Design and validation of a light inference system to support embedded context reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.     

基于uSD卡的移动情境数据采集和汇报系统的设计与实现*

提出了一种基于uSD卡的移动情境数据采集和汇报系统。移动设备利用uSD卡与外部传感器节点通信,并将来自于内部和外部的情境数据汇报给远程数据库。提出了一种集中式的情境数据采集汇报调度算法,通过移动设备之间的协作来去除不同移动设备所汇报情境数据间的冗余,并提高情境数据采集的精度。定义了基于XML的传感器数据包描述语言,以克服外部传感器数据包的异构性。设计并实现了连接管理模块,支持在多种通信方式之间的透明动态切换。基于提出的情境数据收集和共享系统设计并实现了混合现实的PacMan游戏,游戏的试运行结果表明,本系统具有较好的实时性和可用性。     

An osmotic computing infrastructure for urban pollution monitoring

Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet-of-Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet-of-Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.     

结合网络层次分析法的云推理威胁评估模型*

针对传统的威胁评估方法存在指标数据冗余、指标权值设置合理性、推理有效性等问题,建立结合网络层次分析法的云推理威胁评估模型,能够合理精简指标,有效优化推理规则。将该模型用于目标识别系统的威胁评估,首先给出威胁评估指标,用网络层次分析法精简指标并得到规范化权值;构建指标云模型,将规范后的指标数据输入前件云发生器,建立推理规则库,引入分级结构简化规则数,运用加权扎德算子实现规则的合成,将合成结果输入后件云发生器得到威胁度云滴,经多次重复操作后,处理数据得到系统威胁度。最后,以实例说明方法的有效性。     

Multiple-Agent Perspectives in Reasoning About Situations for Context-Aware Pervasive Computing Systems

In open heterogeneous context-aware pervasive computing systems, suitable context models and reasoning approaches are necessary to enable collaboration and distributed reasoning among agents. This paper proposes, develops, and demonstrates the following: 1) a novel context model and reasoning approach developed with concepts from the state-space model, which describes context and situations as geometrical structures in a multidimensional space; and 2) a context algebra based on the model, which enables distributed reasoning by merging and partitioning context models that represent different perspectives of computing entities over the object of reasoning. We show how merging and reconciling different points of view over context enhances the outcomes of reasoning about the context. We develop and evaluate our proposed algebraic operators and reasoning approaches with cases using real sensors and with simulations. We embed agents and mobile agents with these modeling and reasoning capabilities, thus facilitating context-aware and adaptive mobile agents operating in open pervasive environments.     

Automatic feature selection for context recognition in mobile devices

In mobile devices there exist several in-built sensor units and sources which provide data for context reasoning. More context sources can be attached via wireless network connections. Usually, the mobile devices and the context sources are battery powered and their computational and space resources are limited. This sets special requirements for the context recognition algorithms. In this paper, several classification and automatic feature selection algorithms are compared in the context recognition domain. The main goal of this study is to investigate how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices. The main result is that even a simple linear classification algorithm can achieve a reasonably good accuracy if the features calculated from raw data are selected in a suitable way. Usually context recognition algorithms are fitted to a particular problem instance in an off-line manner and modifying methods for on-line learning is difficult or impossible. An on-line version of the Minimum-distance classifier is presented in this paper and it is justified that it leads to considerably higher classification accuracies compared with the static off-line version of the algorithm. Moreover, we report superior performance for the Minimum-distance classifier compared to other classifiers from the view point of computational load and power consumption of a smart phone.     

普适计算中基于上下文信息的缓存管理算法

普适计算的一个常见的难题是断连操作,而移动设备在断连状态下对数据进行操作又是必要的.为了支持断连操作,需要在移动客户端上进行数据缓存.数据收集的目的是在断连前把用户将来可能访问的数据预先存储到本地缓存,因此收集过程的结果将对断连操作的性能产生重大影响.目前针对断连操作的数据收集算法,对缓存命中都有一定效果,为了进一步提高缓存命中率,本文根据上下文信息进行数据收集算法;然后在访问数据时同步建立数据之间的关联,并在数据关联的基础上自动选择要收集的数据集;最后将结果按缓存驻留时间和访问次数进行缓存替换.模拟试验结果表明,此算法对于存储容量小的手持移动设备可以有效地提高断连操作时的缓存命中率,可以更好的支持移动设备的断连操作.     

