About

Samir R. Das is a Professor and Chair in the Department of Computer Science at Stony Brook University. He received his Ph.D. in computer science from Georgia Institute of Technology and was previously educated at Jadavpur University in Kolkata, India, and the Indian Institute of Science in Bangalore, India. He also worked briefly at the Indian Statistical Institute. Prior to joining Stony Brook in 2002, Das was a faculty member at the University of Texas at San Antonio and the University of Cincinnati. His research focuses on mobile and wireless networking, including protocols, systems, and performance evaluation. He directs the Wireless Networking and Systems Lab (WINGS lab), which conducts research related to various forms of mobile and wireless networks such as local area networks, cellular wide area networks, ad hoc/mesh or sensor networks, and more recently, free space laser networks. Das has been recognized with several awards, including the CS department's faculty research award in 2014, faculty service award in 2010, the Best Paper Award at ACM MobiSys in 2007, IEEE Computer Society Distinguished Visitor from 2001 to 2003, and the NSF Faculty Early CAREER Award in 1998.

Research topics

• Computer Science
• Artificial Intelligence
• Real-time computing
• Computer network
• Computer Security
• Multimedia
• Software engineering
• Engineering
• Embedded system
• Computer vision
• Distributed computing

Selected publications

• Improving Communication Performance of Passive Backscattering Tags Using Collaborative Backscatter

  2025-04-22 · 2 citations

  article

  We propose collaborative backscatter techniques for tag-to-tag communication between battery-less RF tags. The low incident backscatter power and the limited processing ability in the passive receive circuits limit the performance of such links. By recruiting 'helper' tags to boost backscatter signals, such links can be substantially strengthened, depending upon the network topology and channel conditions. Two techniques are developed and evaluated on a prototype tag network, demonstrating close-to-optimal performance with low computational overhead.

  PublisherDOI

• Pavement subsurface spatial displacement monitoring system with embedded passive RF sensors (Conference Presentation)

  2025-05-12 · 2 citations

  article

  Pavement subsurface courses (layers) are critical components of transportation infrastructure but can be deteriorated by aging, overuse, and climate change, leading to structural failure. Current monitoring systems focus on monitoring surface cracking and do not provide information on subsurface courses. Existing systems also lack efficiency on large-scale 2D or 3D spatial coverage. This presentation presents a novel spatial displacement monitoring system with embeddable passive radio frequency (RF) sensors that estimate the distance between multiple sensors, enabling the detection of spatial displacement in pavement subsurface courses. Laboratory experiments demonstrate the system successfully detects the spatial displacement within the coverage region. The results demonstrate proof of concept on sensors' applicability in subsurface course spatial displacement monitoring, with potential for real-world implementation.

  PublisherDOI

• Robust and Energy-Efficient Channel Estimation in RF Backscatter Tag-to-Tag Network

  IEEE Journal of Radio Frequency Identification · 2025-01-01 · 5 citations

  article

  RF tag networks that utilize fully passive tags and backscatter communication offer a promising solution for ultralow-power and cost-effective Internet of Things (IoT) applications. Accurate estimation of tag-to-tag channel state information (CSI), particularly channel phase and path loss, is critical for enabling a wide range of such applications. However, the absence of active radio IQ demodulation and the limited computational resources in passive tags pose significant challenges for precise CSI estimation. To address these challenges, we propose a multi-phase modulator tag architecture and two optimized low-complexity channel estimation techniques: the Linear Least Squares (LLS) and the Three Reflection Loads (TRL) technique. Both techniques are designed to minimize estimation error while maintaining ultra-low computational cost, making them wellsuited for energy-constrained implementations. We validate the proposed techniques through measurements conducted in both indoor and outdoor environments with a discrete-component, battery-powered RF tag prototype. Experimental results show that the 90th percentile phase estimation error is 11. in outdoor settings and 25. in indoor settings under ambient RF excitation at 915 MHz. These phase errors correspond to ranging inaccuracies of approximately 10 mm and 23 mm, respectively, over tag-totag distances from 28 cm to 228 cm. These results highlight the robustness, energy efficiency, and scalability of the proposed channel estimation techniques for practical deployment in passive RF tag networks.

