WORK DONE ON MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION

Runtime detection of contextual properties is one of the primary approaches to enabling context-awareness in pervasive computing scenarios. Among various properties the applications may specify, the concurrency property, i.e., property delineating concurrency among contextual activities, is of great importance. It is because the concurrency property is one of the most frequently specified properties by context-aware applications. Moreover, the concurrency property serves as the basis for specification of many other properties. Existing schemes implicitly assume that context collecting devices share the same notion of time. Thus, the concurrency property can be easily detected. However, this assumption does not necessarily hold in pervasive computing environments, which are characterized by the asynchronous coordination among heterogeneous computing entities. To cope with this challenge, we identify and address three essential issues. First, we introduce logical time to model behavior of the asynchronous pervasive computing environment. Second, we propose the logic for specification of the concurrency property. Third, we propose the Concurrent contextual Activity Detection in Asynchronous environments (CADA) algorithm, which achieves runtime detection of the concurrency property. Performance analysis and experimental evaluation show that CADA effectively detects the concurrency property in asynchronous pervasive computing scenarios.

Earlier work of P. Gacs and J. Reif (see J. Comput. Syst. Sci., vol.36, no.2, p.125-147 (1988)) on reliable computation using cellular automata is extended to asynchronous cellular automata. The goal is to find ways to implement computations of arbitrary size by a homogeneous asynchronous array of unreliable elementary components. An asynchronous two-dimensional cellular automaton is constructed so that given any computation and reliability requirement, a program can be found for such an automaton that performs the computation with probability that meets the reliability requirement. This is the strongest among the published results on reliable computation in an asynchronous environment. It is stronger than its asynchronous counterpart in the sense that it removes the assumption of a fault-free global synchronization clock underlying a synchronous system. >

Computer mobility allows people to use computers in varied and changing environments. This variability forces applications to adapt thus requiring awareness of the computational and physical environment (e.g. information about power management, network connections, synchronization opportunities, storage, computation, location-based services, etc.). An important application for mobility is hoarding, i.e. automatic file replication between devices. To be accurate and not obstructive to the user, the hoarding mechanism requires both context awareness (e.g. amount of usable storage) and estimation of future environment conditions (e.g. network connection, tasks to be performed by the user in the near future, etc.). However, making applications context-aware is hindered by the complexity of dealing with the large variety of different modules, sensors and service platforms, i.e. there is no middleware supporting such applications and their development in a uniform and integrated way. This paper presents the architecture for an environment awareness system (EAS) and how it applies to hoarding. EAS is a middleware component that acts as an intermediary between applications and all mechanisms that assess the surrounding environment. It lets applications query and combine environment properties in a standardized way. Crucial for the success of automatic file hoarding is the EAS's capability of supporting environment prediction based on simple reasoning and pattern detection. Thus, applications may advise users accordingly or even make decisions on their behalf.

More than a decade ago, pervasive computing and context awareness where envisioned as the future of computing [16], initial work concentrating on location, typically indoor. Today, small, handheld computers of various forms and purposes are becoming pervasive in the form of PDAs, mobile phones, and increasingly advanced GPS units. However, except for location based services, like knowing your location based on GPS, context awareness has not really materialised yet.In modern agriculture, computers are pervasive, but only in the sense that they are present everywhere. All types of equipment, ranging from feeding- and ventilation systems to tractors have build in computers, and, in most cases, can also be queried or controlled remotely. These systems provide an excellent base for gathering context, which may then be exploited to ease the work of the farmer. Furthermore, additional sensors may collect context about the individual agricultural worker, e.g. in the form of location or current activity. Knowing the context of the farm and the workers may then be utilised for building pervasive computing applications to support the daily work at farms, e.g. by easing access to information which is useful in the current context, or even automatic registration of work carried out.In this paper, we look at the challenges for building a middleware which supports collection and sharing of context from the heterogeneous sources and environments found at a farm as well as context reasoning, and design a middleware to overcome these challenges, using semantic technologies. We also study new ways of designing such middleware, in that we make the end user of the applications built with the middleware participate in the requirement gathering and design phases.