Energy consumption of data mining algorithms on mobile phones: Evaluation and prediction

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. Energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices. This paper presents an experimental study of the energy consumption behavior of representative data mining algorithms running on mobile devices. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime. Based on the outcome of this study we also proposed a machine learning approach to predict energy consumption of mobile data-intensive algorithms. Results show that a considerable accuracy is achieved when the predictor is trained with specific-algorithm features.     

Providing user context for mobile and social networking applications

The processing capabilities of mobile devices coupled with portable and wearable sensors provide the basis for new context-aware services and applications tailored to the user environment and daily activities. In this article, we describe the approach developed within the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth to provide user contexts. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information in web-centered servers that support well-known social networking services. In the current prototype, context inference is based on decision trees to learn and to identify contexts dynamically at run-time, but the middleware allows the integration of different inference engines if necessary. Experimental results in a real-world setting suggest that the proposed solution is a promising approach to provide user context to local mobile applications as well as to network-level applications such as social networking services.     

iConAwa - An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.     

Threat modeling of a mobile device management system for secure smart work

To enhance the security of mobile devices, enterprises are developing and adopting mobile device management systems. However, if a mobile device management system is exploited, mobile devices and the data they contain will be compromised. Therefore, it is important to perform extensive threat modeling to develop realistic and meaningful security requirements and functionalities. In this paper, we analyze some current threat modeling methodologies, propose a new threat modeling methodology and present all possible threats against a mobile device management system by analyzing and identifying threat agents, assets, and adverse actions. This work will be used for developing security requirements such as a protection profile and design a secure system.     

Creating human digital memories with the aid of pervasive mobile devices

The abundance of mobile and sensing devices, within our environment, has led to a society in which any object, embedded with sensors, is capable of providing us with information. A human digital memory, created with the data from these pervasive devices, produces a more dynamic and data rich memory. Information such as how you felt, where you were and the context of the environment can be established. This paper presents the DigMem system, which utilizes distributed mobile services, linked data and machine learning to create such memories. Along with the design of the system, a prototype has also been developed, and two case studies have been undertaken, which successfully create memories. As well as demonstrating how memories are created, a key concern in human digital memory research relates to the amount of data that is generated and stored. In particular, searching this set of big data is a key challenge. In response to this, the paper evaluates the use of machine learning algorithms, as an alternative to SPARQL, and treats searching as a classification problem. In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning. Our approach produces good results with 100% sensitivity, 93% specificity, 93% positive predicted value, 100% negative predicted value, and an overall accuracy of 97%.     

Context-aware multiagent system: Planning home care tasks

Context-aware systems are able to capture information from the context in which they are executed, assign a meaning to the gathered information, and change their behavior accordingly. As a result, the systems can offer services to users according to their individual situation within the context. This article analyzes the important aspects of context-aware computing such as capturing information for context attributes and determining the manner of interacting with users in the environment. Used in conjunction with mobile devices, context-aware systems are specifically used to improve the usability of applications and services. This article proposes the home care context-aware computing (HoCCAC) multiagent system that identifies and maintains a permanent fix on the location of patients in their home, and manages the infrastructure of services within their environment securely and reliably by processing and reasoning the data received. Based on the multiagent system, a prototype was developed to monitor patients in their home. The HoCCAC multiagent system uses a critical path method-based planning model that, in the present study, prepares the most optimal task-planning schedule for the patients in their home, is capable of reacting automatically when faced with dangerous or emergency situations, replanning any plans in progress and sending alert messages to the system. The results obtained with this prototype are presented in this article.     

