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SpyGlass: a wireless sensor network visualizer
Abstract and Figures
In this paper we present a modular and extensible visualization framework for wireless sensor networks. These networks have typically no means of visualizing their internal state, sensor readings or computational results. Visualization is therefore a key issue to develop and operate these networks. Data emitted by individual sensor nodes is collected by gateway software running on a machine in the sensor network. It is then passed on via TCP/IP to the visualization software on a potentially remote machine. Visualization plug-ins can register to different data types, and visualize the information using a flexible multi-layer mechanism that renders the information on a canvas. Developers can easily adapt existing or develop new custom tailored plug-ins for their specific visualization needs and applications.
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... Information may include partial topology, routing information and, data content. There have been some related works on sniffing tools for WSNs in academia and industry, such as SNA [92], ZENA [93], SANMP [94] and SNDS [95]. However, their high costs and lack of analysis of integration with LoWPAN protocols such as 6LoWPAN and RPL diminish their application into the IoT domain [96]. ...
... Graph generators such as NetworkX [NetworkX developer team 2014], Open Graph Drawing Framewor [Gutwenger et al. 2013], and JGraphT [Naveh 2013] are commonly used for generating network topologies. There are also tools and simulators available for visualizing wireless sensor networks [Buschmann et al. 2005;Ö sterlind et al. 2010;Shu et al. 2008]. However, these tools are not specially designed for the evaluation of force-directed algorithms for boundary node detection in ad hoc networks. ...
Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are not entirely convex in shape. In this article, we propose a Kamada-Kawai-based algorithm called W-KK-MS for boundary node detection problems, which is capable of aligning node positions while achieving high sensitivity, specificity, and accuracy in producing a visual drawing from the input network topology. The algorithm put forward in this article selects and assigns weights to top-k nodes in each iteration to speed up the updating process of nodes. We also propose a novel approach to detect and unfold stacked regions in CNCAH networks. Experimental results show that the proposed algorithms can achieve fast convergence on boundary node detection in CNCAH networks and are able to successfully unfold stacked regions. The design and implementation of a prototype system called ELnet for analyzing CNCAH networks is also described in this article. The ELnet system is capable of generating synthetic networks for testing, integrating with force-directed algorithms, and visualizing and analyzing algorithms' outcomes.
... Ancak bu yazılım grafik ve görselleştirme hizmetlerini sadece yerel (local) olarak sağlayabilmektedir. SpyGlass yazılımı, Java tabanlı olarak geliştirilmiş modüler bir KAA görselleştiricisidir [11]. jWebDust ise yine Java'da geliştirilmiş genel amaçlı ve modüler uygulama ortamıdır [12]. ...
... 12,13 Past researchers have developed tools for WSN visualization and troubleshooting. 14,15 Recently, researchers have studied various network architecture using the Wi-Fi module, Bluetooth module, and Zigbee module for visualizing WSNs with AR devices. 16 There has also been an effort for using AR to provide access to sensors and actuators for the Internet of Things (IoT) applications. ...
Decaying infrastructure maintenance cost allocation depends heavily on accurate and safe inspection in the field. New tools to conduct inspections can assist in prioritizing investments in maintenance and repairs. The industrial revolution termed as “Industry 4.0” is based on the intelligence of machines working with humans in a collaborative workspace. Contrarily, infrastructure management has relied on the human for making day-to-day decisions. New emerging technologies can assist during infrastructure inspections, to quantify structural condition with more objective data. However, today’s owners agree in trusting the inspector’s decision in the field over data collected with sensors. If data collected in the field is accessible during the inspections, the inspector decisions can be improved with sensors. New research opportunities in the human–infrastructure interface would allow researchers to improve the human awareness of their surrounding environment during inspections. This article studies the role of Augmented Reality (AR) technology as a tool to increase human awareness of infrastructure in their inspection work. The domains of interest of this research include both infrastructure inspections (emphasis on the collection of data of structures to inform management decisions) and emergency management (focus on the data collection of the environment to inform human actions). This article describes the use of a head-mounted device to access real-time data and information during their field inspection. The authors leverage the use of low-cost smart sensors and QR code scanners integrated with Augmented Reality applications for augmented human interface with the physical environment. This article presents a novel interface architecture for developing Augmented Reality–enabled inspection to assist the inspector’s workflow in conducting infrastructure inspection works with two new applications and summarizes the results from various experiments. The main contributions of this work to computer-aided community are enabling inspectors to visualize data files from database and real-time data access using an Augmented Reality environment.
Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things, for which situational awareness is normally the crucial first step. While traditional static surveillance data on cities have been available for decades, this review reports a type of relatively new yet highly important urban data source, i.e., the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city. We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System (GPS). This technique enjoys advantages such as a large penetration rate (∼50% urban population covered), uniform spatiotemporal coverage, and high localization precision. We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced. Then we introduce two suites of empowering technologies that help fulfill the requirements of (1) cybersecurity insurance for smart cities and (2) spatiotemporal modeling and visualization for situational awareness, both via big mobile data. The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.
In this chapter, an in-depth study and comparisons of simulators and emulators have been presented, with care accorded to their features, implementation, and use. Since emulators are hardware dependent, selecting one to use is straightforward. On the other hand, with the wide variety of simulators, the choice is rather complex and is subject mainly to how is the simulator easy to use, and fulfilling the model requirements. Remarkably, different simulators do not give similar results for the same model due to their different underlying features and implementations. Simulation has proven to be a valued tool in many areas where analytical methods are not applicable and experimentation is not feasible. Researchers generally use simulation to analyze system performance prior to physical design or to compare multiple alternatives over a wide range of conditions. Notably, errors in simulation models or improper data analysis often produce incorrect or misleading results. Although there exists an extensive row of performance evaluation tools for WSNs, it is impractical to have an all-in-one integrated tool that simultaneously supports simulation, emulation, and testbed implementation. In fact, there is no all-in-one stretchy simulator for WSNs. Each simulator exhibits different features and models, and each has advantages and weaknesses. Different simulators are appropriate and most effective in typical conditions, so in choosing a simulation tool from available picks, it is fruitful to elect a simulator that is best suited for the intended study and targeted application. Also, it is recommended to weigh the pros and cons of different simulators that do the same job: the level of complexity of each simulator, availability, extensibility, and scalability. Usually, WSN applications consist of a large number of sensor nodes; therefore it is recommended to settle on the simulation tool capable of simulating large-scale WSNs. Essentially, the reported use, besides the simulation results of a simulator, should not be ignored before deciding which simulator to prefer. The exercises at the end of the chapter are designed to pinpoint the simulator comparison and selection criteria suitable to the model under study. When bottom-up building a simulator, many decisions need to be made. Developers must consider the pros and cons of different programming languages, whether simulation is event-based or time-based, component-based, or object-oriented architecture, the level of complexity of the simulator, features to include and to not include, use of parallel execution, ability to interact with real nodes, and other design choices that are pertinent to a typical application. For researchers, choosing which simulator to use is not an easy duty. A full understanding of one’s own model is however the first major step before looking into the bookshelf of simulators. Then follows a survey of the available simulators that can do the job. A major step comes after, the careful weighting of the simulator features against the model under study and the programming capabilities of the researcher.
In this chapter an in-depth study and comparisons of simulators and emulators have been presented, with care accorded to their features, implementation, and use. Since emulators are hardware dependent, selecting one to use is straightforward. On the other hand, with the wide variety of simulators, the choice is rather complex and is subject mainly to how is the simulator easy to use and fulfilling the model requirements. Remarkably, different simulators do not give similar results for the same model due to their different underlying features and implementations.
Wireless sensor networks can provide large amounts of data that, when combined with pre-processing and data analysis processes, can generate large amounts of data that may be difficult to present in visual forms. Often, understanding of the data and how it spatially and temporally changes as well as the patterns suggested by the data are of interest to human viewers. This chapter considers the issues involved in the visual presentations of such data and includes an analysis of data set sizes generated by wireless sensor networks and a survey of existing wireless sensor network visualization systems. A novel model is presented that can include not only the raw data but also derived data indicating certain patterns that the raw data may indicate. The model is informally presented and a simulation-based example illustrates its use and potential.
