server. The existing schemes do not fulfill real time processing and efficiency requirements for using complex crypto-primitives. To this end, we propose a novel two-factor biometric authentication protocol for MEC enabling efficient and secure combination of Physically Unclonable Functions (PUFs) with user-oriented biometrics employing fuzzy extractor-based procedures. The performance analysis depicts that our scheme offers resistance to known attacks using lightweight operations and supports 30% more security features than comparative studies. Our scheme is provably secure under Real-or-Random (ROR) formal security analysis model.

Proactive tasks management for Pervasive Computing Applications

Current advances in the Internet of Things (IoT) and Edge Computing (EC) involve numerous devices/nodes present at both ‘layers’ being capable of performing simple processing activities close to end users. This approach targets to limit the latency that users face when consuming the provided services. The minimization of the latency requires for novel techniques that deliver efficient schemes for tasks management at the edge infrastructure and the management of the uncertainty related to the status of edge nodes during the decision making as proposed in this paper. Tasks should be executed in the minimum time especially when we aim to support real time applications. In this paper, we propose a new model for the proactive management of tasks’ allocation to provide a decision making model that results the best possible node where every task should be executed. A task can be executed either locally at the node where it is initially reported or in a peer node, if this is more efficient. We focus on the management of the uncertainty over the characteristics of peer nodes when the envisioned decisions should be realized. The proposed model aims at providing the best possible action for any incoming task. For such purposes, we adopt an unsupervised machine learning technique. We present the problem under consideration and specific formulations accompanied by the proposed solution. Our extensive experimental evaluation with synthetic and real data targets to reveal the advantages of the proposed scheme.

WITHDRAWN: Advanced implementation patterns of internet of things with MQTT providers in the cutting edge communications

In the current era, a number of devices and gadgets are communicating in real time environment using advanced wireless technologies. In addition, the implementations of smart cities, smart offices, smart home automation and many others are also in the agenda of government as well as corporate sectors. These interconnected things are generally associated with a specific term Internet of Things (IoT) in which enormous gadgets and smart objects communicate with each other on specific wireless protocols. IoT is also referred as Ubiquitous Computing or Pervasive Computing and many real time applications are under deployment using these technologies.

Privacy Enforced Access Control Model for Secured Data Handling in Cloud-Based Pervasive Health Care System

Health care is a significant application of pervasive computing. In recent years, the pervasive health care applications are replaced by cloud-based health care applications thus solving the problem of scalability and computing cost. In a cloud-based pervasive health care data security and privacy becomes a greater concern. The key contribution of this paper is to provide high privacy, data confidentiality, availability against the health data and this is achieved using the proposed access control model which uses a PR based approach for providing access control to various users of the system. Privacy rating (PR) is calculated for both the user and the data to provide access to any data that is being requested by the user. The results achieved shows that the proposed model achieves high level of privacy and security for the data stored in the healthcare system.

A High Precision, Wireless Temperature Measurement System for Pervasive Computing Applications.

This paper describes the design and calibration of a highly accurate temperature measurement system for pervasive computing applications. A negative temperature coefficient (NTC) thermistor with high resistance tolerance is interfaced through a conditioning circuit to a 12-bit digital converter of a wireless microcontroller. The system is calibrated to minimize the effect of component uncertainties and achieves an accuracy of ±0.03 °C on average (±0.05 °C in worst cases) in a 5 °C to 45 °C range. The calibration process is based on a continuous temperature sweep, while calibration data are simultaneously logged to reduce the delays and cost of conventional calibration approaches. An uncertainty analysis is performed to support the validity of the reported performance results. The described approach for interfacing the thermistor to the hardware platform can be straightforwardly adjusted for different thermistors, temperature ranges/accuracy levels/resolutions, and voltage ranges. The low power communication combined with the energy consumption optimization adopted enable an operation to be autonomic for several months to years depending on the application’s measurement frequency requirements. The system cost is approximately $45 USD in components, while its design and compact size allow its integration with extended monitoring systems in various pervasive computing environments. The system has been thoroughly tested and validated in a field trial concerning a precision agriculture application and is currently used in a health monitoring application.

Analysis of Direction on Product Design in the Era of the Internet of Things

Internet of things (IoT) as a new revolution in science and technology will bring new vitality to the design. IoT has become the third revolution of the world industry information development after the world experienced the computer and the Internet. With the development of industry information, IoT as Internet service and applications expand through intellisense, identification technology, pervasive computing and ubiquitous network convergence applications. IoT era is the era of intelligent products. This article elaborates on the aspects of design content, design objects, design procedures and methods, means of obtaining design requirements, design tools, and changes in design direction. In the era of IoT, industry designers should focus on the interaction between humans and smart products and the interaction between smart products.

Interactive Mobile Health Monitoring System

Health monitoring system is an active application in pervasive and ubiquitous computing. It is an application of mobile computing technology for enhancing communication among health care workers, physicians and patients with a view to provide better health care system. Recent elevation in sensors, wireless communication and low-power integrated circuits has empowered the design of pocket size, light weight, low-cost, and interactive bio-sensor nodes. These nodes are seamlessly integrated for mobile health monitoring using wireless body area network which can sense, process and communicate one or more vital parameters. The proposed system, through mobile device can provide patient health parameters (such as temperature, heart rate and ECG) to medical server, care taker and to medical practitioner based on the biomedical and environmental data collected by deployed sensors. In this system, multiple physiological parameters are incorporated for monitoring as against one or two parameters in legacy system. In this paper hardware, software and implementation of system is discussed whereas the focus is on authentication, power consumption, accuracy in transmission of health parameters to medical server.

Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.

