Context-aware Services Research Articles

Intelligent Access Control Design for Security Context Awareness in Smart Grid

Recently, damages such as internal system intrusion, network and device vulnerability attacks, malicious code infection, and information leakage due to security attacks are increasing within the smart grid environment. Detailed and dynamic access control must be implemented to enable the power system in the smart grid environment to respond to such attacks. Dynamic and partial delegation must be available, and permission role restrictions must be considered for dynamic access control when delegating a role because of changes in power resource manager authority. In this paper, we propose an intelligent access control framework that can recognize security context by analyzing security vulnerabilities for security management of power systems. The intelligent access control framework is designed as a framework that enables collaboration within the smart grid environment, and a system administrator is designed to transmit access control policy information required between the power service principal and the agent. In addition, an experiment is conducted for the control inference of security context ontology-based access, attack detection inference of the security context awareness service, and the attack response of the intelligent integrated access control system. Experimental results show that the precision of security context ontology-based access control inference is 70%, and the attack response rate of integrated access control is 72.8%.

A user-centric intelligent context-aware system for realizing internet-of-things environments

In the near future, an Internet-of-things (IoT) environment will be realized across ubiquitous terminals such as computers and smartphones, supporting connections between people and machines. IoT platforms facilitate the deployment of various services to users; however, the information provided by these platforms is focused on the present state of things and environments. To realize a true IoT environment, there is a need for user-centric and seamless context-aware services as well as IoT control services based on environmental feedback. In this paper, we propose a dynamic mesh network configuration method to analyze real-time user context and provide seamless services, as well as a user context-aware abstraction mechanism. To evaluate the performance of the proposed system, we simulate a parking system, and then compare the performance to the best effort method, finding that performance was significantly improved. Thus, we expect that seamless context-aware services will become possible through the application of such methods.

Context-Aware Service Recommendation Based on Knowledge Graph Embedding

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-aware service knowledge graph</i> (C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.

SatProbe: Low-Energy and Fast Indoor/Outdoor Detection via Satellite Existence Sensing

Indoor-outdoor (IO) detection provides very useful hints for a mobile device to perform context-aware services. To that end, GPS presents a viable solution by relating a device's IO status with its positioning performance, which depends on the device's exposure to the open sky. This approach, however, is prohibitively expensive in terms of energy consumption and response time. Recent work has thus been focused on exploiting low-energy sensors such as light, cellular, and magnetic sensors to infer the IO status indirectly, at the cost of reduced adaptability or manual labeling effort. In this article, we propose a new method to address these problems. Our method, called SatProbe, reverts to the GPS approach for its directness and robustness, but avoids its drawback by extracting only the number of visible satellites from the raw GPS data, instead of going through extensive computation to obtain a final position. This metric provides a clear indicator of the IO status, yet can be obtained with great efficiency. Experiments on 79 raw GPS traces with 2595 detection points across a variety of environments show that SatProbe produces a 14.2 percent improvement in detection accuracy, with a 98.8 percent reduction in both energy use and detection time, in comparison with the standard GPS method.

Multi-dimensional trust for context-aware services computing

The paper addresses the problem of trust management of cloud, fog and IoT services in dynamically changing environments. The continuous dynamic environment is one of the challenges that trustworthy services management in the cloud, fog and IoT settings faces. Services in such an environmental context have difficulty securing an acceptable quality of service (QoS). This article proposes a trust management framework that establishes service trust by considering the direct trust from the truster (subjective trust), aggregating referrals about the service in a collusion-resistant manner (objective trust), and bootstrapping new services. We introduce a subjective trust model based on the formalism of dependency networks to dynamically predict the provided QoS in response to context environment changes. The proposed approach leverages the dependency relations that exist among the QoS metrics and environmental context variables. The novelty at the subjective trust level lies in considering the dynamic cyclic dependency relations that enhances the prediction accuracy. However, subjective trust based on direct interactions could be insufficient to make the trust estimate credible. Hence, on top of the subjective layer, we propose an objective trust management model resilient to collusion attacks by leveraging the power of mass collaboration among referees. Finally, we propose a bootstrapping mechanism that is resilient to the white-washing attacks by observing the behaviours of newcomer services with no trust resources using the concept of social adoption to estimate their initial trust values. Experiments conducted on real-life and synthetic datasets demonstrate the effectiveness of our approach compared with state-of-the-art approaches. We used the statistical log score to assess the model’s prediction accuracy and employed the estimation error of the objective trust as indicated by referees relative to the one calculated by the multi-round simulation. The ROC (Receiver Operating Characteristic) curves are finally used to measure the accuracy of the classifier used in the proposed trust bootstrapping mechanism in providing accurate initial trust values. The main findings of the paper are around the new trust model of cloud, fog and IoT services considering their dynamically changing environments. The first finding is that the prediction of the provided QoS shows better results when it is dynamic and responds to context environment changes by leveraging the dynamic dependency network linking the QoS metrics and context variables of the environment. The second finding is that the objective trust performs better when it is resilient to collusion attacks by leveraging the power of mass collaboration among referees. The third finding is that the bootstrapping mechanism that observes the behaviours of new comer services with no trust resources using the concept of social adoption to estimate their initial trust values excels by being resilient to white watching attacks.

