Multi-modal sensor system integrating COTS technology for surveillance and tracking

Kozma, Róbert; Wang, Lan; Iftekharuddin, Khan M.; McCracken, Ernest; Khan, Mohammed U.; Islam, Khandakar; Demirer, R. Murat

doi:10.1109/radar.2010.5494467

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In this work we have developed an experimental methodology for a distributed multi-modal system for surveillance and tracking using radar and supplementary sensor data. Our research goal is to develop a prototype sensor network using commercial off-the-shelf components and to provide innovative solutions for detecting, tracking and classifying different targets and events under conditions with high noise and clutter [ 15 ]. Our distributed system supports a pervasive sensor network approach and it consists of a suite of radar transceivers, supplemented with acoustic, vibration, and light sensors.…”

Section: Discussionmentioning

confidence: 99%

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

Kozma

Wang

Iftekharuddin

et al. 2012

Sensors

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The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

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Section: Discussionmentioning

confidence: 99%

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

Kozma

Wang

Iftekharuddin

et al. 2012

Sensors

Self Cite

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“…Sensor systems or motes are developed in favor of the low-cost, availability, sensor versatility and time-to-market. Distributed data processing across network solving fusion challenges is also studied in [29], [26], [27]. However, these approaches often involve permanent deployment or installation on civil infrastructures.…”

Section: E Data Fusion and System Impactmentioning

confidence: 99%

SIROM3 -- A Scalable Intelligent Roaming Multi-modal Multi-sensor Framework

Zhang

Qiu

Shamsabadi

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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Understanding the future transportation infrastructure performance demands a smart Cyber-Physical Systems (CPS) approach integrating heterogeneous sensors, versatile computing systems, and mobile agents. However, due to sensor versatility and computing intricacy, designing such systems faces challenges of immense complexity in mobile sensor fusion, big data handling, system scalability, and integration. This paper introduces SIROM 3 , a Scalable Intelligent ROaming Multi-Modal Multi-Sensor framework, for next generation transportation infrastructure performance inspection. SIROM 3 offers a scalable and expandable framework through orthogonally abstracting software / hardware structures in a layered Run-Time Environment (RTE), which facilities sensor fusion, distributed computing, communication and mobile services. A Heterogeneous Stream File-system Overlay (HSFO) and a flexible plugin system (PLEX) are embedded in SIROM 3 to simplify big data storage, processing, and correlation. To evaluate the scalability of SIROM 3 , we implemented a mobile sensing system of 30 heterogeneous sensors and 5 computing platforms coordinated by 1 data center. SIROM 3 's expandability is highlighted by adding an advanced radar platform which required less than 50 lines of C++ code for integration. Over 20 terabytes of data covering 300 miles have been collected, aggregated, and fused using SIROM 3 for comprehending the pavement dynamics of the entire city of Brockton, MA. SIROM 3 offers a unified solution and ideal research platform for rapid, intelligent and comprehensive evaluation of tomorrow's transportation infrastructure performance using heterogeneous systems.Current roadway pavement monitoring methodologies often face challenges such as intrusive data gathering (e.g. stopping traffic), manual efforts and subsequently infrequent data collection and limited coverage [33]. Hence, non-intrusive, automated, fast, and adaptive solutions for data collection and infrastructure assessment are necessary. Heterogeneous sensor systems such as Multi-Modal Multi-Sensor (MMMS) systems are promising aiming for adaptability, automated operations, power and fuel efficiency, and ubiquitous assessment capability in multiple data domains [9], [12]. Integrating MMMS systems onto a mobile platform creates a Roaming Multi-Modal MultiSensor (RMMMS) system to collect multi-modal data under roaming conditions. However, RMMMS systems are challenging to develop and operate due to the heterogeneity in sensors, data types and synchronization principles, as well as the sheer number of sensors. Typically sensor systems are designed tailor-made to a specific application, which impedes the overall scalability. Meanwhile, system complexity increases exponentially with computational diversities, network-wide collaboration and data correlation. In addition, data-intensive sensors produce a large volume of streaming data in real-time raising the importance to effectively store, access, and process big data. Additionally, deploying multiple RMMMS to increase geogr...

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“…An experimental methodology has been developed to show the feasibility of low-cost, COTS sensor nodes in a distributed multi-modal system for surveillance and tracking of ground targets in conditions with high noise and clutter in [4]. With the use of low cost radar transceivers, supplemented by acoustic, vibration, magnetic, and infrared sensors, this method provides efficient solutions for detecting, tracking and classifying different types of targets.…”

Section: Related Workmentioning

confidence: 99%

Centroidal voronoi tessellation based energy efficient clustering protocol for heterogeneous wireless sensor and robot networks

Haque

Rahman

et al. 2014

2014 17th International Conference on Computer and Information Technology (ICCIT)

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In this paper, we propose a novel clustering protocol for wireless sensor and robot networks (WSRN) to ensure efficient energy usage and maintain maximum connectivity among the sensors. Our algorithm mainly works in two phases: (i) creates optimal number of clusters in the region of interest via threepoint centroidal voronoi diagram. (ii) aggregates data from the members of each cluster and transfers to base station. This protocol is designed using double layered adaptive clustering and unknown region exploration which can be changed when needed for specific application. Additionally, this protocol works without the prior knowledge of the deployment region. We compared the results with LEACH and LEACH-C as a proof of concept. Specifically, simulation results exploit higher level of performance improvement in terms of energy dissipation, node failure, transmission overhead, and data aggregation among large number of mobile sensors. Finally, we analyzed the protocol with different settings which manifest the viability of our design.

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Multi-modal sensor system integrating COTS technology for surveillance and tracking

Cited by 9 publications

References 6 publications

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

SIROM3 -- A Scalable Intelligent Roaming Multi-modal Multi-sensor Framework

Centroidal voronoi tessellation based energy efficient clustering protocol for heterogeneous wireless sensor and robot networks

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