Showing papers on "Context awareness published in 2021"

Collision-free human-robot collaboration based on context awareness

Abstract: Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.

A graph-based context-aware requirement elicitation approach in smart product-service systems

Abstract: The paradigm of Smart product-service systems (Smart PSS) has emerged recently owing to the edge-cutting Information and Communication Technology (ICT) and artificial intelligence (AI) techniques. ...

Intermittent Contextual Learning for Keyfilter-Aware UAV Object Tracking Using Deep Convolutional Feature

Abstract: Visual tracking, one of the most favorable multimedia applications, has been widely used in unmanned aerial vehicle (UAV) for civil infrastructure monitoring, aerial cinematography, autonomous navigation, etc. Most existing trackers utilize deep convolutional feature to enhance tracking robustness in scenarios of various appearance variation. However, they commonly neglect speed which is crucial for UAV with restricted calculation resources. In this work, a novel correlation filter-based keyfilter-aware tracker with a new intermittent context learning strategy is proposed to efficiently and effectively alleviate the problems of background clutter, deficient description, occlusion, illumination change, etc. Specifically, context information is utilized to empower the filter higher discriminating ability through response repression of the omnidirectional context patches. Furthermore, keyfilter is produced from the periodically selected keyframe. The latest produced keyfilter is used to restrain the current filter's corrupted changes. Most importantly, context learning of correlation filter is implemented intermittently to fully increase the tracking efficiency. This intermittent learning strategy can ensure every filter maintain context awareness owing to the restriction of keyfilter, periodically enhancing the context awareness. Substantial experiments on three challenging UAV benchmarks totally with 213 image sequences have shown that our tracker surpasses the state-of-the-art results, and exhibits a remarkable generality in short-term and long-term UAV tracking tasks as well as a variety of challenging attributes.

Digital Twins for Intelligent Authorization in the B5G-Enabled Smart Grid

Abstract: Beyond fifth generation (B5G) communication networks and computation paradigms in the edge are expected to be integrated into power grid infrastructures over the coming years. In this sense, AI technologies will play a fundamental role to efficiently manage dynamic information flows of future applications, which impacts the authorization policies applied in such a complex scenario. This article studies how digital twins can evolve their context awareness capabilities and simulation technologies to anticipate faults or to detect cyber-security issues in real time, and update access control policies accordingly. Our study analyzes the evolution of monitoring platforms and architecture decentralization, including the application of machine learning and blockchain technologies in the smart grid, toward the goal of implementing autonomous and self-learning agents in the medium and long term. We conclude this study with future challenges on applying digital twins to B5G-based smart grid deployments.

Context-Aware Business Process Management

Abstract: Context awareness is essential for successful business process management (BPM). So far, research has covered relevant BPM context factors and context-aware process design, but little is known about how to assess and select BPM methods in a context-aware manner. As BPM methods are involved in all stages of the BPM lifecycle, it is key to apply appropriate methods to efficiently use organizational resources. Following the design science paradigm, the study at hand addresses this gap by developing and evaluating the Context-Aware BPM Method Assessment and Selection (CAMAS) Method. This method assists method engineers in assessing in which contexts their BPM methods can be applied and method users in selecting appropriate BPM methods for given contexts. The findings of this study call for more context awareness in BPM method design and for a stronger focus on explorative BPM. They also provide insights into the status quo of existing BPM methods.

Context-Aware Security for 6G Wireless The Role of Physical Layer Security.

Abstract: Sixth generation systems are expected to face new security challenges, while opening up new frontiers towards context awareness in the wireless edge. The workhorse behind this projected technological leap will be a whole new set of sensing capabilities predicted for 6G devices, in addition to the ability to achieve high precision localization. The combination of these enhanced traits can give rise to a new breed of context-aware security protocols, following the quality of security (QoSec) paradigm. In this framework, physical layer security solutions emerge as competitive candidates for low complexity, low-delay and low-footprint, adaptive, flexible and context aware security schemes, leveraging the physical layer of the communications in genuinely cross-layer protocols, for the first time.