Combining planning and learning for context aware service composition

Computing vision introduced by Mark Weiser in the early '90s has defined the basis of what is called now ubiquitous computing. This new discipline results from the convergence of powerful, small and affordable computing devices with networking technologies that connect them all together. Thus, ubiquitous computing has brought a new generation of service-oriented architectures (SOA) based on context-aware services. These architectures provide users with personalised and adapted behaviours by composing multiple services according to their contexts. In this context, the objective of this paper is to propose an approach for context-aware semantic-based services composition. Our contributions are built around following axes: 1) a semantic-based context modelling and context-aware semantic composite service specification; 2) an architecture for context-aware semantic-based services composition using artificial intelligence planning; 3) an intelligent mechanism based on reinforcement learning for context-aware selection in order to deal with dynamicity and uncertain character of modern ubiquitous environment.

SaaSRec+: a new context-aware recommendation method for SaaS services.

Cloud computing is an attractive model that provides users with a variety of services. Thus, the number of cloud services on the market is growing rapidly. Therefore, choosing the proper cloud service is an important challenge. Another major challenge is the availability of diverse cloud services with similar performance, which makes it difficult for users to choose the cloud service that suits their needs. Therefore, the existing service selection approaches is not able to solve the problem, and cloud service recommendation has become an essential and important need. In this paper, we present a new way for context-aware cloud service recommendation. Our proposed method seeks to solve the weakness in user clustering, which itself is due to reasons such as 1) lack of full use of contextual information such as cloud service placement, and 2) inaccurate method of determining the similarity of two vectors. The evaluation conducted by the WSDream dataset indicates a reduction in the cloud service recommendation process error rate. The volume of data used in the evaluation of this paper is 5 times that of the basic method. Also, according to the T-test, the service recommendation performance in the proposed method is significant.

Combining planning and learning for context aware service composition

Combining planning and learning for context aware service composition

CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Where am I? Predicting user location semantics from engagement with smartphone notifications

Location semantics are important for the delivery of context-aware ubiquitous services to users, such as the contextually-relevant handling of interruptions on mobile devices. For such purposes, user coordinates can be used to query global venue databases, to get back the likely venue (and its categories) where the user is located. This potentially compromises user privacy, allowing service providers to track users. We analyse data from a longitudinal study of 44 participants (university students and staff in Patras, Greece), including notification handling, device state and location information. Using semantic labels from the Google Places API as ground truth, we demonstrate that it is possible to semantically label a user’s location based on their notification handling behaviour, even when location coordinates are obfuscated so as not to precisely match known venue locations. On the other hand, the reliability of this ground truth is questioned through a crowdsourcing exercise. We demonstrate that Places API data can only be reliably used for some venue categories, and recommend best practices for using such data to establish ground truth in location context aware services.

Designing and configuring context-aware semantic web applications

Context-aware services are attracting attention of world as the use of web services are rapidly growing. We designed an architecture of context-aware semantic web which provides on demand flexibility and scalability in extracting and mining the research papers from well-known digital libraries i.e. ACM, IEEE and SpringerLink. This paper proposes a context-aware administrations system, which supports programmed revelation and incorporation of setting dependent on Semantic Web administrations. This work has been done using the python programming language with a dedicated library for the semantic web analysis named as “Cubic-Web” on any defined dataset, in our case as we have used a dataset for extracting and studying several publications to measure the impact of context aware semantic web application on the results. We have found the average recall and averge accuracy for all the context aware research journals in our research work. Moreover, as this study is limited journal documents, other future studies can be approached by examining different types of publications using this advance research. An efficient system has been designed considering the parameters of research article meta-data to find out the papers from the web using semantic web technology. Parameters like year of publication, type of publication, number of contributors, evaluation methods and analysis method used in publication. All this data has been extracted using the designed context-aware semantic web technology.

A Social-Relationships-Based Service Recommendation System for SIoT Devices

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

Personalized cloud service review analysis based on modularized ontology

PurposeThe purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.Design/methodology/approachpropose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.FindingsThe findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.Research limitations/implicationsLimited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.Practical implicationsThe SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.Originality/valueThe SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

