A semantic enriched data model for sensor network interoperability

One of the main open issues in the development of applications for sensor network management is the definition of interoperability mechanisms among the several monitoring systems and heterogeneous data. Interesting researches related to integration techniques have taken place, they are primary based on the adoption of sharing data mechanisms. In the last years, the Service-Oriented Architecture (SOA) approach has become predominant in many sensor network projects as it enables the cooperation and interoperability of different sensor platforms at an higher level of abstraction. In this paper we propose a novel architecture for the interoperability of sensor networks, which is based on web services technologies and on a common data model enriched with semantic concepts and annotations. The proposed architecture allows the development of complex decision support system applications by integration of heterogeneous data, accessible through services, according to standard data format and standard protocols.     

Discover and visualize association rules from sensor observations on the web

Nowadays, Web-based applications has became a common practice in environment monitoring. These applications provide open platforms for users to discover access and integrate near real-time sensor data which is collected from distributed sensors and sensor networks. To make use of the shared sensor data on the Web, conceptual models in a particular domain are normally adopted. However, most conceptual models require high quality data and high level domain knowledge. Such limitations greatly limit the application of these models. To overcome some of these limitations, this paper proposes a data-mining approach to analyze patterns and relationships among different sensor data sets. This approach provides a flexible way for users to understand hidden relationships in shared sensor data, and can help them to make use Web-based sensor systems better.     

Navigation Aided Image Processing in UAV Surveillance: Preliminary Results and Design of an Airborne Experimental System

This paper describes an airborne reconfigurable measurement system being developed at Swedish Defence Research Agency (FOI), Sensor Technology, Sweden. An image processing oriented sensor management architecture for UAV (unmanned aerial vehicles) IR/EO-surveillance is presented. Some preliminary results of navigation aided image processing in UAV applications are demonstrated, such as SLAM (simultaneous localization and mapping), structure from motion and geolocation, target tracking, and detection of moving objects. The design goal of the measurement system is to emulate a UAV-mounted sensor gimbal using a stand-alone system. The minimal configuration of the system consists of a gyro-stabilized gimbal with IR and CCD sensors and an integrated high-performance navigation system. The navigation system combines dGPS real-time kinematics (RTK) data with data from an inertial measurement unit (IMU) mounted with reference to the optical sensors. The gimbal is to be used as an experimental georeferenced sensor platform, using a choice of carriers, to produce military relevant image sequences for studies of image processing and sensor control on moving surveillance and reconnaissance platforms. Furthermore, a high resolution synthetic environment, developed for sensor simulations in the visual and infrared wavelengths, is presented. © 2004 Wiley Periodicals, Inc.     

Remote sensing of row crop structure and component temperatures using directional radiometric temperatures and inversion techniques

A physically based sensor response model of a row crop was used as the mathematical framework from which several inversion strategies were tested for extracting row structure information and component temperatures using a series of sensor view angles. The technique was evaluated on ground-based radiometric thermal infrared data of a cotton row crop that covered 48% of the ground in the vertical projection. The results showed that the accuracies of the predicted row heights and widths, vegetation temperatures, and soil temperatures of the cotton row crop were on the order of 5 cm (± 10% of mean values), 1°, and 2°C, respectively. The inversion techniques can be applied to directional sensor data from aircraft platforms and even space platforms if the effects of atmospheric absorption and emission can be corrected. In theory, such inversion techniques can be applied to a wide variety of vegetation types and thus can have significant implications for remote sensing research and applications in disciplines that deal with incomplete vegetation canopies.     

Collegial activity learning between heterogeneous sensors

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper, we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.     

An open framework for translating portable applications into operating system‐specific wireless sensor networks applications

Wireless sensor networks (WSNs) are distributed systems integrated by tiny devices, called sensor nodes, with capabilities to monitor the environment and forward their measurements to a special node, the sink, where the results can be collected and further processed. The trend in WSN is moving towards heterogeneous networks that will contain different sensor nodes running different instances of custom operating systems. Given the growing demand of new hardware platforms and operating systems specifically designed for sensor nodes, the applications programming for sensor nodes is becoming a challenging process that needs to be alleviated. Currently, application programming for sensor nodes is a complex, ad hoc, and error‐prone process where the portability among different platforms has been sacrificed. In this paper, we propose an open framework aimed to achieve application portability in heterogeneous sensor networks. Our approach provides the programming abstractions needed to support the application development process for sensor nodes. We have implemented an open framework that provides a set of tools on top of the most popular WSN operating systems to translate portable applications to the native operating system in an automatic, simple, and transparent way for developers. We have also evaluated the applications thus generated in terms of productivity and overhead, by comparing their footprint to those originally developed in each specific operating system. The results show that the overhead is minimal—4% in the worst case—and in some cases, it was even possible to reduce the footprint by using code optimizations. Copyright © 2012 John Wiley & Sons, Ltd.     

A data collection protocol for real-time sensor applications

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring) — a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.     