  PublisherDOI

• Channel Sensing Based Distance Estimation in Backscattering RF Tag Networks

  2025-05-25

  article

  A backscatter tag-to-tag network enables battery-less communication by harvesting energy and reflecting wireless signals between tags, making it ideal for energy-efficient IoT applications such as asset tracking, structural health monitoring, and environmental sensing. Accurate localization is crucial for these applications. While RSSI-based (Received Signal Strength Indicator) localization is the most common method for RF localization—estimating distance based on the received signal strength—it is often dependent on the position and power of the excitation source. We present a novel distance estimation method based on the estimation of the channel path loss and phase between tags, which is independent of the excitation source’s position and power. The experimental results demonstrate millimeter-level accuracy in 67% of cases and 99% accuracy within 17 cm for tag-to-tag distances up to 2.4 meters at 915 MHz.

  PublisherDOI

• Representation Similarity: A Better Guidance of DNN Layer Sharing for Edge Computing without Training

  2024-12-04 · 1 citations

  articleOpen access

  Edge computing has emerged as an alternative to reduce transmission and processing delay and preserve privacy of the video streams. However, the ever-increasing complexity of Deep Neural Networks (DNNs) used in video-based applications (e.g. object detection) exerts pressure on memory-constrained edge devices. Model merging is proposed to reduce the DNNs' memory footprint by keeping only one copy of merged layers' weights in memory. In existing model merging techniques, (i) only architecturally identical layers can be shared; (ii) requires computationally expensive retraining in the cloud; (iii) assumes the availability of ground truth for retraining. The re-evaluation of a merged model's performance, however, requires a validation dataset with ground truth, typically runs at the cloud. Common metrics to guide the selection of shared layers include the size or computational cost of shared layers or representation size. We propose a new model merging scheme by sharing representations (i.e., outputs of layers) at the edge, guided by representation similarity S. We show that S is extremely highly correlated with merged model's accuracy with Pearson Correlation Coefficient |r| > 0.94 than other metrics, demonstrating that representation similarity can serve as a strong validation accuracy indicator without ground truth. We present our preliminary results of the newly proposed model merging scheme with identified challenges, demonstrating a promising research future direction.

  PublisherOA PDFDOI

• A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems

  Sensors · 2024-09-18 · 6 citations

  articleOpen access

  Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.

  PublisherDOI

• A novel passive embeddable RF sensor for structural health monitoring

  2024-05-09 · 3 citations

  article

  A present challenge in structural health monitoring consists in the detection, localization, and quantification of small damage (e.g., small cracks) within large structures, such as bridges and buildings. Existing sensing solutions have several limitations, the most important being those related to the extent of spatial coverage by sensors and power supply. In this work, we will present proof-of-concept research for sub-millimeter displacement measurement using novel embeddable passive wireless radio frequency (RF) sensors. The novel sensors estimate relative displacement from phase shifts in the transmitted RF signal. The proposed system represents a novel paradigm in wireless sensing in structural health monitoring, as the wireless sensors are battery-less and will be deployed in a form of densely populated 3D network embedded within large volume of material.

  PublisherDOI

• OVIDA: Orchestrator for Video Analytics on Disaggregated Architecture

  2024-12-04 · 1 citations

  articleOpen access

  Millions of video cameras are deployed globally across major cities for learning-based video analytic (VA) applications, such as object detection. Video streams from the cameras are either sent over the wide-area network to be processed by the cloud or are (at least partially) processed in a local edge workstation, incurring significant latency and elevated financial costs. In this paper, to minimize reliance on the cloud and overcome the unavailability of high-compute workstations on edge, we investigate the use of heterogeneous and distributed embedded devices as edge nodes shared by multiple cameras to fully serve the video processing needs of a VA application (without requiring cloud support). We present OVIDA, an edge-only orchestrator to deploy VA application(s) on a distributed edge environment to maximize accuracy. Given the resource-constrained nature of edge nodes, OVIDA disaggregates the VA application pipeline into multiple modules. OVIDA's core functionality and contributions are: (i) optimizing the placement and replication of the VA application modules across the edge nodes to maximize the throughput, and in turn, accuracy; and (ii) an adaptive model selection algorithm for VA modules based on accuracy-throughput tradeoff to maximize accuracy in response to varying load conditions. To further improve performance, OVIDA employs a central-queue-based design (instead of the usual push-based design), which also obviates the need for complex load balancing algorithms. We implement OVIDA on top of Kubernetes and evaluate its performance for three VA applications, supported over a heterogeneous edge cluster under varying network conditions. When compared against several baselines in our evaluation, we achieve throughput and accuracy gains of at least 51% and 28%.