Information processing in Internet of Things using big data analytics

With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering discipline. These applications and services could vigorously respond to the surroundings transformation and users’ preference. Developing a scalable system for data analysis, processing and mining of enormous real world based datasets has turned into one of the demanding problems that faces both system research scholars and data management research scholars. Employing big data analytics with IoT technologies is one of the ways for handling the timely analyzing information (i.e., data, events) streams. In this paper, we propose an integrated approach that coalesce IoT systems with big data tools into a holistic platform for real-time and continuous data monitoring and processing. We propose Fog assisted IoT based Smart and real time healthcare information processing (SRHIP) system in which large amounts of data generated by IoT sensor devices are offloaded at Fog cloud form data analytics and processing with minimum delay. The processed data is then transferred to a centralized cloud system for further analysis and storage. In this work, we introduce a Fog-assisted model with big data environment for data analytic of real time data with remote monitoring and discuss our plan for evaluating its efficacy in terms of several performance metrics such as transmission cost, storage cost, accuracy, specificity, sensitivity and F-measure. The proposed SRHIP system needs less transmission cost of 40.10% in comparison to SPPDA, 100% fewer bytes are compromised in comparison to GCEDA. Our proposed system data size reduction of 60% reduction due to proposed compression scheme in comparison to other benchmark strategies that offer 40% of reduction.

A Context-aware Strategy for Mobile Services in M-commerce

Due to mobile communication technologies, the novel mobile commerce is coming true. The contextaware service is one of the most potential mobile commerce. The data about mobile users and their surroundings are recognized and collected, which are used to provide the services they may be interested. Fuzzy logical processing model are utilized to make basic data correspond to the mobile users’ high-level information, so the recognition of their behaviours and situations could be inferred in our research. The numerical examples are also illustrated in our scheme. Based on the results of our activity recognition mechanism, it is possible to automatically discover and transmit the contents or services that users require in specific scenarios. Therefore, more and more novel mobile applications are coming true. To sum up, the proposed scheme is supposed to enhance context-aware services and provide more attractive life styles.

Hybrid Positioning for Smart Spaces: Proposal and Evaluation

Positioning capabilities have become essential in context-aware user services, which make easier daily activities and let the emergence of new business models in the trendy area of smart cities. Thanks to wireless connection capabilities of smart mobile devices and the proliferation of wireless attachment points in buildings, several positioning systems have appeared in the last years to provide indoor positioning and complement GPS for outdoors. Wi-Fi fingerprinting is one of the most remarkable approaches, although ongoing smart deployments in the area of smart cities can offer extra possibilities to exploit hybrid schemes, in which the final location takes into account different positioning sources. In this paper we propose a positioning system that leverages common infrastructure and services already present in smart spaces to enhance indoor positioning. Thus, GPS and WiFi are complemented with access control services (i.e., ID card) or Bluetooth Low Energy beaconing, to determine the user location within a smart space. Better position estimations can be calculated by hybridizing the positioning information coming from different technologies, and a handover mechanism between technologies or algorithms is used exploiting semantic information saved in fingerprints. The solution implemented is highly optimized by reducing tedious computation, by means of opportunistic selection of fingerprints and floor change detection, and a battery saving subsystem reduces power consumption by disabling non-needed technologies. The proposal has been showcased over a smart campus deployment to check its real operation and assess the positioning accuracy, experiencing the noticeable advantage of integrating technologies usually available in smart spaces and reaching an average real error of 4.62 m.

Smart and intelligent network selection approach to support location-dependent and context-aware service migration

Smart and intelligent network selection approach to support location-dependent and context-aware service migration

CATS-CAE Reflective Middleware Framework for Adapting Context-Aware Transactional Services

Pervasive environments are characterized by limited computing resources and wireless connectivity. In parallel, current application domains have variable transactional requirements that do not fit the traditional ACID model. As a result, the pervasive environment characteristics are compelling and cannot be supported by conventional solutions that are typically dedicated to a specific application domain and support a limited set of context parameters. This article aims at providing a complete solution that addresses the challenges of the adaptability of context-aware transactional services “CATS” in pervasive environments. Thus, a new framework CATS-CAE was designed, which offers a comprehensive structure of multiple component chains. The adaptation strategy in CATS-CAE is based on a hybrid approach combining the use of adaptation policies, alternative strategy and behavioral adaptation of composite services through the “Profiled Task Class” concept. A probabilistic model is also presented to support the efficiency of the proposed approach.

IoT-commerce - opportunities for customers through an affordance lens

Retail commerce is influenced by digital technologies at large scale. After electronic commerce and its evolution into mobile commerce, we now see that the Internet of Things (IoT), one of the most disruptive developments in recent times, is about to radically transform retail commerce from need recognition to post-purchase engagement and service. Extant literature mainly investigates technical features of IoT, missing out on a customer-centric perspective. Theoretically founded in Activity and Affordance Theories, this paper conceptualizes IoT-commerce, identifies opportunities for customers, and links them to the customer buying process. Based on an extensive literature review, twelve affordances are derived and evaluated with a sample of real-world IoT devices. All affordances offered by electronic and mobile commerce are still valid for IoT-commerce but extended by three affordances unique to IoT-commerce: context-aware services, natural interactions, and automated customer processes. Affecting all steps of the customer buying process, IoT-commerce is worth to be understood by researchers, customers, and companies.