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Träfflista för sökning "WFRF:(Moothedath Vishnu Narayanan) "

Sökning: WFRF:(Moothedath Vishnu Narayanan)

  • Resultat 1-10 av 11
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1.
  • Al-Atat, Ghina, et al. (författare)
  • The Case for Hierarchical Deep Learning Inference at the Network Edge
  • 2023
  • Ingår i: NetAISys 2023 - Proceedings of the 1st International Workshop on Networked AI Systems, Part of MobiSys 2023. - : Association for Computing Machinery (ACM). ; , s. 13-18
  • Konferensbidrag (refereegranskat)abstract
    • Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, developing tinyML models is an area of active research - DL models with reduced computation and memory storage requirements - that can be embedded on these devices. However, tinyML models have lower inference accuracy. On a different front, DNN partitioning and inference offloading techniques were studied for distributed DL inference between EDs and Edge Servers (ESs). In this paper, we explore Hierarchical Inference (HI), a novel approach proposed in [19] for performing distributed DL inference at the edge. Under HI, for each data sample, an ED first uses a local algorithm (e.g., a tinyML model) for inference. Depending on the application, if the inference provided by the local algorithm is incorrect or further assistance is required from large DL models on edge or cloud, only then the ED offloads the data sample. At the outset, HI seems infeasible as the ED, in general, cannot know if the local inference is sufficient or not. Nevertheless, we present the feasibility of implementing HI for image classification applications. We demonstrate its benefits using quantitative analysis and show that HI provides a better trade-off between offloading cost, throughput, and inference accuracy compared to alternate approaches.
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2.
  • Alsakati, Molham, et al. (författare)
  • Performance of 802.11be Wi-Fi 7 with Multi-Link Operation on AR Applications
  • 2023
  • Ingår i: 2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC. - : Institute of Electrical and Electronics Engineers (IEEE).
  • Konferensbidrag (refereegranskat)abstract
    • Since its first release in the late 1990s, Wi-Fi has been updated to keep up with evolving user needs. Recently, Wi-Fi and other radio access technologies have been pushed to their edge when serving Augmented Reality (AR) applications. AR applications require high throughput, low latency, and high reliability to ensure a high-quality user experience. The 802.11be amendment - which will be marketed as Wi-Fi 7 - introduces several features that aim to enhance its capabilities to support challenging applications like AR. One of the main features introduced in this amendment is Multi-Link Operation (MLO) which allows nodes to transmit and receive over multiple links concurrently. When using MLO, traffic is distributed among links using an implementation-specific traffic-to-link allocation policy. This paper aims to evaluate the performance of MLO, using different policies, in serving AR applications compared to Single-Link (SL). Experimental simulations using an event-based Wi-Fi simulator have been conducted. Our results show the general superiority of MLO when serving AR applications. MLO achieves lower latency and serves a higher number of AR users compared to SL with the same frequency resources. In addition, increasing the number of links can improve the performance of MLO. Regarding traffic-to-link allocation policies, we found that policies can be more susceptible to channel blocking, resulting in possible performance degradation.
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3.
  • Moothedath, Vishnu Narayanan, et al. (författare)
  • Distributed Pareto Optimal Beamforming for the MISO Multi-Band Multi-Cell Downlink
  • 2020
  • Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 19:11, s. 7196-7209
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we consider a multi-cell multi-band downlink where the base station (BS) in each cell has multiple transmit antennas. Each cell has one active mobile station (MS) with a single receive antenna and treats interference from the other cells as noise. There is a sum transmit power constraint for each BS over all the bands. An alternating maximization (AM) algorithm is proposed to determine the optimal power allocation among the bands and the optimal beamforming vectors for each BS in each band. This algorithm can be implemented in a distributed manner with limited exchange of interference constraints between the BSs, and only local channel state information at each BS. The proposed algorithm alternates between: (1) weighted sum-rate (WSR) optimization for the beamformers in each band for a given power allocation, and (2) optimal power allocation across bands for a given set of beamformers. For the 2-cell and 3-cell settings the WSR optimization in each band is significantly simplified using analytical solutions for the sub-problems. The power allocation across bands for a given set of beamformers is obtained analytically in all cases. Numerical results show good convergence properties and significant performance gain using the proposed AM algorithm compared to: (i) equal power allocation across bands and weighted sum-rate optimization in each band, (ii) zero-forcing (ZF) beamforming, and (iii) maximal ratio transmission (MRT) beamforming.
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4.
