Context-aware Management Research Articles

Realizing an excellent solution for detecting and solving conflicts between viewpoints of designers in self-adaptive systems

Recently, given the rise of emerging demands on intelligent technological systems, increasing attention has been given to proliferating and enhancing the adaptation capacities of self-adaptive systems (SAS), which are capable of adapting their behavior to changes in the context or system resources contributing to overcome the complexity of today’s software-intensive systems. However, there are a significant number of challenges for developing adaptations on the SAS, such as simplifying the designer’s task, improving responsiveness, and reducing the conflicts between its adaptations. This study focuses on simplifying the designers’ tasks by utilizing independent designers’ viewpoints on context modeling. We proposed several solutions for solving the conflicts among different viewpoints of designers in the layer of context-aware management (CAM). The validation results are promising and show that our method effectively provides a solution that supports the problem of using independent viewpoints in the context modeling process and improves the SAS adaptation capacity.

Declarative continuous reasoning in the cloud-IoT continuum

AbstractDeveloping and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service placement by reacting both to changes in the infrastructure and in the application requirements, and capable of suggesting migrations only for services affected by such changes. The methodology is prototyped in Prolog and assessed through simulations over a realistic use case and over a lifelike motivating scenario at increasing infrastructure sizes. Experimental results show that our approach brings considerable speed-up in comparison with an exhaustive search employing non-incremental reasoning.

An Intelligent Context-Aware Management Framework for Cold Chain Logistics Distribution

Contextual information can accurately describe the user’s time, location, environment, activities, the use of devices, and so on. The recommender systems with contextual information have better prediction accuracy, coverage, and user satisfaction. However, the application of conventional context-aware framework in some special fields has some drawbacks. For example, in the field of cold chain logistics distribution, the conventional context-aware framework cannot adapt to the multi-dimensional, complex and dynamic contexts of cold chain logistics, the lack of context-aware risk management, and the lack of traceability of cargo contexts. In this paper, we propose an intelligent context-aware management framework for cold chain logistics (CMFCCL) distribution that contains acquisition framework, recommender systems framework, risk management framework, tracing back framework, and user portrait framework of cold chain logistics distribution. Cold chain info data set is used for simulation, and three other recommendation approaches are considered in comparison. The experimental results indicate that CMFCCL can reduce the mean absolute error and root mean squared error of the data set by about 8.37%–19.99%. Thus, the evaluation presents encouraging results, indicating that CMFCCL would be useful in the context-aware management of cold chain logistics distribution.

Intelligent context-awareness system for energy efficiency in smart building based on ontology

Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user's comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption.

Evidence-Based Context-Aware Log Data Management for Integrated Monitoring System

Integrated monitoring system, enabled with semi-structured datastore, is a promising solution for monitoring SaaS systems. However, according to increasing scale of SaaS systems and their long-term of service operations, the monitoring system has faced the problem in response times of log analysis and storage consumption. Our empirical observation is that the problem is primarily derived from the unselective log processing of semi-structure datastore, whereas there should be heterogeneities in log data that we can take advantage of for efficient log management. Based on this observation, we first attest this insight by investigating the usage patterns of log data in a quantitative manner with an actual dataset of log access histories obtained from a SaaS system serving to enterprise users, and we show that there are heterogeneities in required retention period of logs, response time, and amount of data, depending on log data category and its analysis scenario. Armed with the evidence found from the investigation, we design a methodology of context-aware log management, key features of which are to speculatively pre-cache the log analysis and to proactively archive ones depending on log data category and analysis scenario. Evaluation with a prototype implementation shows that the proposed method reduces the response time and the storage consumption.

