11 research outputs found

    Building the Future Internet through FIRE

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    The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

    A Highly Reliable, Low Power Consumption, Low-Cost Multisensory Based System For Autonomous Navigational Mobile Robot

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    There has been remarkable growth in most real-time systems in the area of autonomous mobile robots. Collision-free path planning is one of the critical requirements in designing mobile robot systems since they all featured some obstacle detection techniques. This work focuses on the collaborations of low cost multi-sensor system to produce a complementary collision-free path for mobile robots. The proposed algorithm is used with a new model to produce the shortest, and most energy-efficient path from a given initial point to a goal point. Multiple sensors are utilized together, so the benefits of one compensate for the limitations of the other. The experimental results demonstrate that the robot is capable of measuring different distances to obstacles in unknown environments. Moreover, this work aims to minimize the energy consumption of a wheeled mobile robot in dynamic environments. The total energy consumption is evaluated in multiple directions, where both motional energy and operational energy are considered, while the robot is moving in dynamic environments and avoiding collisions. A time complexity analysis and a comparison of the proposed model, and states-of-arts methods are presented by using required resources and the overall performance of the proposed model. The proposed model is characterized by its low cost, low power consumption, and its efficiencies to follow the shortest path while avoiding collisions

    The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

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    As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course

    Information extraction from large-scale WSNs - a complex querying perspective

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    Envirosuite: An Environmentally-Immersive Programming Framework for Wireless Sensor Networks

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    Networked, embedded sensors allow for an instrumentation of the physical world at unprecedented granularities and from unimagined perspectives. The advent of a ubiquitous sensing era is evident. Yet, sensor network techniques are still far from entering mainstream adoption due to multiple unresolved research challenges, especially due to the high development cost of sensor network applications. Therefore, in this dissertation, we propose to design, implement, and evaluate an environmentally-immersive programming framework, called EnviroSuite, to reduce sensor network software development cost. The goal of our research is to create reusable sensor network development support for the community and reduce the adoption barriers for a broader category of users, ultimately leading to a transition of sensor networks from a research concept to a general-purpose technology available for use for a wide variety of research, government, industry, and everyday purposes. Current sensor network programming practice remains very cumbersome and inefficient for several reasons. First, most existing programming abstractions for sensor networks are either too low-level (thus too tedious and error-prone) or too high-level (unable to support the diversity of sensor network applications). Second, there is no clear separation between application-level programming and system-level programming. A significant concern is the lack of a general middleware library to isolate application developers from low-level details. Finally, testing sensor network systems is particularly challenging. Sensor systems interact heavily with a (non-repeatable) physical environment, making lab experiments not representative and on-site experiments very costly. This dissertation is targeted for a comprehensive solution that addresses all the above-mentioned problems. The EnviroSuite framework consists of (i) a new programming paradigm that exports environment-based abstractions, (ii) critical middleware services that support the abstractions and separate application programmers from tedious, low-level details, and (iii) testing tools geared for in-situ experimenting, debugging, and troubleshooting. First, we introduce a new programming paradigm, called environmentally-immersive programming (EIP), to capture the common characteristics of sensor network applications, the rich, distributed interactions with the physical environment. EIP refers to an object-based programming model in which individual objects represent physical elements in the external environment. It allows the programmer to think directly in terms of physical objects or events of interest. We provide language primitives for programmers to easily implement their environmental tracking and monitoring applications in EIP. A preprocessor translates such EIP code transparently into a library of support middleware services, central to which are object management algorithms, responsible for maintaining a unique mapping between physical and logical objects. The major outcome of sensor networks is observations of the instrumented environment, in other words, sensory data. Implementing an application mainly involves encoding how to generate, store, and collect such data. EIP object abstractions provide simple means for programmers to define how observations of the environment should be made via distributed coordination among multiple nodes, thus simplifying data generation. Yet, the next steps, namely, data storage and collection, remain complicated and fastidious. To isolate programmers from such concerns, we also include in the support library a set of data management services, comprising both network protocols and storage systems to allow data to be collected either in real-time or in a delay-tolerant manner. The final phase in sensor network software development life-cycle is testing, typically performed in-field, where the effects of environmental realities can be studied. However, physical events from the dynamic environment are normally asynchronous and non-repeatable. This lack of repeatability makes the last phase particularly difficult and costly. Hence, it is essential to have the capability to capture and replay sensing events, providing a basis not only for software testing, but also for realistic protocol comparison and parameter tuning. To achieve that, EnviroSuite also provides testing and debugging facilities that enable controllable and repeatable in-field experiments. Finally, to demonstrate the benefits of our framework, we build multiple representative applications upon EnviroSuite, drawn from both tracking systems such as military surveillance, and monitoring systems such as environmental acoustic monitoring. We install these applications into off-the-shelf hardware platforms and physically deploy the hardware into realistic environments. Empirical results collected from such deployments demonstrate the efficacy of EnviroSuite

    Sensor network localization based on natural phenomena

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    Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 107-116).Autonomous localization is crucial for many sensor network applications. The goal of this thesis is to develop a distributed localization algorithm for the PLUG indoor sensor network by analyzing sound and light sensory data from naturally occurring background phenomena as well as synthesized emulations of background transients. Our approach has two main phases: passive and active. The system enters an active mode when, its sensed region stays relatively silent and stable, hence assumed to be unoccupied; otherwise, it stays in the passive mode. In the passive mode, each node looks for sonic transients and compares the timing of its highest sound peak to that of synchronized sound peaks from other nodes in its neighborhood in order to estimate its distance. Passive ranging achieved 50.96cm error and simulated passive localization achieved 103.06cm error with a typical node-spacing of 2m. In addition, the system exploits background transients based on light sensory data to determine room boundaries. In the active mode, each node occasionally generates recorded mimics of natural sonic transients, like pencils dropping or water glasses clinking and manipulates an attached light source. Active acoustic ranging achieved 2.1cm error and simulated active localization achieved 7.97cm error with a typical node-spacing of 2m. In addition, passive location estimation in a real deployment is found to converge as more sensory data is available; range resolutions of 2.5m and localization errors of 20.3cm were obtained after running in passive mode for 20 hours in 7m by 5m dorm hallway. The main features of author's approach are its distributed properties, the lack of any heavy infrastructure, its unobtrusive exploitation of multi-sensory background phenomena, and in active mode, making the sound signal between nodes unobtrusive by mimicking the natural sounds.by Daniel Sang Kim.M.Eng

    Efficient Passive Clustering and Gateways selection MANETs

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    Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets

    Self-* properties of multi sensing entities in smart environments

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    Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 78-87).Computers and sensors are more and more often embedded into everyday objects, woven into garments, "painted" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks.by Arnaud Pilpré.S.M
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