  PublisherOA PDFDOI

• Cross-Layer Scheduling in QUIC and Multipath QUIC for 360-Degree Video Streaming

  2024-04-21 · 7 citations

  articleSenior author

  The emergence of immersive multimedia content over mobile devices introduces applications that demand superior data bandwidth and low delay in network connections. Especially, the required network bandwidth and delays in 360-degree video streaming make it hard to achieve high QoE using TCP over inherently variable wireless networks. To overcome such limi-tations, this paper explores the use of QUIC and its multipath extension (Multipath QUIC or MPQUIC) for 360-degree video streaming. We also present a Cross-Layer Scheduling mechanism (CLS) for QUIC and MPQUIC to address further the lack of prioritization in QUIC's stream multiplexing. CLS uses appli-cation layer information, such as object sizes and priorities, and network layer information, such as network delays and bandwidth. It uses a variation of a job scheduling algorithm to schedule segments for QUIC streams. We implement CLS in the application layer without modifying QUIC/MPQUIC and follow the MPEG-DASH standard for video streaming. Our evaluation demonstrates significant improvement in user's QOE including rebuffering over baseline QUIC or TCP.

  PublisherDOI

• Optimized Channel Phase Estimation in Passive RF Tag Network

  2024-06-04 · 2 citations

  article

  We present a method for passive wireless channel estimation in RF tag-to-tag link. The technique has a low computational complexity, a small memory footprint and requires only one additional port in the modulator design of RF tag. The performance of the proposed method is evaluated on distance estimation task. The performance in the range of tag-to-tag distance from 28 cm to 228 cm is limited to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10^{\circ}(9 \text{mm})$</tex>. The demonstrated performance is comparable to performance of distance estimation techniques based on RFID technology which utilize RFID reader and are not scalable as the proposed method. The proposed method is amenable to deployment in near zero power operation RF-powered tags and it enriches RF tags with the ability to passively ‘fingerprint’ their surrounding.

  PublisherDOI

Recent grants

• NeTS-WN: Collaborative Research: A Measurement-Driven Physical-Interference-Based Approach for the Design of Mesh Networks

  NSF · $200k · 2007–2010

• NeTS: Small: Collaborative Research: Understanding Traffic Dynamics in Cellular Data Networks and Applications to Resource Management

  NSF · $320k · 2011–2015

• U.S.-India Cooperative Research: Multichannel Multihop Wireless LANs for Rapid Deployment

  NSF · $29k · 2004–2009

• Collaborative Research: Measurement-Augmented Spectrum Databases for White Spaces

  NSF · $345k · 2014–2018

• Collaborative Research: NECO: A Market-Driven Approach to Dynamic Spectrum Sharing

  NSF · $422k · 2008–2013

Frequent coauthors

• Petar M. Djurić

  Stony Brook University

  24 shared

• Arani Bhattacharya

  23 shared

• Himanshu Gupta

  National Institute of Technology Kurukshetra

  21 shared

• Milutin Stanaćević

  Stony Brook University

  20 shared

• Anand Prabhu Subramanian

  17 shared

• Akshay Athalye

  Stony Brook University

  15 shared

• Ayon Chakraborty

  Indian Institute of Technology Madras

  14 shared

• Mallesham Dasari

  Universidad del Noreste

  13 shared

Awards & honors

• CS department's faculty research award 2014
• faculty service award 2010
• Best Paper Award in ACM MobiSys 2007
• IEEE Computer Society Distinguished Visitor, 2001-2003
• NSF Faculty Early CAREER Award, 1998