Context-aware Services Research Articles

A Survey on Indoor Positioning Systems for IoT-Based Applications

The Internet of Things (IoT), as a pervasive paradigm, is becoming an integral part of the tech industry and academic research in recent years. It forms a ubiquitous heterogeneous network connecting humans and things. The basic premise is acquiring data from the environment with sensors and remote intelligent management via actuators. For IoT service providers, time and place are functional parameters. Whereas most IoT scenarios are in indoor spaces and GPS cannot fully cover them, applying an indoor positioning system (IPS) is necessary. Besides, indoor enabling technologies can leverage the capability of IoT in context-aware services. In this article, we aim to provide a panoramic view of IPSs and localization services with the centrality of IoT. First, we explain the main concepts and review the latest positioning methods, techniques, and technologies with IoT remarks. Then, we discuss technical implementation challenges and open issues with feasible solutions. Finally, we mentioned location-based services (LBSs), real IoT applications, and active vendors in the realm of positioning services. This article provides a real insight into LBSs in IoT for future research.

A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan

The great popularity of cloud services, together with the increasingly important aim of providing Internet context-aware services, has spurred interest in developing diverse agriculture applications. This paper presents a cloud-based service built by incrementally integrating state-of-the-art models of deep learning, photography, object recognition and the multi-functionalities of cloud services. This study consists of an object detection phase with a convolutional neural network (CNN) model, which involves enabling simultaneous image data gathered from drones. The experimental results show 97% accurate watermelon recognition. Our results also include a two-model comparison in the cloud-based service, with the main findings demonstrating the feasibility of developing accurate object recognition using a CNN model without the need for additional hardware. Finally, this study adopted a confusion matrix to validate the result with RetinaNet for recognizing images taken on the watermelon farm with an average precision in recognizing watermelon quantity of up to 98.8%.

Research on Internet of Things Context-Aware Information Fusion Technology for Smart Libraries

In the information age, the rapid innovation and development of information technology plays an important supporting role in the dissemination and utilization of information. As an important concentration of literature information and knowledge resources, the library also actively introduces various advanced technologies, optimizes the service model, and transforms to intelligence. This paper expounds the development status of smart library and context-aware technology at home and abroad, analyzes the application of context-aware technology in smart library, constructs a framework of smart library based on context awareness, and puts forward some suggestions for smart library’s context-aware service.

Contextual Awareness Service of Internet of Things User Interaction Mode in Intelligent Environment

In order to reduce the difficulty of the Internet of Things intelligent environment system to provide users with layout services and optimize the system structure, this paper proposes to design a context-aware service system based on the user interaction mode of the Internet of Things. This article first collected a large amount of data through the literature survey method, which laid a sufficient theoretical foundation for the subsequent research; then, using smart sensors, smart interaction technology, and context awareness methods is employed to design and build a context-aware service system based on the user interaction modality of the Internet of Things; in order to test the performance and effect of the situational awareness system, take the elderly home care behavior as the research object, collect the 28-day home behavior of the elderly as sample data, and divide it into 7-day data, 14-day data, 21-day data, and 28-day data, by adopting rule pruning demand reasoning algorithm and evidence theory algorithm to reason about the service demand of the elderly. Finally, the experimental results show that the rule-pruning-based service demand reasoning algorithm combined with the evidence theory algorithm optimizes the system, which can better perceive the situation of the elderly living alone and can well reason about the elderly’s service demand, which is of great significance for solving the life and safety problems of the elderly.

Uni-Messe: Unified Rule-Based Message Delivery Service for Efficient Context-Aware Service Integration

Rule-based systems, which are the typical technology used to realize context-aware services, have been independently implemented in various smart services. The challenges of these systems are the versatility of action, looseness, and the coding that is needed to describe the conditional branches. The purpose of this study was to support the realization of service coordination and smart services using context-aware technology by converting rule-based systems into services. In the proposed method, we designed and implemented the architecture of a new service: Unified Rule-Based Message Delivery Service (Uni-messe), which is an application-neutral rule management and evaluation service for rule-based systems. The core part of the Uni-messe proposal is the combination of a Pub/Sub and a rule-based system, and the proposal of a new event–condition–route (ECR) rule-based system. We applied Uni-messe to an audio information presentation system (ALPS) and indoor location sensing technology to construct concrete smart services, and then compared and evaluated the implementation to “if this then that” (IFTTT), which is a typical service coordination technology. Moreover, we analyzed the characteristics of other rule-based systems that have been serviced in previous studies and compared them to Uni-messe. This study shows that Uni-messe can provide services that simultaneously combine versatility, ease of conditional description, looseness, context independence, and user interface (UI), which cannot be achieved using conventional rule-based system services. By using Uni-messe, advanced heterogeneous distributed service coordination using rule-based systems and the construction of context-aware services can be performed easily.