Programmatically defining the software footprint of sensor networks using the Android platform

Sensor networks are being used for an increasing number of applications ranging from environmental monitoring and precision agriculture to manufacturing and health care. By gathering data at unprecedented temporal and spatial granularity, sensor networks are revolutionizing these applications. Given the scale and complexity of these systems, they face a critical challenge in software Operations and Management, that is, installing, configuring, and updating thousands of software components in a heterogeneous sensor network. In this paper, we discuss automating and simplifying the process of defining the software environment on a sensor node running the Android platform. Android has significant share in the mobile phone market and runs on a broad range of devices including embedded platforms. This paper outlines the use of a new method for configuring embedded devices and sensor networks by drawing from techniques used for defining the software environment in data centers. We have also developed a software prototype to allow for a simple, reproducible, and flexible configuration of the Android software stack on a sensor node. Our exploratory research provides an insight into the use of modular configuration techniques in embedded devices and how they can be applied to the Android ecosystem. Copyright © 2013 John Wiley & Sons, Ltd.     

On the Choice and Placement of Wearable Vision Sensors

This paper discusses two of the most important design considerations for a wearable device with visual sensing: what kind of sensor to use and where to place it. While nature and computer vision have explored a wide range of imaging techniques, wearables have mostly viewed the world through conventional narrow-view passive cameras designed for nonwearable applications, which are attached to the wearer's head. The rationale presented here for sensor selection and the novel methodology developed for objectively studying sensor placement have informed the development of a number of visual wearables.      

Retina-like visual sensor for fast tracking and navigation robots

This paper describes the development of an anthropomorphic visual sensor which generates a spatially variant resolution image by using a retina-like structure. This sensor consists of a dove prism for image rotation and two linear CCD sensors with 512 pixel/line resolution and holds approximately 45 kbytes of image data. The retina-like sensor has variable resolution with increasing density towards the center of the visual field and yields a polar-coordinate image directly. The motion analysis of the object in the scene from the optical flow is considerably simplified if the velocity is represented in polar coordinates, compared to the case when the image is represented in cartesian coordinates. A calibration procedure for the proposed retina-like sensor is also presented with experimental data to verify the validity of the system. Development of this sensor holds promise in applications to high-speed tracking systems, such as the eyes of navigation robots, because it has data reduction and polar mapping characteristics.     

光学视觉传感器技术研究进展

视觉传感是人类感知外界、认知世界的主要途径，研究表明人类获取的外界信息大约有80%来自于视觉。作为感知外界信息的“电子眼球”，视觉传感器是消费电子、机器视觉、安防监控、科学探测和军事侦察等领域的核心器件。近年来视觉传感器技术发展迅速，不同类型的传感器从不同维度提供丰富的视觉数据，不断增强人类感知与认知能力，视觉传感器研究工作具有重要的理论与应用需求。本报告以典型光学视觉传感器技术为主线，通过综合国内外文献和相关报道，从CCD图像传感器、CMOS图像传感器、智能视觉传感器以及红外图像传感器等研究方向，梳理论述近年来光学视觉传感器技术的发展现状、前沿动态、热点问题和趋势。     

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security, and highly interactive games. It is therefore important to define design methodologies and programming frameworks which enable rapid prototyping of WBSN applications. Several effective application development frameworks have been already proposed for WBSNs designed for TinyOS-based sensor platforms, e.g. CodeBlue, SPINE, and Titan. In this paper we present an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring. The agent-oriented programming abstractions provided by MAPS allow effective and rapid prototyping of the sensor-side software. In particular, the architecture of the developed system is a typical star-based WBSN composed of a coordinator node and two sensor nodes located respectively on the waist and the thigh of the monitored assisted living. The coordinator relies on a JADE-based enhancement of the SPINE coordinator and allows configuring sensors, receiving their data, and recognizing pre-defined human activities. On the other hand, each sensor node runs a MAPS-based agent that performs sensing of the 3-axial accelerometer sensor, computation of significant features on the acquired data, feature aggregation and transmission to the coordinator. The experimentation phase of the prototype, which allows evaluating the obtainable monitoring performances and activity recognition accuracy, is described. Moreover, a comparison of the monitoring system based on MAPS, AFME and SPINE in terms of programming effectiveness and system performances is discussed.     

SPINE: a domain‐specific framework for rapid prototyping of WBSN applications

Wireless body sensor networks (WBSNs) enable a broad range of applications for continuous and real‐time health monitoring and medical assistance. Programming WBSN applications is a complex task especially due to the limitation of resources of typical hardware platforms and to the lack of suitable software abstractions. In this paper, SPINE (signal processing in‐node environment), a domain‐specific framework for rapid prototyping of WBSN applications, which is lightweight and flexible enough to be easily customized to fit particular application‐specific needs, is presented. The architecture of SPINE has two main components: one implemented on the node coordinating the WBSN and one on the nodes with sensors. The former is based on a Java application, which allows to configure and manage the network and implements the classification functions that are too heavy to be implemented on the sensor nodes. The latter supports sensing, computing and data transmission operations through a set of libraries, protocols and utility functions that are currently implemented for TinyOS platforms. SPINE allows evaluating different architectural choices and deciding how to distribute signal processing and classification functions over the nodes of the network. Finally, this paper describes an activity monitoring application and presents the benefits of using the SPINE framework. Copyright © 2010 John Wiley & Sons, Ltd.     

