Samir R. Das

Bio: Samir R. Das is an academic researcher from Stony Brook University. The author has contributed to research in topics: Wireless network & Physics. The author has an hindex of 58, co-authored 186 publications receiving 29007 citations. Previous affiliations of Samir R. Das include University of Texas at San Antonio & University of Cincinnati.

Ad hoc network routing for hotspot mitigation and load balancing in wireless LANs

Abstract: A method for balancing network load among mobile hosts includes determining and monitoring a performance of a first access point connected to a mobile host by an infrastructure network, determining that the performance of the first access point is undesirable, and virtualizing a network interface of the mobile host connected to the first access point, wherein an ad hoc network are established in addition to the infrastructure network. The method further including determining a route to a second access point through at least one cooperating mobile host, and switching a connection of the mobile host to the second access point via the route to the second access point, wherein the connection is established via the ad hoc network.

Understanding spatial relationships in resource usage in cellular data networks

Abstract: We conduct a detailed measurement analysis to investigate the spatial characteristics of network resource usage using a large-scale data set collected ‘in situ’ in a nationwide 3G cellular data network. The data set spans over thousands of base stations. We first characterize the spatial correlation in radio resource usage using different statistical techniques. The analysis shows existence of significant spatial correlation that varies during the day, peaking during the middle of the day and waning in the middle of the night. We also use the notion of spectral clustering to show how base stations can be clustered based on how correlated they are in terms of radio resource usage. We show that this produces spatially connected clusters. We also show that only a few clusters exist when clustered optimally. Finally, we use the concept of Granger causality to understand the underlying functional connectivity and flow of influence in the network. We show that roughly one-third of neighboring base station pairs exhibit statistically significant Granger causality, and long causal paths exist in the network. Our observations can lead to development of new techniques for network monitoring and resource management in future cellular data networks.

Advancing User Quality of Experience in 360-degree Video Streaming

Abstract: Conventional streaming solutions for streaming 360- degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach.We also develop an end-to-end testbed using standardscompliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques – two for conventional adaptive video streaming and two for 360- degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality.

Combining optimism limiting schemes in time warp based parallel simulations

Abstract: The time warp protocol is considered to be an effective synchronization mechanism for parallel discrete event simulation (PDES). However, it is widely recognized that it suffers over-optimistic behavior on the part of the simulation processes that may be very harmful for performance. In current literature, two techniques have been used to counteract this problem: throttling of over-optimistic processes; and scheduling or load balancing. However, the study of these techniques has been primarily done in isolation. We demonstrate using a parameterized simulation model of time warp that an appropriate combination of throttling and global scheduling using LP migration can be very beneficial for performance compared to any one of these schemes acting in isolation. This study forms the basis of the design of more powerful control schemes that use a combination of multiple techniques.

Reading detection in real-time

Abstract: Observable reading behavior, the act of moving the eyes over lines of text, is highly stereotyped among the users of a language, and this has led to the development of reading detectors-methods that input windows of sequential fixations and output predictions of the fixation behavior during those windows being reading or skimming. The present study introduces a new method for reading detection using Region Ranking SVM (RRSVM). An SVM-based classifier learns the local oculomotor features that are important for real-time reading detection while it is optimizing for the global reading/skimming classification, making it unnecessary to hand-label local fixation windows for model training. This RRSVM reading detector was trained and evaluated using eye movement data collected in a laboratory context, where participants viewed modified web news articles and had to either read them carefully for comprehension or skim them quickly for the selection of keywords (separate groups). Ground truth labels were known at the global level (the instructed reading or skimming task), and obtained at the local level in a separate rating task. The RRSVM reading detector accurately predicted 82.5% of the global (article-level) reading/skimming behavior, with accuracy in predicting local window labels ranging from 72-95%, depending on how tuned the RRSVM was for local and global weights. With this RRSVM reading detector, a method now exists for near real-time reading detection without the need for hand-labeling of local fixation windows. With real-time reading detection capability comes the potential for applications ranging from education and training to intelligent interfaces that learn what a user is likely to know based on previous detection of their reading behavior.

An application-specific protocol architecture for wireless microsensor networks

Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

Directed diffusion: a scalable and robust communication paradigm for sensor networks

Abstract: Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

Epidemic routing for partially-connected ad hoc networks

Abstract: Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.