We are witnessing a significant expansion and penetration of wireless appliances, sensors, smart phones, and PDAs in a wide range of domains such as hospitals, healthcare, smart homes, and elderly home care. Healthcare applications are mission-critical, real-time, and are used in a complex socio-technical system that requires collaboration, communication and coordination between many actors (doctors, patients, nurses, lab technicians, administration, etc.) and devices in the system, depending on their context. This paper introduces a framework for supporting context awareness, which is built from available open source components. The paper illustrates how the framework can be used in the healthcare domain with the help of an emergency department application.

This paper discusses the issue of ambient intelligence system for agri-food application. A framework for agri-food supply chain management is designed with context awareness support. The context ontology of main elements including user, environment, service and platform, are modeled and discussed in detail. A system materialization case for warehouse management has been shown including the devices, functionality and relevant procedures. The implementation case applied in agri-food warehouse management is introduced with the details about its design and systematic materialization from devices to network system. Considering safety in management involved with authentication, secure context-aware strategy is designed with jointly mutual authentication to guarantee the information security for interaction with the management procedures.

The concept of aware systems is among the most exciting trends in computing today, fueled by recent developments in pervasive computing, including new computers worn by users, embedded devices, smart appliances, sensors, and varieties of wireless networking technology. Context-Aware Pervasive Systems: The Architecture of a New Breed of Applications introduces a diverse set of application areas and provides blueprints for building context-aware behavior into applications.Reviewing the anatomy of context-aware pervasive applications, this resource covers abstract architecture. It examines mobile services, appliances, smart devices, software agents, electronic communication, sensor networks, security frameworks, and intelligent software agents. The book also discusses the use of context awareness for communication among people, devices, and software agents and how sensors can be aware of their own situations. Exploring the use of physical context for controlling and enhancing security in pervasive computing environments, this guide addresses mirror worlds and elucidates design perspectives based on a declarative programming language paradigm.This carefully paced volume presents a timely and relevant introduction to the emergence of context-aware systems and brings together architectures and principles of context-aware computing in one source.

With the rapid development of the information technology, it is inevitable that the distributed mobile computing will evolve to the pervasive computing gradually whose final goal is fusing the information space composed of computers with the physical space in which the people are working and living in. To achieve this goal, one of the problems is how to continuously monitor/capture and interpret the environment related information efficiently to assure high context awareness. Many attentions have been paid to the research of the context-aware pervasive applications. However, most of them just use the raw context directly or take the Quality of Context (QoC) into account in just one or two aspect. Therefore, we propose a agent based quality management middleware to support QoC management through various layers. By the agents, we can configure different strategies to refinery raw context, discard duplicate and inconsistent context so as to protect and provide QoS-enriched context information of users to context-aware applications and services.

The advent of Large Language Models (LLMs), including tools like GitHub Copilot and OpenAI Codex, has brought substantial changes to the field of software engineering. These technologies support developers through features such as automated code generation, smart code suggestions, and productivity enhancements. Despite these advancements, the development workflow is still scattered across multiple standalone tools used for coding, testing, documentation, and team communication. This lack of integration disrupts the development flow and negatively impacts overall team efficiency. To address these challenges, this paper proposes the AI-Driven Developer Ecosystem (AIDE)—a comprehensive development framework that harnesses the capabilities of LLMs while addressing gaps in tool interoperability and contextual awareness. AIDE functions as a unified, intelligent development environment that offers AI-assisted coding, predictive insights for continuous integration and deployment (CI/CD), automated issue classification, adaptive system architecture analysis, and harmonized documentation tools. AIDE sets itself apart from conventional development environments by providing continuous, context-aware support. It does this by analyzing real-time code changes, historical data, and team collaboration behavior. The platform also integrates collaborative tools such as Excalidraw for visual planning and embedded communication features for real-time coordination, promoting a deeply collaborative development experience that extends beyond code writing. By drawing from current academic research and industry practices, this paper illustrates how AIDE effectively addresses critical issues in intelligent software development, resulting in better code quality, minimized downtime, and increased developer satisfaction.