一种不确定信息条件下的威胁估计方法

未来复杂战场环境下信息具有高度不确定性,对于不同类型的目标很难客观地估计其威胁等级。针对该问题,采用粒计算的有关理论建立了可实时更新的威胁估计信息系统,基于决策逻辑语言提取出极小化的规则集,它反应了信息系统所包含的专家经验知识。通过分析知识的不确定性,并给出其不确定性表示,提出了相应的知识推理策略,从而可以对复杂情况下的不同类型多目标进行有效的威胁估计。     

A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

Human activity recognition (HAR) essentially uses (past) sensor data or complex context information for inferring the activities a user performs in his daily tasks. HAR has been extensively studied using different paradigms, such as different reasoning mechanisms, including probabilistic, rule-based, statistical, logical reasoning, or the machine learning (ML) paradigm, to construct inference models to recognize or predict user activities. ML for HAR allows that activities can be recognized and even anticipated through the analysis of collected data from different sensors, with greater accuracy than the other paradigms. On the other hand, context-aware middlewares (CAMs) can efficiently integrate a large number of different devices and sensors. Moreover, they provide a programmable and auto-configurable infrastructure for streamline the design and construction of software solutions in scenarios where lots of sensors and data are their bases, such as ambient intelligence, smart cities, and e-health domains. In this way, the full integration of ML capabilities as services in CAMs can advance the development of software solutions in these domains when ML is necessary, specially for HAR, which is the basis for many scenarios in these domains. In this work, we present a survey for identifying the state-of-the-art in using ML for HAR in CAMs through a systematic literature review (SLR). In our SLR, we worked to answer four research questions: (i) what are the different types of context reasoners available in CAMs; (ii) what are the ML algorithms and methods used for generating models for context reasoning; (iii) which CAMs support data processing in real time; and (iv) what are the HAR scenarios usually tackled by the research works. In our analysis, we observed that, although ML offers viable approaches to construct inference models for HAR using different ML approaches, including batch learning, adaptive learning and data stream learning, there are yet some gaps and research challenges to be tackled, specially on the use of data stream learning considering concept drift on data, mechanisms for adapting the inference models, and further considering all of this as services in CAMs, specially for HAR.

     

Privacy threats in a mobile enterprise social network

The ‘Instant Knowledge’ system is an enterprise based social network that aims to introduce employees of the enterprise to contacts within the organization who may have skills relevant to particular tasks. The skills database is maintained through context-aware devices, and mobile devices in particular. The aim is to populate the database automatically based on user context data and to provide automatic introductions, again based on context data. This paper examines the security and privacy implications of this system and shows that while threat modelling on its own provides a solid base from which to secure the system, this is not enough to ensure that all privacy issues are considered. This is demonstrated by applying a mis-use case analysis that shows how personal identifying information can be inadvertantly leaked to malicious parties.     

基于语义Web技术的上下文感知的智能移动服务

语义Web技术应用于上下文感知的智能移动服务，通过构建上下文信息本体，使得移动服务的实体之间可以进行上下文信息共享和语义互操作，并进行上下文信息推理，实现智能服务。本文首先介绍了语义Web及本体技术，其次阐述了语义Web技术应用于上下文感知的移动服务，然后详细分析了智能移动服务中的上下文信息本体构建，包括通用的上下文信息本体、用户概况本体、情境本体以及服务本体等，接着介绍了相关的研究项目，最后进行展望和总结。     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.     

Landmark-assisted location and tracking in outdoor mobile network

Modern mobile devices integrating sensors, like accelerometers and cameras, are paving the way to the definition of high-quality and accurate geolocation solutions based on the informations acquired by these sensors, and data collected and managed by GSM/3G networks. In this paper, we present a technique that provides geolocation and mobility prediction of mobile devices, mixing the location information acquired with the GSM/3G infrastructure and the results of a landmark matching achieved thanks to the camera integrated on the mobile devices. Our geolocation approach is based on an advanced Time-Forwarding algorithm and on database correlation technique over Received Signal Strength Indication (RSSI) data, and integrates information produced by a landmark recognition infrastructure, to enhance algorithm performances in those areas with poor signal and low accurate geolocation. Performances of the algorithm are evaluated on real data from a complex urban environment.