Context Design and Tracking for IoT-Based Energy Management in Smart Cities

The advent of the Internet of Things (IoT) and its innumerous applications for Smart Cities emphasizes the need for context-aware systems able to adapt behavior automatically to instant environment conditions. Currently, there is a gap in terms of understanding how context information is interrelated, as well as tracking which events occurred under which conditions within certain context scopes. Visualizing, specifying, tracking and monitoring typical contexts involved in IoT-based applications are still challenging activities since context modeling remains a low level process that requires much human expertise. In this paper we propose a new context life cycle that involves context design and context tracking. We developed a context-aware management framework, where contexts are modeled as graphs and can be explicitly designed and their occurrences can be tracked down. We believe that this feature of designing and tracking context graphs may help developers, administrators, and end-users in harnessing the wealth of information generated by highly scalable IoT systems.

Context aware power management in smart grids using load balancing approach

Smart grid is an electrical grid with an advanced digital communication network. In the past decade, the development in the field of smart girds attracted more researchers towards it. This paper presents the power aware model for context awareness and load management in the smart homes. In the adaptive system of the smart grid, the smart homes are treated as smart node. The proposed power aware smart home management model (PASH) incorporates the evolutionary programming algorithm and context awareness rules for communicating with the users. The PASH model explains the advantage of load balancing in the smart homes for both users and smart grid. The context management module in the PASH helps to utilise the power efficiency in the peak demands.

Context aware power management in smart grids using load balancing approach

Context aware power management in smart grids using load balancing approach

Context-aware radio resource management below 6\xa0GHz for enabling dynamic channel assignment in the 5G era

Heterogeneous networks constitute a promising solution to the emerging challenges of 5G networks. According to the specific network architecture, a macro-cell base station (MBS) shares the same spectral resources with a number of small cell base stations (SBSs), resulting in increased co-channel interference (CCI). The efficient management of CCI has been studied extensively in the literature and various dynamic channel assignment (DCA) schemes have been proposed. However, the majority of these schemes consider a uniform approach for the users without taking into account the different quality requirements of each application. In this work, we propose an algorithm for enabling dynamic channel assignment in the 5G era that receives information about the interference and QoS levels and dynamically assigns the best channel. This algorithm is compared to state-of-the-art channel assignment algorithm. Results show an increase of performance, e.g., in terms of throughput and air interface latency. Finally, potential challenges and way forward are also discussed.

A context-aware decision-support system in clinical pharmacy: Drug monitoring in the elderly

Summary Introduction Can aging models be used to develop a model of pharmaceutical practice that improves patient care in a context of increasing workload and budgetary constraints? Material and method A before–after observational study was performed in nine healthcare institutions to assess the impact of a context-aware computerized decision-support system designed with available software (SAP Business Object®) to automatically compare prescriptions recorded in computerized patient files (Cariatide®) against the main consensual guidelines for medical management of elderly subjects and the French Health Authority's Drug Prescription in Elderly Subjects (PMSA) program. The system generated alerts to improve medication management and displayed a list of clinical actions to be taken for individual patients. The name and localization of these patients were identified so that the pharmacist could analyze the alerts and take any necessary action to adjust potentially inappropriate prescriptions as part of a systematic individualized update of good therapeutic practice in elderly subjects. Drug interactions were not taken into consideration. During the study period, data were collected from all patients over 65 years of age receiving daily care (n = 369) with no change in the size of the pharmacy team. Results Over a 10.5-month period from 27 October 2015 to 16 September 2016, this context-aware pharmaceutical analysis tool was implemented for all patients aged more than 65 years: n = 184; 118 F, 66 M; mean age: 73.9 ± 7.1 years. Over the corresponding period of the previous year (27 October 2014 to 16 September 2015), 185 elderly patients (116 F, 69 M; mean age, 75.4 ± 7.4 years) were managed before the new computerized system had been implemented. The new tool took a mean 45 s to display the good drug management analysis, highlighting discrepancies with respect to guidelines for the treatment of elderly subjects and listing potentially inappropriate drugs per patient and per care unit. The tool generated a table displaying PMSA health-quality indicators per care unit. Another screen displayed potentially inappropriate prescriptions for the elderly. Mean hospital stay for elderly patients was comparable for the two periods: 33 and 37 days. Between the two periods, prescription of short-acting ( Discussion and conclusion The literature shows that implementation of good clinical practice guidelines is improved when they are communicated via decision-support instruments, preferably context-aware tools linked to patient files. Despite possible limitations, the project was found to comply with practice objectives and models based on recognized factors contributing to the development of clinical pharmacy departments. A study of context-aware computer-assisted drug management in nursing homes or rehabilitation units prior to hospital (re)admission is proposed, with the aim of reducing admission rates for the elderly, and with the added benefit of providing extended assessment of this model of pharmaceutical practice.

