Detection of Social Interaction in Smart Spaces

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. An aspect of daily life that is important for one's emotional and physical health is social interaction. In this paper we investigate the use of smart environment technologies to detect and analyze interactions in smart spaces. We introduce techniques for collect and analyzing sensor information in smart environments to help in interpreting resident behavior patterns and determining when multiple residents are interacting. The effectiveness of our techniques is evaluated using two physical smart environment testbeds.     

Integrated Development of Context-Aware Applications in Smart Spaces

When designing context-aware applications for smart spaces, developers must provide interface specifications for perceptual components, which often come from multiple vendors. The authors outline a set of structuring principles that leverage such specifications. IBM has introduced two tools that facilitate the development of smart space applications. Chilix is a middleware library for bridging disaggregated systems, and SitCom is an integrated development environment enabling context modeling, and simulation and lifecycle management of perceptual components. Chilix provides an attractive lightweight alternative to legacy middleware for distributed computing. SitCom supports perceptual component developers, service developers and context-modeling experts across the development life cycle.     

Smart Spaces for Adaptive Information Integration in Bioinformatics

Bioinformatics is reaping the benefits of advances in Semantic Web technology thanks to the growing number of available biomedical web resources and portals. Although positive in general, this abundance poses practical challenges to researchers who must be skilled in techniques for retrieving and integrating sparse and complex contents, and thereof calls for more “intelligent” and user friendly ways of interaction to easily get information. With the aim of making available the intelligent functionality of smart systems, this paper presents SSAIIB (Smart Spaces for Adaptive Information Integration in Bioinformatics), a reference framework for designing bioinformatics smart applications that support discovering, aggregating and delivering contents from web resources according to user’s goals, tasks and concerns. The “Smart Spaces” are software environments whose smartness lies in their ability to adaptively accomplish specific user’s activities such as the exploiting content from biomedical resources, integrating data captured from different sources, supporting data analytics, etc. SSAIIB is structured around two main technologies: service oriented architectures and software agents. In particular, it relies on mechanisms for dynamically assembling suitable services and the use of agents as a natural metaphor for both modelling user’s activities and accessing web resources. A case study is presented that shows the application of SSAIIB to the design and the implementation of a smart space for annotating biomedical texts.     

Information management in context trees

Summary Information management in context trees involves three principal problems: retrieval, updating and garbage collection. These problems are discussed in the paper, and solutions are proposed and motivated. A list organization and relative algorithms to implement context trees are presented. Finally, experimental results are reported about the behaviour of a system which exploits context trees.     

Sensor Networks Motes, Smart Spaces, and Beyond

Sensor networks have come a long way since their humble beginnings in DARPA-funded academic research projects in the 1990s and have morphed into a significant research area in their own right. Over the last decade or so, networked sensing devices have become embedded all around us. In this article we look at how sensor network research and applications have evolved and how emerging trends could determine where they're headed.     

Smart radiation sensor management

We developed two radiation mapping algorithms that can handle different situations based on prior information of the search area. The algorithms were developed in the framework of model-driven measurement, where a world model was used to drive measurement collection, and measurements were used to update the world model.We developed and experimentally tested a robotic implementation of two Bayesian-based radiation mapping strategies in two dimensions, using a commercially available desktop mobile robot fitted with a CsI radiation sensor. Our approach for implementing the Bayesian radiation mapping algorithms was to drive the robot over each segment of the search area, in real time, according to the radiation counts collected by the sensor. Future research directions include extensions to three-dimensional mapping; exploring and characterizing the tradeoffs between time efficiency, map confidence level, and utilization of prior knowledge information; as well as the implementation of Bayesian statistics for the online update of the world model.     

Electromagnetic modular Smart Surface architecture and control in a microfactory context

This paper presents an electromagnetic conveyance system called electromagnetic modular Smart Surface (emSS) permitting to move pallets on a planar surface in a microfactory context. The proposed surface concept allows flexibility in reconfiguring the system layout along with product routing. The possibilities of accurate positioning of the moving pallet and controlling multiple pallets on the surface make the emSS suitable for reconfigurable and flexible manufacturing systems. However, the emSS control needs to be robust and scalable to adapt the changes in manufacturing systems. A framework is therefore defined to monitor and control the emSS by simulation or in-line. It allows to define product routing on the emSS by satisfying numerous requirements such as reduction in energy consumption, collision avoidance, etc., and to minimize the human interventions by changing product routing when emSS component failures occur. A first experiment realized on an emSS prototype, allowed to compare two paths strategies regarding cost function linked to energy consumption and velocities. Two other studies exploit the emSS modeling in terms of pallet path generation and simulation of collision avoidance.     

Managing context in business process management systems

Businesses need to continuously focus on change and innovation in order to survive in dynamic environments. The ability of an organization to deploy appropriate business processes requires that the fit between business processes and systems that support the management of these processes is continuously maintained and evolved. Acquisition and use of the knowledge about the context in which business processes are defined, modified, and implemented can help maintain this fit. We identify requirements for a business process management system (BPMS) capable of managing contextual knowledge. Based on these requirements, we have enhanced KOPeR, a knowledge-based system for business process improvement, with an explanation facility that can acquire and maintain knowledge about the context behind process definitions and design choices. A case study that illustrates the functionalities of this system which is designed to improve the fit between business processes and BPMS is presented.

Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

Network aware dynamic context subscription management

Context-awareness is a key requirement in many of today’s networks, services and applications. Context management systems are used to provide access to distributed, dynamic context information. The reliability of remotely accessed dynamic context information is impacted by network delay, packet drop probability, its information dynamics and the access strategy used. Due to the characteristics of the different access strategies, different levels of reliability of context information can be ensured, but at the same time, these strategies lead to different access traffic which impacts also the network performance, and hence feeds back to the reliability of the information. Furthermore, different levels of QoS may be available and used in order to mitigate the impact of network performance degradation on the reliability of the dynamic context information. In this paper we describe a system and algorithms that are capable of configuring effectively context access strategies in order to maximize reliability of all accessed dynamic context information. The framework utilizes and extends existing information reliability models, and it can utilize different network performance models. Simulation results of scenarios in which the framework uses finite-buffer bottleneck performance models demonstrate the effectiveness of our algorithm to increase reliability. Furthermore, the framework is applied to a scenario with QoS classes that allows to trade off delay and loss via different buffer-size configurations.     

Uncertain context data management in dynamic mobile environments

Building systems that acquire, process and reason with context data is a major challenge. Model updates and modifications are required for the mobile context-aware systems. Additionally, the nature of the sensor-based systems implies that the data required for the reasoning is not always available nor it is certain. Finally, the amount of context data can be significant and can grow fast, constantly being processed and interpreted under soft real-time constraints. Such characteristics make it a case for a challenging big data application. In this paper we argue, that mobile context-aware systems require specific methods to process big data related to context, at the same time being able to handle uncertainty and dynamics of this data. We identify and define main requirements and challenges for developing such systems. Then we discuss how these challenges were effectively addressed in the KnowMe project. In our solution, the acquisition of context data is made with the use of the AWARE platform. We extended it with techniques that can minimise the power consumption as well as conserve storage on a mobile device. The data can then be used to build rule models that can express user preferences and habits. We handle the missing or ambiguous data with number of uncertainty management techniques. Reasoning with rule models is provided by a rule engine developed for mobile platforms. Finally, we demonstrate how our tools can be used to visualise the stored data and simulate the operation of the system in a testing environment.     

Smart Sankey picturization for energy management systems in India

AI & SOCIETY - India’s energy demand is predicted to rise by 135% within a span of 20 years. Coping up with surging energy demands requires several reforms in both renewable and...     

智能化通信网络综合管理系统浅析

首先对智能化网络管理技术相关的现状和趋势进行了分析;接着介绍结合了基于策略的网络管理技术、基于规则的告警关联分析技术、基于工作流的过程控制技术实现的智能化综合网络管理系统原型系统。     

Architecture for management and fusion of context information

Information in a context-aware system has diverse natures. Raw data coming from sensors are aggregated and filtered to create more abstract information, which can be processed by context-aware application components to decide what actions should be performed. This process involves several activities: finding the available sources of information and their types, gathering the data from these sources, facilitating the fusion (aggregation and interpretation) of the different pieces of data, and updating the representation of the context to be used by applications. The reverse path also appears in context-aware systems, from changes in the context representation to trigger actions in certain actuators. FAERIE (Framework for AmI: Extensible Resources for Intelligent Environments) is a framework that facilitates management and fusion of context information at different levels. It is implemented as a distributed blackboard model. Each node of the system has a private blackboard to manage pieces of information that can be accessed by observer components, either locally or remotely (from other nodes) in a transparent way. The use of the framework is illustrated with a case study of an application for guiding people to meetings in a university building.     

Concurrent Cooperating Solvers over Reals

Systems combining an interval narrowing solver and a linear programming solver can tackle constraints over the reals that none of these solvers can handle on their own. In this paper we introduce a cooperating scheme where an interval narrowing solver and a linear programming solver work concurrently. Information exchanged by the solvers is therefore handled as soon as it becomes available. Moreover, to improve the pruning, the linear programming solver computes the actual range of values of each variable with respect to the subset of linear constraints. To validate the proposed architecture a prototype system-named CCC-has been developed. Several examples are given to illustrate the gain in speed and precision we can expect with CCC.     

Risk driven Smart Home resource management using cloud services

In order to fully exploit the concept of Smart Home, challenges associated with multiple device management in consumer facing applications have to be addressed. Specific to this is the management of resource usage in the home via the improved utilization of devices, this is achieved by integration with the wider environment they operate in. The traditional model of the isolated device no longer applies, the future home will be connected with services provided by third parties ranging from supermarkets to domestic appliance manufacturers. In order to achieve this risk based integrated device management and contextualization is explored in this paper based on the cloud computing model. We produce an architecture and evaluate risk models to assist in this management of devices from a security, privacy and resource management perspective. We later propose an expansion on the risk based approach to wider data sharing between the home and external services using the key indicators of TREC (Trust, Risk, Eco-efficiency and Cost). The paper contributes to Smart Home research by defining how Cloud service management principles of risk and contextualization for virtual machines can produce solutions to emerging challenges facing a new generation of Smart Home devices.     

Key management for Smart Grid based on asymmetric key-wrapping

Cyber security is considered as a critical issue which must be addressed early during the transition from the current aging electric power grid into the expected Smart Grid. Key management is the fundamental supporting cryptographic technologies for Smart Grid cyber security. This paper focuses on remote key generation and distribution for Smart Grid, especially computation-saving mechanisms for meters. Specifically, we specialize asymmetric key-wrapping to the Smart Grid application scenario, and further suggest an instantiation of remotely generating and distributing keys for Smart Grid, which is an issue raised by National Institute of Standards and Technology. In this way, we achieve a key-wrapping adapter, retrofitting an encryption system to work with an incompatible key-management architecture and seamlessly supporting different options of cryptosystems for smart meters. The proposed scheme has low computational cost at weak smart meters, because of the weaker security assumption of the wrapping algorithm and the transfer of computational cost from meters to servers.     

User Profile Modeling in the context of web-based learning management systems

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.