Research on Context-Awareness Mobile Tourism E-Commerce Personalized Recommendation Model

Abstract: E-commerce personalized recommendation problem in social network based on context is of great realistic significance to users and merchants. Aiming at data sparsity and low precision of personalized recommendation in tourism E-commerce personalized recommendation model integrating multivariate social information, this paper integrates social information such as trust relationship between users, time and geographic position of commodity purchasing into traditional collaborative filtering recommendation mode based on users and proposed context-awareness mobile tourism E-commerce personalized recommendation model-MTERec, which digs interest and preference of users under different contexts, calculates weight of user interest from perspective of mobile environment context where the user is located and finally refers to idea recommended by collaborative filtering recommendation to realize rating prediction of users to commodities and recommend according to interest and preference of the users. Experimental results indicate that compared with existing similar algorithms, context- awareness mobile tourism E-commerce personalized recommendation model and algorithm proposed in this paper have higher recommendation precision and user’s satisfaction degree.

A context‐aware framework for modelling and verification of smart parking systems in urban cities

Abstract: Parking spaces have been considered as vital resources in urban areas. Finding parking spaces in jam‐packed areas is often challenging, stressful, and uncertain for the drivers that causes traffic congestion with a consequent of wastage of time, fuel, and increase of pollution. In recent years, context‐aware computing paradigm has been considered to be the most effective approach to address these kinds of issues. Context‐aware systems acquire and understand contextual information according to the current situation, perform reasoning, and then act intelligently on behalf of the user. These applications often run on tiny resource‐bounded smart devices with the incorporation of embedded or attached sensors on these devices and they often exhibit complex and adaptive behaviour. In this paper, we propose a context‐aware parking application framework to assist drivers in finding parking slots dynamically while moving and/or arriving at the destination. We optimize the context‐aware parking framework with bounds on computational resources for the decision support dynamically in a highly decentralized environment. To illustrate the use of the proposed system, we model the context‐aware parking system using Uppaal model checker for formal analysis and verify the correctness properties of the system.

Energy-Positive Activity Recognition - From Kinetic Energy Harvesting to Smart Self-Sustainable Wearable Devices.

Abstract: Wearable, intelligent, and unobtrusive sensor nodes that monitor the human body and the surrounding environment have the potential to create valuable data for preventive human-centric ubiquitous healthcare. To attain this vision of unobtrusiveness, the smart devices have to gather and analyze data over long periods of time without the need for battery recharging or replacement. This article presents a software-configurable kinetic energy harvesting and power management circuit that enables self-sustainable wearable devices. By exploiting the kinetic transducer as an energy source and an activity sensor simultaneously, the proposed circuit provides highly efficient context-aware control features. Its mixed-signal nano-power context awareness allows reaching energy neutrality even in energy-drought periods, thus significantly relaxing the energy storage requirements. Furthermore, the asynchronous sensing approach also doubles as a coarse-grained human activity recognition frontend. Experimental results, using commercial micro-kinetic generators, demonstrate the flexibility and potential of this approach: the circuit achieves a quiescent current of 57 nA and a maximum load current of 300 mA, delivered with a harvesting efficiency of 79 %. Based on empirically collected motion data, the system achieves an energy surplus of over 232 mJ per day in a wrist-worn application while executing activity recognition at an accuracy of 89 % and a latency of 60 s.

Enabling Adaptive, Personalised and Context-Aware Interaction in a Smart Learning Environment: Piloting the iCollab System.

Abstract: Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of time, place, platform, and form. In this paper we introduce the iCollab platform, an adaptive environment where learning activities are moderated through conversation with an intelligent agent who can operate across multiple web-based platforms, integrating formal and informal learning opportunities. Fifty-eight undergraduate computer science students were randomly assigned to either an intervention or control group for the 12 weeks of the pilot study. Learning analytics were used to examine their interactions with iCollab, while their course performance investigated the impact of using iCollab on learning outcomes. Results from the study showed a high level of interaction with iCollab, especially social interaction, indicating an interweaving of formal learning within their informal network spaces. These findings open up new possibilities for ways that SLEs can be designed to incorporate different factors, improving the ability of the system to provide adaptive and personalised learning experiences in relation to context and time.