In the SWARMS research project [1], we explore the development of applications for huge collections of mobile devices communicating via radio technologies. Our approach is inspired by the analogy of biological swarms and flocks with the following characteristics: • Relatively resource constrained individuals achieve complex joint operation by local, non-centralized interaction. • Individuals move independently from each other. • Individuals might fail at any time due to malfunction or death. We are especially interested in a problem-centric way of application programming: we prefer to describe the problem and let the swarm decide how to solve it. Our approach is to provide services such as communication, coordination or context awareness that help to hide the distribution from the application and support self-organization. They are based on a new communication paradigm we call the distributed virtual shared information space (dvSIS). The dvSIS describes the state of both the swarm as well as the environment and is based on information that the different devices acquire and publish. No device has complete knowledge (that is, no device sees the whole dvSIS), and must therefore rely on partial information. Due to environmental conditions, node failure, and so forth, the views of different devices might be inconsistent. Since we do not only want to prove the feasibility of our ideas by simulations, we have developed a first version of a middleware (which we call "SWARMSware") that realizes the dvSIS and hides the environment's complexity from the application. Communication is similar to the well-known publish/subscribe paradigm except that we neither use central instances nor a fixed division into information sources and consumers. The content-based mechanism XCast [2] controls the information dissemination in the network. The dvSIS consists of semi-structured self-describing information components that are defined by an XML schema. These components comprise meta-information describing the context of data acquisition (such as position and reliability) or the data itself (such as the level of aggregation or scope) that let the application evaluate information. Based on this meta-information, the application can decide whether or not to integrate received information into its own view of the dvSIS.
This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
Habitat and environmental monitoring is a driving application for wireless sensor networks. We present an analysis of data from a second generation sensor networks deployed during the summer and autumn of 2003. During a 4 month deployment, these networks, consisting of 150 devices, produced unique datasets for both systems and biological analysis. This paper focuses on nodal and network performance, with an emphasis on lifetime, reliability, and the the static and dynamic aspects of single and multi-hop networks. We compare the results collected to expectations set during the design phase: we were able to accurately predict lifetime of the single-hop network, but we underestimated the impact of multi-hop traffic overhearing and the nuances of power source selection. While initial packet loss data was commensurate with lab experiments, over the duration of the deployment, reliability of the backend infrastructure and the transit network had a dominant impact on overall network performance. Finally, we evaluate the physical design of the sensor node based on deployment experience and a post mortem analysis. The results shed light on a number of design issues from network deployment, through selection of power sources to optimizations of routing decisions.
We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island o# the coast of Maine streaming useful live data onto the web. The applicationdriven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.
We visualize the world as a fully connected information space where each object communicates with all other objects without any temporal and geographical constraints. We can model this fully connected space using fine granularity processing which can be implemented using sensors technology. We regard sensors as atomic computing particles which can deployed to geographical locations for capturing and processing data of their surrounding. This report introduces a number of excellent research articles which present unique problems and their success in finding efficient solutions for them. It also peeks in to the future of ever changing information processing discipline.
The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them. In the ZebraNet system we use GPS technology to record fine-grained position data in order to track long term animal migrations [14]. The ZebraNet hardware is composed of a 16-bit TI microcontroller, 4 Mbits of off-chip flash memory, a 900 MHz radio, and a low-power GPS chip. In this paper, we discuss our techniques for devising efficient power supplies for sensor networks, methods of managing the energy consumption of the nodes, and methods of managing the peripheral devices including the radio, flash, and sensors. We conclude by evaluating the design of the ZebraNet nodes and discussing how it can be improved. Our lessons learned in developing this hardware can be useful both in designing future sensor nodes and in using them in real systems.
This article focuses on various future prospects of the Internet This article focuses on various future prospects of the Internet. The future networked computing systems will achieve a degree of sophistication and functionality that will make today's Internet appear primitive in comparison. It is indicated that technological advances will enable ubiquitous networked computing in one's day-to-day lives. In future, the application of computing technologies in settings where they are unusual today such as device and appliance networking in the home; faithful capture of scientific experiments in the laboratory and automated full-time monitoring of patient health. The Web already provides a standard interface that can be leveraged to integrate data harvested from these embedded systems. According to the article, most of these future visions are predicted on predictable advances in chip fabrication and radio and sensor design. It is concluded that regardless of which of these complementary visions of a future ubiquitous computing universe emerges first, and when they reveal themselves, it is expected that computing, communications, and the world at large will be changed profoundly by the impending revolution in embedded Internet devices.
We visualize the world as a fully connected information space where each object communicates with all other objects without any temporal and geographical constraints. We can model this fully connected space using fine granularity processing which can be implemented using sensors technology. We regard sensors as atomic computing particles which can deployed to geographical locations for capturing and processing data of their surrounding. This report introduces a number of excellent research articles which present unique problems and their success in finding efficient solutions for them. It also peeks in to the future of ever changing information processing discipline.
an open-source discrete event simulator for sensor networks
- Shawn
Shawn, an open-source discrete event simulator for sensor
networks: http://www.swarmnet.de/shawn