Load-adaptive networking for energy-efficient wireless access

Energy-efficient operation is essential for mobile network operators to meet the growing demand for higher data rates while managing rising operating costs. Here, the main challenge is to guarantee the quality of user experience whilst saving energy. This challenge demands adaptive algorithms that enable a load-aware network operation that dynamically configures different network elements according to user needs. To this end, in this paper, we present an adaptive and context-aware power management framework for networks composed of different radio access technologies. We implement and evaluate our framework in an indoor and outdoor testbed. The experimental results confirm that significant energy can be saved in practice by efficiently adapting resources to the actual traffic demand.

Context-Aware Security using Gimbal based Sensor Control System

Objectives: In order to provide efficient knowledge for location aware computing system in mobile applications, this paper proposes an experimental analysis considering user's location information. Methods/Analysis: These systems can entail information from array of sensors. Context aware management system combining central, distributed management scheme along with sensors, which can be utilized for location application. In order to provide better context/location aware management system, sensors (magnetometers) are mounted on Gimbal platform. This sensor platform synchronizes the device direction in all three axes. Findings: Magnetometer sensor can detect ground changes with respect to orientation angle with maximum accuracy. These possessions are validated with magnetic field intensity&rotation headings towards the north direction. Calibration of magnetometer reduces the noise and drift variations in the output. Conclusion :The proposed analysis offers displacement of the device and ensures the quality of service.

Mobility management in HetNets: a learning-based perspective

Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three.

Wireless Femto-Relays

The continuous increase in mobile data traffic creates the need for radical innovations in the mobile broadband system design. Heterogeneous networks (HetNets) deployment paradigm is an emerging research trend considered as the most significant aspect for meeting the mobile data challenge, mainly by proposing spectral efficiency enhancements and cell capacity gains from an overall system perspective. Two are the main challenges that dense small cell networks are facing: a) various types of interference mainly caused by unplanned deployments, and b) limited and Quality of Service (QoS)-unreliable wired backhaul links. In this paper, the authors propose a model where femtocells' and relays' complementary characteristics are effectively exploited into femto-relays. More specifically, the authors describe four main functionalities of this model: a) interference protection, b) two-fold backhaul alternatives (i.e. wired and wireless backhaul), c) opportunistic femto-relay selection, and d) full-duplex in-band relaying. To evaluate the anticipated technical impact of the proposed model, targeted simulation results are presented for a campus topology where femto-relays are compared with classic femtocells. Finally, related open issues, such as signaling overheads management, context-aware resource management, and business logic aspects are presented, with emphasis on the femto-relay's real market penetration opportunities in the mid and long-term future.

IMuseumA: an agent-based context-aware intelligent museum system.

Currently, museums provide their visitors with interactive tour guide applications that can be installed in mobile devices and provide timely tailor-made multimedia information about exhibits on display. In this paper, we argue that mobile devices not only could provide help to visitors, but also to museum staff. Our goal is to integrate, within the same system, multimedia tour guides with the management facilities required by museums. In this paper, we present iMuseumA (intelligent museum with agents), a mobile-based solution to customize visits and perform context-aware management tasks. iMuseumA follows an agent-based approach, which makes it possible to interact easily with the museum environment and make decisions based on its current status. This system is currently deployed in the Museum of Informatics at the Informatics School of the University of Málaga, and its main contributions are: (i) a mobile application that provides management facilities to museum staff by means of sensing and processing environmental data; (ii) providing an integrated solution for visitors, tour guides and museum staff that allows coordination and communication enrichment among different groups of users; (iii) using and benefiting from group communication for heterogeneous groups of users that can be created on demand.

