The Experts below are selected from a list of 29199 Experts worldwide ranked by ideXlab platform
Mani Srivastava - One of the best experts on this subject based on the ideXlab platform.
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a dynamic operating system for Sensor nodes
International Conference on Mobile Systems Applications and Services, 2005Co-Authors: Ram Kumar, Roy Shea, Eddie Kohler, Mani SrivastavaAbstract:Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems. They must use little energy and be robust to environmental conditions, while also providing common services that make it easy to write applications. In TinyOS, the current state of the art in Sensor node operating systems, reusable components implement common services, but each node runs a single statically-linked system image, making it hard to run multiple applications or incrementally update applications. We present SOS, a new operating system for mote-Class Sensor nodes that takes a more dynamic point on the design spectrum. SOS consists of dynamically-loaded modules and a common kernel, which implements messaging, dynamic memory, and module loading and unloading, among other services. Modules are not processes: they are scheduled cooperatively and there is no memory protection. Nevertheless, the system protects against common module bugs using techniques such as typed entry points, watchdog timers, and primitive resource garbage collection. Individual modules can be added and removed with minimal system interruption. We describe SOS's design and implementation, discuss tradeoffs, and compare it with TinyOS and with the Mate virtual machine. Our evaluation shows that despite the dynamic nature of SOS and its higher-level kernel interface, its long term total usage nearly identical to that of systems such as Mate and TinyOS.
Michael Zink - One of the best experts on this subject based on the ideXlab platform.
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multi user data sharing in radar Sensor networks
International Conference on Embedded Networked Sensor Systems, 2007Co-Authors: Ming Li, Deepak Ganesan, Eric Lyons, Prashant Shenoy, Arun Venkataramani, Michael ZinkAbstract:In this paper, we focus on a network of rich Sensors that are geographically distributed and argue that the design of such networks poses very different challenges from traditional "mote-Class" Sensor network design. We identify the need to handle the diverse requirements of multiple users to be a major design challenge, and propose a utility-driven approach to maximize data sharing across users while judiciously using limited network and computational resources. Our utility-driven architecture addresses three key challenges for such rich multi-user Sensor networks: how to define utility functions for networks with data sharing among end-users, how to compress and prioritize data transmissions according to its importance to end-users, and how to gracefully degrade end-user utility in the presence of bandwidth fluctuations. We instantiate this architecture in the context of geographically distributed wireless radar Sensor networks for weather, and present results from an implementation of our system on a multi-hop wireless mesh network that uses real radar data with real end-user applications. Our results demonstrate that our progressive compression and transmission approach achieves an order of magnitude improvement in application utility over existing utility-agnostic non-progressive approaches, while also scaling better with the number of nodes in the network.
Vinayak P Dravid - One of the best experts on this subject based on the ideXlab platform.
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on the performance evaluation of hybrid and mono Class Sensor arrays in selective detection of vocs a comparative study
Sensors and Actuators B-chemical, 2006Co-Authors: Arvind K Srivastava, Vinayak P DravidAbstract:Detection of volatile organic compounds (VOCs) using non-selective Sensor requires an array of multiplexed Sensors followed by pattern recognition approach. Based on this concept, we compare three different approaches for selective detection of ethanol, ammonia, toluene, acetone and chloroform at different concentrations using non-selective Sensors which are: (a) an array of Sensors operated at a fixed temperature (hybrid Class Sensors), (b) operating one Sensor at different temperatures (mono-Class Sensors), and (c) operating all Sensors in an array at different temperatures (hybrid and mono-Class Sensors). Contrary to common practice of using Sensors with partially overlapping response patterns (hybrid Class Sensors) in an array, we demonstrate that even one type of Sensors (mono-Class Sensors) operated at different temperatures can be used for the selective detection of VOCs. It is further shown that an array consisting of hybrid and mono-Class Sensors each operated at different temperatures not only results in approaching 100% Classification but also the quantified samples fall within 10% of error, which is an encouraging result.
Ram Kumar - One of the best experts on this subject based on the ideXlab platform.
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a dynamic operating system for Sensor nodes
International Conference on Mobile Systems Applications and Services, 2005Co-Authors: Ram Kumar, Roy Shea, Eddie Kohler, Mani SrivastavaAbstract:Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems. They must use little energy and be robust to environmental conditions, while also providing common services that make it easy to write applications. In TinyOS, the current state of the art in Sensor node operating systems, reusable components implement common services, but each node runs a single statically-linked system image, making it hard to run multiple applications or incrementally update applications. We present SOS, a new operating system for mote-Class Sensor nodes that takes a more dynamic point on the design spectrum. SOS consists of dynamically-loaded modules and a common kernel, which implements messaging, dynamic memory, and module loading and unloading, among other services. Modules are not processes: they are scheduled cooperatively and there is no memory protection. Nevertheless, the system protects against common module bugs using techniques such as typed entry points, watchdog timers, and primitive resource garbage collection. Individual modules can be added and removed with minimal system interruption. We describe SOS's design and implementation, discuss tradeoffs, and compare it with TinyOS and with the Mate virtual machine. Our evaluation shows that despite the dynamic nature of SOS and its higher-level kernel interface, its long term total usage nearly identical to that of systems such as Mate and TinyOS.
Ming Li - One of the best experts on this subject based on the ideXlab platform.
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multi user data sharing in radar Sensor networks
International Conference on Embedded Networked Sensor Systems, 2007Co-Authors: Ming Li, Deepak Ganesan, Eric Lyons, Prashant Shenoy, Arun Venkataramani, Michael ZinkAbstract:In this paper, we focus on a network of rich Sensors that are geographically distributed and argue that the design of such networks poses very different challenges from traditional "mote-Class" Sensor network design. We identify the need to handle the diverse requirements of multiple users to be a major design challenge, and propose a utility-driven approach to maximize data sharing across users while judiciously using limited network and computational resources. Our utility-driven architecture addresses three key challenges for such rich multi-user Sensor networks: how to define utility functions for networks with data sharing among end-users, how to compress and prioritize data transmissions according to its importance to end-users, and how to gracefully degrade end-user utility in the presence of bandwidth fluctuations. We instantiate this architecture in the context of geographically distributed wireless radar Sensor networks for weather, and present results from an implementation of our system on a multi-hop wireless mesh network that uses real radar data with real end-user applications. Our results demonstrate that our progressive compression and transmission approach achieves an order of magnitude improvement in application utility over existing utility-agnostic non-progressive approaches, while also scaling better with the number of nodes in the network.