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Publications, ExScal Notes, & Posters

ExScal Publications - August 16, 2005

Reliable Bursty Convergecast in Wireless Sensor Networks

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Authored By Hongwei Zhang, Anish Arora, Young-ri Choi, Mohamed Gouda on 08/16/2005
Conference: 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2005)


"ExScal: Elements of an Extreme Scale Wireless Sensor Network," (to appear, 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005))

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Authored By Anish Arora on 07/12/2005
Project ExScal (for Extreme Scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 1.3km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.


"Analyzing the Yield of ExScal, a Large-Scale Wireless Sensor Network Experiment," OSU-Technical Report OSU-CISRC-6/05-TR46 (to appear, 13th IEEE International Conference on Network Protocols (ICNP) 2005)

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Authored By Sandip Bapat, Vinod Kulathumani, and Anish Arora on 07/11/2005
Recent experiments have taken steps towards realizing the vision of extremely large wireless sensor networks, the largest of these being ExScal, in which we deployed about 1200 nodes over a 1.3k m by 300m open area. Such experiments remain especially challenging because of: (a) prior observations of failure of sensor network protocols to scale, due to network faults and their spatial and temporal variability, (b) complexity of protocol interaction, (c) lack of sufficient data about faults and variability, even at smal ler scales, and (d) current inadequacy of simulation and analytical tools to predict sensor network protocol behavior. In this paper, we present detailed data about faults, both anticipated and unanticipated, in ExScal. We also evaluate the impact of these faults on ExScal as wel l as the design principles that enabled it to satisfy its application requirements despite these faults. We describe the important lessons learnt from the ExScal experiment and suggest services and tools as a further aid to future large scale network deployments.


"Barrier Coverage With Wireless Sensors," The Eleventh Annual International Conference on Mobile Computing and Networking (ACM MobiCom) 2005, Cologne, Germany, 2005

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Authored By Santosh Kumar, Ten H. Lai, and Anish Arora on 07/11/2005
In old times, castles were surrounded by moats (deep trenches filled with water, and even alligators) to thwart or discourage intrusion attempts. One can now replace such barriers with stealthy and wireless sensors. In this paper, we develop theoretical foundations for laying barriers of wireless sensors.


"Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events," In The Fourth International Conference on Information Processing in Sensor Networks (IPSN), 2005

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Authored By Prabal Dutta, Mike Grimmer, Anish Arora, Steve Bibyk, and David Culler on 07/11/2005
We present the design of the eXtreme Scale Mote, a new sensor network platform for reliably detecting and classifying, and quickly reporting, rare, random, and ephemeral events in a large-scale, long-lived, and retaskable manner. This new mote was designed for the ExScal project which seeks to demonstrate a 10,000 node network capable of discriminating civilians, soldiers and vehicles, spread out over a 10km2 area, with node lifetimes approaching 1,000 hours of continuous operation on two AA alkaline batteries. This application posed unique functional, usability, scalability, and robustness requirements which could not be met with existing hardware, and therefore motivated the design of a new platform. The detection and classification requirements are met using infrared, magnetic, and acoustic sensors. The infrared and acoustic sensors are designed for low-power continuous operation and include asynchronous processor wakeup circuitry. The usability and scalability requirements are met by minimizing the frequency and cost of human-in-the-loop operations during node deployment, activation, and verification through improvements in the user interface, packaging, and configurability of the platform. Recoverable retasking is addressed by using a grenade timer that periodically forces a system reset. The key contributions of this work are a specific design point and general design methods for building sensor network platforms to detect exceptional events.


"Learn on the Fly: Quiescent Routing in Sensor Network Backbones," OSU-Technical Report OSU-CISRC-7/05-TR48

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Authored By Hongwei Zhang, Anish Arora, and Prasun Sinha on 07/11/2005
In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent difficulties in precisely estimating unicast link properties via those of broadcast beacons even if we make the length and transmission rate of beacons be the same as those of data packets. To circumvent the difficulties in link estimation, we propose to estimate unicast link properties directly via data traffic itself without using beacons. To this end, we design a beacon-free routing protocol Learn on the Fly (LOF). LOF estimates link quality based solely on data traffic, and it chooses routes by way of a locally measurable metric ELD, the expected MAC latency per unit-distance to the destination. Using a realistic sensor network traffic trace and an 802.11b testbed of 195 Stargates, we experimentally compare the performance of LOF with that of existing protocols, represented by the geography-unaware ETX and the geography-based PRD. We find that LOF reduces end-to-end MAC latency by a factor of 3, enhances energy efficiency by a factor up to 2.37, and improves route stability by 2 orders of magnitude. The results demonstrate the feasibility as well as potential benefits of data-driven beacon-free link estimation and routing.


ExScal Note Series - May 21, 2005

"Sprinkler: A Reliable and Scalable Data Dissemination Service for Wireless Embedded Devices," ExScal-OSU-EN04-2005-05-11

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Authored By Vinayak Naik, Anish Arora, Prasun Shnha, and Hongwei Zhang on 06/02/2005
We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices so as to avoid redundant transmissions ,and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. Sprinkler is tolerant to fail-stop and state corruption faults.We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and an indoor environment.


"Topology and Naming for Clean Point Demonstration," ExScal Note Series, ExScal-OSU-EN03-2004-12-05, December 2004

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Authored By Santosh Kumar and Anish Arora on 12/05/2004
This document describes the topology used in the Clean Point demo of the Extreme Scaling Project. This topology used 983 XSM motes and 203 stargates, out of which 45 stargates were used for Tier One communication. This topology is very similar to the topology described in the Original Topology document [1]. However, there are some differences as mentioned below:


"ExScal Topology for Node Deployment," ExScal Note Series, ExScal-OSU-EN00-2004-01-30, July 2004

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Authored By Santosh Kumar and Anish Arora on 01/30/2004
This note describes a topology for deploying 10,000 motes to protect an area of size 10,000mX500m (with an asset in the middle that runs parallel to the length) from intruders. The requirement for the topology is to allow detection of any moving intruder (people and vehicles) in less than 10 seconds and classify a car from a person. Due to the inherent unreliability of packet delivery in a sensor network, a point needs to be covered by more than one sensor to establish confidence in the detection. This note describes a topology which fulfills these requirements by placing a thick line of densely deployed sensors 500m away from the asset. By tiling the inner region with thin lines of sensors vertically and horizontally, it enables bounded uncertainty tracking, where the intruder's location is bounded in a box of size 360m X 180m.


Posters - February 1, 2005

Kansei Testbed Poster

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Authored By Vinayak Naik, Sandip Bapat, Hongwei Zhang, Chris Anderson, Gavin Fox, John Wiesemann, Anish Arora, Emre Ertin, and Rajiv Ramnath on 02/01/2005
This poster details the OSU Kansei testbed.


Tier 1 Poster

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Authored By Sandip Bapat, Anish Arora, OSU Team on 02/01/2005
This poster details tier 1 of the ExScal Demo.


Tier 2 Architecture Poster

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Authored By Anish Arora, Prasun Sinha, Emre Ertin, Vinayak Naik, Hongwei Zhang, Mukundan Sridharan, and Sandip Bapat on 02/01/2005
This poster provides information on the Tier 2 Network Protocol Suite and Monitoring.



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