Towards Convergence of AI and IoT for Smart Policing

With the fast growth of IoT and AI techniques, AIoT’s potential in creating and capturing business value is being increasingly acknowledged. AIoT is the practice of combining AI with IoT-based hardware to proactively predict what might happen and what action needs to be taken. However, research on this issue has been limited and what the key mechanisms for designing AIoT based context-aware services are in the real field is still underexplored. In this article, we bridge this gap by studying a case study of AIoT based context-aware system in law enforcement in terms of information system artifacts to extend our understating of the AIoT based system’s mechanisms for business applications. Our case study reveals a conceptual model that can ensure AIoT empowered context-aware services in a police field unit. The implications, limitations, and future applications of the development of AIoT empowered context-aware services are discussed.

User Integration in Two IoT Sustainable Services by Evaluation Grid Method

To meet the need for sustainable development, Taiwan has been spreading a network of micro-monitoring stations to measure the environmental quality in rivers and air to protect people from environmental pollution. As a result, more and more information technology companies develop Internet of Things (IoT) services for this job. However, most IoT services are screened out of the market because lacking design thinking. Therefore, including users’ desires in IoT products and services is a critical determinant for their survival in this permanently changing market. Thus, this study proposed a systematic framework to identify the users’ desires from different stakeholders to determine technological development. For this, we use the evaluation grid method (EGM) to explore the users’ desires by a series of in-depth interviews and visualize the user’s response as a hierarchical evaluation map of attraction. After that, an IoT prototype is built and used to capture the insightful feedback of respondents. Meanwhile, we adopt the minimum viable product (MVP) design principles to develop two prototypes that manage a wastewater treatment plant and household environment. Overall, this study proposes an applicable user integration procedure to help IT engineers develop the IoT for sustainable service. This study also confirms that the MVP method can help to accelerate user integration. We propose a service-oriented IoT architecture in technology development and develop a decision-making service of human dispatch in operating environmental facilities and a context-awareness service for environmental control.

CA-BPEL: A New Approach to Facilitate the Development and Execution of Context-Aware Service Orchestrations

CA-BPEL: A New Approach to Facilitate the Development and Execution of Context-Aware Service Orchestrations

QL-CBR Hybrid Approach for Adapting Context-Aware Services

A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in real situations, we propose a combination of the Q-learning and case-based reasoning (CBR) algorithms. We then analyze how the incorporation of CBR enables Q-learning to become more efficient and adapt to changing environments by continuously producing suitable services. Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices’ status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices’ features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

DeepContext: Mobile Context Modeling and Prediction Via HMMs and Deep Learning

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in &#x201C;Location X&#x201D;) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs&#x2014;two stochastic models based on the theory of Hidden Markov Models&#x00A0;(HMMs) and one model based on deep learning&#x2014;<i>personalized model</i> (<i>HPContext</i>), <i>collaborative filtering model</i> (<i>(HCFContext)</i>), and <i>deep learning model</i> (<i>DeepContext</i>) to eventually replace <i>HPContext</i>. <i>HPContext</i> and <i>DeepContext</i> predict the current context using sequential history of the user&#x0027;s own past context observations; while <i>HCFContext</i> enhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc. <i>DeepContext</i> models mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.

How Can Transactional Semantics Enhance the Commit Rate of Context-aware Service Composition in Advanced Pervasive Systems?

Context-aware composition of services exhibiting transactional properties poses several challenges. A major challenge is the transactional behavior of candidate services which is subject to perpetual change while the composition is running. Compositions of services displaying transactional properties must be dynamically adapted at run time to cope with context fluctuations. By dynamic adaptation, we refer to the ability to alter the composition behavior in response to changes affecting its execution. We focus on changes impacting the successful commit rate of transactional service composition. This has led us to explore the trail of a flexible homeomorphism between alternative behaviors. We propose a behavioral adaptation approach that adjusts the behavior of transactional compositions of services in a proactive and transparent manner. This strategy is based on the Profiled Task Class concept. A service composition generator has also been developed for the performance evaluation of components implementing the behavioral adaptation strategy in order to identify its impact on the commit rate of CATS compositions.

An area optimization based cooperative localization algorithm with node selection

In recent years, position information has become a key feature to drive location and context aware services in mobile communication. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, due to weak signals, multipath or non-line-of-sight signal propagation, accurately and efficiently localizing targets in harsh indoor environments remains a challenging problem. To improve the performance in harsh environment with insufficient anchors, cooperative localization has emerged. In this paper, a novel cooperative localization algorithm, named area optimization and node selection based sum-product algorithm over a wireless network (AN-SPAWN), is described and analyzed. To alleviate the high computational complexity and build optimized cooperative cluster, a node selection method is designed for the cooperative localization algorithm. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algorithms in the harsh indoor environments.

Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach

Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed.

A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. The system is thoroughly evaluated in terms of system throughput and processing time, revealing that the proposed system is efficient, robust, and scalable.

Dealing With Context Awareness for Service-Oriented Systems

In recent years, rapid advances in the enabling technologies for mobile and ubiquitous computing, software paradigms, embedded sensor technologies, and wide range of wired and wireless protocols have been witnessed. Specifically, context-aware services-oriented applications are emerging as the next computing paradigm in which infrastructure and services are seamlessly available. Context-awareness, being an important ingredient, plays a vital role in enabling such interactive smart environments. More recently, the increasing popularity of ontologies has led to new ontology-based models of context because of their potential to support sophisticated ontology-based reasoning methods. This paper presents an architecture for the development of context-aware services based on ontologies. The authors highlight the context metamodel and discuss about reasoning process. This research also presents the semantic approach for service adaptation in context aware environment.

Privacy protection among three antithetic-parties for context-aware services

The popularity of context-aware services is improving the quality of life, while raising serious privacy issues. In order for users to receive quality service, they are at risk of leaking private information by adversaries that are possibly eavesdropping on the data and/or by the untrusted service platform selling off its data to adversaries. Game theory has been utilized as a powerful tool to achieve privacy preservation by strategically balancing the trade-off between profit (service) and cost (data leakage) for the user. However, most of the existing schemes cannot fully exploit the power of game theory, as they fail to depict the mutual relationship between any two (of the three) parties involved: user, platform, and adversary. Existing schemes are also not always able to provide specific guidance for a user to reduce the impact of the joint threats from the platform and adversary. In this paper, we design a privacy-preserving game to quantify the three parties’ concerns and capture interactions between any two of them. We also identify the best strategy for each party at a fine-grained level, i.e. specific settings, not simply binary choices. We validate the performance of our proposed game model through both a theoretical analysis and experiments.

A smartphone-based multimodal indoor tracking system

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly integrated in most smartphones have already shown promising and better results than location solutions based on a single data source. In this paper, we present a multi-sensor tracking system built by incrementally integrating state-of-the-art models of the Wi-Fi interface and the accelerometer, gyroscope and magnetometer sensors of a smartphone. Our proposal consists of a simple calibration phase of the tracking system, which involves enabling simultaneous data gathering from all three sensors and the Wi-Fi interface. Taking the Wi-Fi signal model as baseline, four different configurations are evaluated by incrementally adding and integrating the models of the other three sensors. The experimental results reveal a mean error accuracy of 60 cm in the case when the tracking system makes use of all four data sources. Our results also include a spatial characterization of the accuracy and processing power requirements of the proposed solution. Our main findings demonstrate the feasibility of developing accurate localization indoor tracking systems using current smartphones without the need for additional hardware.

An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

A Context Aware Job Information Real-Time Delivery Framework for Pervasive Environment

Context-aware systems are getting rapid popularity towards enabling a pervasive computing environment over hand-held mobile devices, wearable devices, and Tablets. These systems deliver services tailored to the specific needs and user’s context. The context-aware system services can utilize information about the user’s context to adapt services based on the user’s location. The prime aim of this research paper is to investigate and analyse the critical issues and challenges while delivering the job information to the job seekers when they migrate from one place to another in search of jobs. In addition, the research also tries to investigate the challenges faced by employers/ job providers when they search and recruit the right person for the right jobs. In this paper the issues and challenges investigated and analysed were timeliness of information dissemination, opportunity missing, fake/genuine information, anywhere, anytime accessibility over small handheld devices. After rigorous analysis of the investigated issues and challenges in the existing state of art employment/job information dissemination systems, a real-time context-aware employment information delivery system framework for the pervasive environment is proposed. The research parameters considered are Preference of Job seekers, Location of Jobs, Context, Qualification, CGPA, Experience, everywhere access at any time, and Identity. The paper used both the quantitative and qualitative approaches for proposing a research-based applied solution. Survey of job seekers, Interview of recruiters, and technical observation of the researcher are used for collecting the relevant facts to check the researchability of the issues and challenges. Protégé version 4.3 is used for modelling, Edraw-Max for designing the framework, Google Form for Survey, and Justin mind tool for Prototype design and testing. The framework and its Prototype was examined using a user acceptance test. The acceptance test indicated that 84.6% of target stakeholders accepted the proposed system framework as a new knowledge solution for the aforementioned issues and challenges in the employment sectors