Telehealth and ubiquitous computing for bandwidth-constrained rural and remote areas

The information and communication technology infrastructure available in rural and remote areas may often not have the bandwidth to support all types of telehealth applications; therefore, for example, some traditionally envisaged videoconferencing-based telehealth applications may not be able to be used or not used in their anticipated form at this time. While the level of broadband services available may impose limitations on these types of telehealth applications, in this review article, we identify applications that allow the maximizing of telehealth benefits in the presence of low-bandwidth connectivity and have potential benefits well-matched to rural and remote area healthcare challenges. In particular, we include consideration of how ubiquitous computing might potentially bring non-traditional approaches to telehealth that can also come into usage more immediately in bandwidth-constrained rural and regional areas. In this article, we review the benefits of ubiquitous computing for rural and remote telehealth including social media-based preventative, peer support and public health communication, mobile phone platforms for the detection and notification of emergencies, wearable and ambient biosensors, the utilization of personal health records including in conjunction with mobile and sensor platforms, chronic condition care and management information systems, and mobile device–enabled video consultation.     

An interface designed for networked monitoring and control in wireless sensor networks

In this paper, a new interface is developed in detail that provides terminal users with ways for direct and convenient interactions with the wireless sensor networks (WSNs). It also embraces an Internet based subsystem for remote monitoring and controlling of the WSNs. Different from existing platforms and interfaces, our design is general, extensible and not constrained for specific applications. More importantly, it paves the way for improving the data processing efficiency and avoiding data execution congestion. It proposes the idea utilizing the advantages of existing professional mathematical software tools, such as Matlab, Mathematica and Maple, to process the WSN data.     

立体视觉传感器优化设计技术研究

依据几何成像原理建立了对称式立体视觉传感器的参数模型,通过对此模型坐标测量误差及传感器各参数误差传递函数的分析,研究了立体视觉传感器结构参数及其误差传递规律对传感器综合测量精度的影响关系特性,分析了有效视场、坐标测量精度和传感器结构参数之间的关系,给出了立体视觉传感器结构参数的设计原则与方法步骤,对立体视觉传感器的结构进行了优化设计,对比了传统结构和新式结构的优缺点,并在文章最后介绍了实际应用以及达到的精度.     

POSAML: A visual modeling language for middleware provisioning

Next generation distributed applications are often hosted on heterogeneous platforms including different kinds of middleware. Due to the applications’ growing functional complexity and their multiple quality of service (QoS) requirements, system developers are increasingly facing a substantial number of middleware provisioning challenges, which include configuring, optimizing and validating the middleware platforms for QoS properties. Traditional techniques for middleware provisioning tend to use non-intuitive, low-level and technology-specific approaches, which are tedious, error prone, and non-reusable across different technologies. Quite often the middleware provisioning activities are carried out by different actors without much interaction among them, which results in an iterative trial-and-error process to provisioning. Higher level abstractions, particularly those that use visual models, are effective in addressing these challenges. This paper describes the design of a visual modeling language called POSAML (pattern-oriented software architecture modeling language) and associated tools that provide an intuitive, higher level and unified framework for provisioning middleware platforms. POSAML provides visual modeling capabilities for middleware-independent configurations and optimizations while enabling automated middleware-specific validation of system QoS properties.     

Fast image motion segmentation for surveillance applications

Wireless, battery-powered camera networks are becoming of increasing interest for surveillance and monitoring applications. The computational power of these platforms is often limited in order to reduce energy consumption. In addition, many embedded processors do not have floating point support in hardware. Among the visual tasks that a visual sensor node may be required to perform, motion analysis is one of the most basic and relevant. Events of interest are usually characterized by the presence of moving objects or persons. Knowledge of the direction of motion and velocity of a moving body may be used to take actions such as sending an alarm or triggering other camera nodes in the network.We present a fast algorithm for identifying moving areas in an image. The algorithm is efficient and amenable to implementation in fixed point arithmetic. Once the moving blobs in an image have been precisely localized, the average velocity vector can be computed using a small number of floating point operations. Our procedure starts by determining an initial labeling of image blocks based on local differential analysis. Then, belief propagation is used to impose spatial coherence and to resolve aperture effect inherent in texture less areas. A detailed analysis of the computational cost of the algorithm and of the provisions that must be taken in order to avoid overflow with 32-bit words is included.     

A clustering approach for sampling data streams in sensor networks

The growing usage of embedded devices and sensors in our daily lives has been profoundly reshaping the way we interact with our environment and our peers. As more and more sensors will pervade our future cities, increasingly efficient infrastructures to collect, process and store massive amounts of data streams from a wide variety of sources will be required. Despite the different application-specific features and hardware platforms, sensor network applications share a common goal: periodically sample and store data collected from different sensors in a common persistent memory. In this article, we present a clustering approach for rapidly and efficiently computing the best sampling rate which minimizes the Sum of Square Error for each particular sensor in a network. In order to evaluate the efficiency of the proposed approach, we carried out experiments on real electric power consumption data streams provided by EDF (électricité de France).