An Ontology-Based Context Model for Wireless Sensor Network (WSN) Management in the Internet of Things

Wireless sensor networks (WSNs) are an enabling technology of context-aware systems. The Internet of Things (IoT), which has attracted much attention in recent years, is an emerging paradigm where everyday objects and spaces are made context-aware and interconnected through heterogeneous networks on a global scale. However, the IoT system can suffer from poor performances when its underlying networks are not optimized. In this paper, an ontology model for representing and facilitating context sharing between network entities in WSNs is proposed for the first time. The context model aims to enable optimal context-aware management of WSNs in IoT, which will also harness the rich context knowledge of IoT systems.

Context-Aware Nanoscale Modeling of Multicast Multihop Cellular Networks

In this paper, we present a new approach to optimization of multicast in multihop cellular networks. We apply a hexagonal tessellation for inner partitioning of the cell into smaller subcells of radius r. Subcells may be several orders of magnitude smaller than, e.g., microcells, resulting in what we refer to as a nanoscale network model (NSNM), including a special nanoscale channel model (NSCM) for this application. For such tessellation, a spatial interleaving SI MAC protocol is introduced for context-aware interlink interference management. The directed flooding routing protocol (DFRP) and interflooding network coding (IFNC) are proposed for such a network model including intercell flooding coordination (ICFC) protocol to minimize the intercell interference. By adjusting the radius of the subcell r , we obtain different hopping ranges that directly affect the throughput, power consumption, and interference. With r as the optimization parameter, in this paper we jointly optimize scheduling, routing, and power control to obtain the optimum tradeoff between throughput, delay, and power consumption in multicast cellular networks. A set of numerical results demonstrates that the NSNM enables high-resolution optimization of the system and an effective use of the context awareness.

To call or not to call—That is the question (while driving)

We studied whether decisions to engage in cell phone conversation while driving and the consequences of such decisions are related to the driver's age, to the road conditions (demands of the driving task), and to the driver's role in initiating the phone call (i.e. the driver as caller vs. as receiver). Two experiments were performed in a driving simulator in which driver age, road conditions and phone conversation, as a secondary task, were manipulated. Engagement in cell phone conversations, performance in the driving and the conversation tasks, and subjective effort assessment were recorded. In general, drivers were more willing to accept incoming calls than to initiate calls. In addition, older and younger drivers were more susceptible to the deleterious effects of phone conversations while driving than middle aged/experienced drivers. While older drivers were aware of this susceptibility by showing sensitivity to road conditions before deciding whether to engage in a call or not, young drivers showed no such sensitivity. The results can guide the development of young driver training programs and point at the need to develop context-aware management systems of in-vehicle cell phone conversations.

Intelligent context-aware energy management using the incremental simultaneous method in future wireless sensor networks and computing systems

AbstractIn this article, we present an intelligent context-aware energy management for saving home energy consumption using activities of daily living. In order to extract energy-consuming patterns, we analyze daily activities, which are collected from location sensors and electronic appliances. In addition, we construct an energy management model using the incremental statistical method, which is improved by the ISM-based adaptive model using house construction and location of electronic devices. Furthermore, we provide a simulator that conducts the various functions using the energy-consuming patterns and present the simulation results for the verification and efficiency of our model.

Exploring solutions for mobile companionship: A design research approach to context-aware management

The mobile phone is not just another device; it is with you day and night, and you rely on its capabilities in work and in private. In short, the mobile phone is your companion. As your companion, it should understand your situational and informational needs. How do we increase the friendliness of your mobile phone, in order to fulfil this promise? In this paper, we explore how context awareness can be used for managing the user mobile experience. To this end, we employed a design research approach to integrate context-aware and cloud based services in an Android application. Through a user evaluation and proof-of-concept implementation we show how new technologies can increase the friendliness of your mobile phone. In so doing, we provide evidence that adaptive applications based on user context offer a fertile ground for taking mobile companionship to the next level.