Enabling situational awareness of business processes

Abstract: The purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business.,Ontology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes.,In this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes.,This paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation.

Aegis+: A Context-aware Platform-independent Security Framework for Smart Home Systems

Abstract: The introduction of modern Smart Home Systems (SHSs) is redefining the way we perform everyday activities. Today, myriad SHS applications and the devices they control are widely available to users. Specifically, users can easily download and install the apps from vendor-specific app markets, or develop their own, to effectively implement their SHS solutions. However, despite their benefits, app-based SHSs unfold diverse security risks. Several attacks have already been reported to SHSs and current security solutions only consider smart home devices and apps individually to detect malicious actions, rather than the context of the SHS as a whole. Thus, the current security solutions applied to SHSs cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these limitations, in this article, we introduce Aegis+, a novel context-aware platform-independent security framework to detect malicious behavior in an SHS. Specifically, Aegis+ observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in an SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of Aegis+ in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) and platforms (i.e., Samsung SmartThings, Amazon Alexa) where real users perform day-to-day activities using real SHS devices. We also measured the performance of Aegis+ against five different malicious behaviors. Our detailed evaluation shows that Aegis+ can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout and platforms, device configurations, installed apps, controller devices, and enforced user policies. Finally, Aegis+ yields minimum overhead to the SHS, ensuring effective deployability in real-life smart environments.

A knowledge-based model for context-aware smart service systems

Abstract: The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Tod...

A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness

Abstract: Emotion-cause pair extraction (ECPE), an emerging task in sentiment analysis, aims at extracting pairs of emotions and their corresponding causes in documents. This is a more challenging problem than emotion cause extraction (ECE), since it requires no emotion signals which are demonstrated as an important role in the ECE task. Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step. However, error propagation across steps and pair combining without contextual information limits the effectiveness. Therefore, we propose a Dual-Questioning Attention Network to alleviate these limitations. Specifically, we question candidate emotions and causes to the context independently through attention networks for a contextual and semantical answer. Also, we explore how weighted loss functions in controlling error propagation between steps. Empirical results show that our method performs better than baselines in terms of multiple evaluation metrics. The source code can be obtained at https://github.com/QixuanSun/DQAN.

Optimization Techniques in Intelligent Transportation Systems

Abstract: Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users. They are located in the linkage of information technology, computer science, electrical engineering, system analysis, civil engineering, and optimization. They form a main branch of the smart cities and are fundamental for the development of countries. ITS applications, usually, use the capabilities of sensor networks, electrical devices, and computer processing units to deliver a service, however, they are not limited to the hardware devices. Instead, the modeling of transportation problems and solving them efficiently are more challenging problems. Especially, when they support good ideas for controlling the transportation systems or guiding some users. In this chapter, the application of optimization models for the transportation context are discussed. To this end, the models for data collection by a sensor network are needed. Then, for mining these data and extracting the necessary knowledge for transportation context awareness, some fundamental models such as regression analysis, frequent pattern mining, clustering or classification can be applied. These backgrounds are used to extend the appropriate models of ITS in an integrated architecture. To solve these models, the classical network and combinatorial optimization methods, simulation-optimization techniques, and metaheuristic algorithms will be explained. Then, we categorize the applications of these optimization models in the different subsystems of ITS architecture. The output of this investigation can be used to develop different ITS services for the urban and inter-cities networks.