With the great progress of technologies, computers are embedded into everywhere to make our daily life convenient, efficient and comfortable. Ubiquitous society became so real for us with this fast development. However, how to integrate distributed resources existed among heterogeneous devices to build pervasive applications that can constantly adapt to the highly dynamic computing environment is still a challenge. On our previous work, by analyzing the pipelining feature within a user task and exploiting the parallelism among ubiquitous processors, we proposed UMP-PerComp, a ubiquitous multiprocessor-based pipeline processing architecture to support high performance pervasive application development. In this research, we evaluated the network transmission speed of UMP system to reduce the network overhead and provided a method to design the best performance UMP system. Our experience revealed that the data size of transmission in UMP network plays an important rule which highly influence the performance of the whole UMP system. Keywords: Ubiquitous Computing, Context Awareness, Pipeline Processing, Performance Evaluation.

Mobile devices have received much research interest in recent years. Mobility raises new issues such as more dynamic context, limited computing resources, and frequent disconnections. A middleware infrastructure for mobile computing must handle all these issues properly. In this project we propose a middleware, called 3DMA, to support mobile computing. We introduce three requirements, distribution, decoupling and decomposition as central issues for mobile middleware. 3DMA uses a space based middleware, which facilitates the implementation of decoupled behavior and support for disconnected operation and context awareness. This is done by defining a set of “workers” which are able to act on the users behalf either: to reduce load on the mobile device, and/or to support disconnected behavior. In order to demonstrate aspects of the middleware architecture we then consider the development of a commonly used mobile application.

A distributed computing model called "fog computing" provides cloud-like services which is closer to end devices, and is rapidly gaining popularity. It offers cloud-like computing including storage capabilities, but with less latency and bandwidth requirements, thereby improving the computation capabilities of IoT devices and mobile nodes. In addition, fog computing offers advantages such as support for context awareness, scalability, dependability, and node mobility. Fog computing is frequently used to offload tasks from end devices' applications, enabling quicker execution utilizing the fog nodes' capabilities. Because of the changing nature of the fog environment, task offloading is challenging and the multiple QoS criteria that depend on the type of application being used. This article proposes an SDN-based offloading technique to optimize the task offloading technique for scheduling and processing activities generated by the Internet of Space Things (IoST) devices. The proposed technique utilizes Software-Defined Networking (SDN) optimization to dynamically manage network resources and to facilitate the deployment and execution of offloaded tasks. To model the system which computes the optimal virtual machines (VM) to be allocated in the fog network in order to actively process the offloaded tasks, the GI/G/r queueing model is utilised. This approach minimizes the delay-sensitive task queue and minimises the necessary number of VMs while minimising the waiting time for the fog layer. The findings of the simulation are used to verify the effectiveness of the proposed model.

The increasing use of mobile devices and the dissemination of wireless networks have stimulated mobile and ubiquitous computing research. In this context, education is being considered one of the main application areas. New pedagogical opportunities are created through the use of location systems to track learners, and through context awareness support. This paper proposes a model to explore these opportunities using location information and context management as learning support tools. This model, called LOCAL, was conceived for small scale learning environments, but can be applied in large-scale as well. The model was implemented and the initial results show its utility to assist the teaching and learning processes.

CoWSAMI: Interface-aware context gathering in ambient intelligence environments

Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield a data set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confronted with the 4 V’s of big data (volume, velocity, variety and veracity), manual intervention may make ETL prohibitively expensive. This paper argues that providing cost-effective, highly-automated approaches to data wrangling involves significant research challenges, requiring fundamental changes to established areas such as data extraction, integration and cleaning, and to the ways in which these areas are brought together. Specifically, the paper discusses the importance of comprehensive support for context awareness within data wrangling, and the need for adaptive, pay-as-you-go solutions that automatically tune the wrangling process to the requirements and resources of the specific application.