ETS (Efficient, Transparent, and Secured) Self-healing Service for Pervasive Computing Applications

To ensure smooth functioning of numerous handheld devices anywhere anytime, the importance of self-healing mechanism cannot be overlooked. Incorporation of efficient fault detection and recovery in device itself is the quest for long but there is no existing self-healing scheme for devices running in pervasive computing environments that can be claimed as the ultimate solution. Moreover, the highest degree of transparency, security and privacy attainability should also be maintained. ETS Self-healing service, an integral part of our developing middleware named MARKS (Middleware Adaptability for Resource discovery, Knowledge usability, and Self-healing), holds promise for offering all of those functionalities.

Meta Model Context Based Space for Ubiquitous Computing

Pervasive computing is a paradigm that focuses on the availability of computer resources anytime anywhere for any application and supports integration of computing services into everyday life. Context awareness is the core feature of pervasive computing. High-level context awareness can be enhanced by situation awareness that represents the ability to detect and reason about the real-life situations. In this paper, in order to deal with the problem in context-aware modeling in pervasive computing environments, the authors present a comprehensive and integrated approach for context modeling. They first propose a Meta model context based on ontology for Pervasive Computing aiming firstly to overcome the limitations of the existing ontologies, and secondly extend its capabilities by adding new environmental aspects. They divide the context model into Meta Ontology level and Domain-specific Ontology level according to the abstraction hierarchy. The Meta Ontology is the high abstract level which extracting the basic elements of the context knowledge. The Domain-specific Ontology is the lower abstract lever which focusing on different domains knowledge, directed by the Meta Ontology. The advantage is that it can provide a flexible modeling mechanism for multiple applications of context-aware pervasive computing. A case study of HealthCare domain is given to illustrate the practicality of the authors' Model.

Continuous discovery of co-location contexts from Bluetooth data

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling.

Smart devices and spaces for pervasive computing

Applications and services for pervasive computing have been dramatically grown and have contributed extensively to our daily experiences in recent years. Smart systems, devices, and spaces are proactive for ubiquitous and pervasive computing. Smart information technology (IT) is also an outcome of the state of the art and novel mobile and ubiquitous computing technologies that include highly capable handheld device, pervasive and personal device, etc. This special issue will be a trigger for further related research and technology improvements in pervasive and ubiquitous computing using smart devices and services. This special issue called for original papers describing the latest developments, trends, and solutions of smart devices and spaces for pervasive computing including real-time operating systems (OS), tiny OS and middleware supports, mobile system performance, trustworthy Internet and communications, agents and mobile and pervasive services, among others. In particular, this special issue focuses on a remote control and media-sharing system, flash storage-based smart system, heterogeneous mobile OS, and prediction and auto-execution system for pervasive computing.

Microcontrollers as (In)Security Devices for Pervasive Computing Applications

Often overlooked, microcontrollers are the central component in embedded systems which drive the evolution toward the Internet of Things (IoT). They are small, easy to handle, low cost, and with myriads of pervasive applications. An increasing number of microcontroller-equipped systems are security and safety critical. In this tutorial, we take a critical look at the security aspects of today's microcontrollers. We demonstrate why the implementation of sensitive applications on a standard microcontroller can lead to severe security problems. To this end, we summarize various threats to microcontroller-based systems, including side-channel analysis and different methods for extracting embedded code. In two case studies, we demonstrate the relevance of these techniques in real-world applications: Both analyzed systems, a widely used digital locking system and the YubiKey 2 onetime password generator, turned out to be susceptible to attacks against the actual implementations, allowing an adversary to extract the cryptographic keys which, in turn, leads to a total collapse of the system security.

Binary and m-ary encoding in applications of tree-based genetic algorithms for QoS routing

Ubiquitous and Pervasive Computing (UPC) applications often have Quality of Service (QoS) requirements. These become constraints for the UPC network infrastructure. In this paper, we refer to Mobile ad Hoc Networks, one of the most important technologies supporting UPC, and investigate on Genetic Algorithms (GAs) for QoS routing. GAs are part of the soft computing paradigm and can solve the NP search of QoS routes with multiple constraints. We elaborate on tree-based GAs, which represent the set of paths from source to destination as a tree and encode them through the crossed junctions. While their most well-known applications use m-ary encoding representing single paths in the chromosomes, in this paper we discuss a binary encoding with the objective of improving the convergence speed. The binary encoding represents classes of paths in the chromosomes and allows local search on classes of paths. These classes are both collectively exhaustive and mutually exclusive. Simulation results compare convergence speed and scalability of GA applications with binary and m-ary encoding in networks with an increasing number of nodes and links per node. As the per-class processing is reason of additional computational cost, an hybrid GA application that uses both binary and m-ary encoding is introduced.

Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction

Trajectory prediction is widespread in mobile computing, and helps support wireless network operation, location-based services, and applications in pervasive computing. However, most prediction methods are based on very coarse geometric information such as visited base transceiver stations, which cover tens of kilometers. These approaches undermine the prediction accuracy, and thus restrict the variety of application. Recently, due to the advance and dissemination of mobile positioning technology, accurate location tracking has become prevalent. The prediction methods based on precise spatiotemporal information are then possible. Although the prediction accuracy can be raised, a massive amount of data gets involved, which is undoubtedly a huge impact on network bandwidth usage. Therefore, employing fine spatiotemporal information in an accurate prediction must be efficient. However, this problem is not addressed in many prediction methods. Consequently, this paper proposes a novel prediction framework that utilizes massive spatiotemporal samples efficiently. This is achieved by identifying and extracting the information that is beneficial to accurate prediction from the samples. The proposed prediction framework circumvents high bandwidth consumption while maintaining high accuracy and being feasible. The experiments in this study examine the performance of the proposed prediction framework. The results show that it outperforms other popular approaches.