Integration of Context Awareness in Smart Service Provision System Based on Wireless Sensor Networks for Sustainable Cargo Transportation

Abstract: Smart service provision systems can assist in the management of cargo transportation. The development of these systems faces a number of issues that relate to the analysis of numerous factors, which are influenced by the properties of such complex and dynamic systems. The aim of this research was the development of an adaptable smart service provision system that is able to recognize a wide spectrum of contextual information, which is obtained from different services and heterogeneous devices of wireless sensor networks (WSNs). To ensure that the smart service provision system can assist with the analysis of specific cases of unforeseen and unwanted situations during the cargo transportation process, the system must have additional adaptability. To address the adequate provision of contextual data, we examined the problems of multi-dimensional definitions of contextual data and the choice of appropriate artificial intelligence (AI) methods for recognition of contextual information. The objectives relate to prioritizing potential service provision by ensuring the optimal quality of data supply channels and avoiding the flooding of wireless communication channels. The proposed methodology is based on methods of smart system architecture development that integrate the identification of context-aware data, conceptual structures of data warehouses, and algorithms for the recognition of transportation situations based on AI methods. Experimental research is outlined to illustrate the algorithmic analysis of the prototype system using an appropriate simulation environment.

SIUV: A Smart Car Identity Management and Usage Control System Based on Verifiable Credentials

Abstract: The automotive industry is witnessing an accelerated growth in digital innovations that turn modern vehicles into digital systems. This makes the security of modern vehicles a crucial concern as they have evolved into cyber-physical and safety-critical systems. Therefore, stateful identity management and continuous access control have become a paramount requirement in smart vehicles. Indeed, several Identity and Access Management (IAM) frameworks have been proposed in the automotive field, but context awareness and continuity of control remain overlooked. To address these challenges, we present SIUV: a stateful smart-car IAM that is based on Usage Control (UCON) and Verifiable Credentials (VCs). SIUV uses Attribute Based Access Control (ABAC) policies to issue privileges to subjects (i.e. drivers or applications) according to their credentials and claims. The issued privileges are then used to decide whether to grant or deny access to in-car resources. Furthermore, the system continuously monitors subject claims, resource attributes and environmental conditions (e.g. location or time). Hence, if a change occurs, the system re-evaluates policies and updates or revokes issued privileges and usage decisions accordingly. We describe the architecture of SIUV, discuss the evaluation results, and define future directions.

SealedGRID: Secure and Interoperable Platform for Smart GRID Applications

Abstract: Recent advancements in information and communication technologies (ICT) have improved the power grid, leading to what is known as the smart grid, which, as part of a critical economic and social infrastructure, is vulnerable to security threats from the use of ICT and new emerging vulnerabilities and privacy issues. Access control is a fundamental element of a security infrastructure, and security is based on the principles of less privilege, zero-trust, and segregation of duties. This work addresses how access control can be applied without disrupting the power grid's functioning while also properly maintaining the security, scalability, and interoperability of the smart grid. The authentication in the platform presumes digital certificates using a web of trust. This paper presents the findings of the SealedGRID project, and the steps taken for implementing Attribute-based access control policies specifically customized to the smart grid. The outcome is to develop a novel, hierarchical architecture composed of different licensing entities that manages access to resources within the network infrastructure. They are based on well-drawn policy rules and the security side of these resources is placed through a context awareness module. Together with this technology, the IoT is used with Big Data (facilitating easy handling of large databases). Another goal of this paper is to present implementation and evaluations details of a secure and scalable security platform for the smart grid.

Mobile Edge Computing for Ultra-Reliable and Low-Latency Communications

Abstract: Driven by the recent technological advances and the flourishing innovative applications, mobile edge computing (MEC) has emerged as a promising computing paradigm, aiming to extend cloud computing services from the centralized cloud to the edges of networks. Indeed, it is an essential component in the fifth generation architecture, which can empower users with rapid and powerful computation, cache capacity, energy efficiency, mobility, location, and context awareness support. However, as network access methods become diverse, how to ensure ultra-reliable and low-latency communications in MEC is a very challenging task. This article tries to present a comprehensive survey of relevant research and technological developments in MEC. We first highlight the advantages of MEC in comparison with mobile cloud computing, based on which we then make a holistic overview of MEC, including its related architectures and key enablers. Subsequently, we introduce several specific themes and the existing techniques in MEC, where we are particularly concerned with efficient communication techniques and reliable mechanisms in MEC. Furthermore, we elaborate on the evolution of MEC standardization and discuss several typical application scenarios. Finally, we strive to shed light on some challenges and potential research directions for MEC, which may facilitate the transformation of MEC from theory to practice.