  • Moothedath, Vishnu Narayanan, et al. (författare)
  • Energy Efficient Sampling Policies for Edge Computing Feedback Systems
  • 2022
  • Ingår i: IEEE Transactions on Mobile Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1233 .- 1558-0660. ; , s. 1-1
  • Tidskriftsartikel (refereegranskat)abstract
    • We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of redundant samples. On the other hand, lower sampling frequency results in higher delay in detecting the event, thus increasing the idle energy usage and degrading the quality of experience. We quantify this trade-off as a weighted function between the number of samples and the sampling interval. We solve the minimisation problem for exponential and Rayleigh distributions, for the random time to the event of interest. We prove the convexity of the objective functions by using novel techniques, which can be of independent interest elsewhere. We argue that adding an initial offset to the periodic sampling can further reduce the energy consumption and jointly compute the optimum offset and sampling interval. We apply our framework to two practically relevant applications and show energy savings of up to 36% when compared to an existing periodic scheme. 
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5.
  • Moothedath, Vishnu Narayanan (författare)
  • Energy-Optimal Sampling for Edge Computing Feedback Systems : Aperiodic Case
  • 2022
  • Ingår i: 2022 IEEE/ACM 7TH SYMPOSIUM ON EDGE COMPUTING (SEC 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 322-328
  • Konferensbidrag (refereegranskat)abstract
    • We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that processes them and generates feedback for a user. Sampling the system with the maximum allowed frequency results in the timely detection of relevant events with minimum delay. However, it incurs high energy costs and causes unnecessary usage of network and compute resources via communication and processing of redundant samples. On the other hand, an infrequent sampling result in a higher delay in detecting the relevant event, thus increasing the idle energy usage and degrading the quality of experience in terms of responsiveness of the system. We quantify this sampling frequency trade-off as a weighted function between the number of samples and the responsiveness. We propose an energy-optimal aperiodic sampling policy that improves over the state-of-the-art optimal periodic sampling policy. Numerically, we show the proposed policy provides a consistent improvement of more than 10% over the state-of-the-art.
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6.
  • Moothedath, Vishnu Narayanan, et al. (författare)
  • Energy-Optimal Sampling of Edge-Based Feedback Systems
  • 2021
  • Ingår i: 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
  • Konferensbidrag (refereegranskat)abstract
    • We study a problem of optimizing the sampling interval in an edge-based feedback system, where sensor samples are offloaded to a back-end server which process them and generates a feedback that is fed-back to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of some redundant samples. On the other hand, lower sampling frequency results in a higher delay in detecting an event of interest thus increasing the idle energy usage and degrading the quality of experience. We propose a method to quantify this trade-off and compute the optimal sampling interval, and use simulation to demonstrate the energy savings.
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7.
  • Moothedath, Vishnu Narayanan, et al. (författare)
  • Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge
  • 2024
  • Ingår i: IEEE Transactions on Machine Learning in Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2831-316X. ; , s. 1-1
  • Tidskriftsartikel (refereegranskat)abstract
    • We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a microcontroller unit, embedded with a small-size ML model (S-ML) for a generic classification application and an Edge Server (ES) that hosts a large-size ML model (L-ML). Since the inference accuracy of S-ML is lower than that of the L-ML, offloading all the data samples to the ES results in high inference accuracy, but it defeats the purpose of embedding S-ML on the ED and deprives the benefits of reduced latency, bandwidth savings, and energy efficiency of doing local inference. In order to get the best out of both worlds, i.e., the benefits of doing inference on the ED and the benefits of doing inference on ES, we explore the idea of Hierarchical Inference (HI), wherein S-ML inference is only accepted when it is correct, otherwise the data sample is offloaded for L-ML inference. However, the ideal implementation of HI is infeasible as the correctness of the S-ML inference is not known to the ED. We thus propose an online meta-learning framework that the ED can use to predict the correctness of the S-ML inference. In particular, we propose to use the probability corresponding to the maximum probability class output by S-ML for a data sample and decide whether to offload it or not. The resulting online learning problem turns out to be a Prediction with Expert Advice (PEA) problem with continuous expert space. For a full feedback scenario, where the ED receives feedback on the correctness of the S-ML once it accepts the inference, we propose the HIL-F algorithm and prove a sublinear regret bound √ n ln(1/λ min )/2 without any assumption on the smoothness of the loss function, where n is the number of data samples and λ min is the minimum difference between any two distinct maximum probability values across the data samples. For a no-local feedback scenario, where the ED does not receive the ground truth for the classification, we propose the HIL-N algorithm and prove that it has O ( n 2/3 ln 1/3 (1/λ min )) regret bound. We evaluate and benchmark the performance of the proposed algorithms for image classification application using four datasets, namely, Imagenette and Imagewoof [1], MNIST [2], and CIFAR-10 [3].
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8.
  • Moothedath, Vishnu Narayanan, et al. (författare)
  • Pareto Optimal Distributed Beamforming for the Multi-Band Multi-Cell Downlink
  • 2017
  • Ingår i: Proceedings GLOBECOM 2017 - 2017 IEEE Global Communications Conference. - : Institute of Electrical and Electronics Engineers (IEEE).
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we consider a multi-cell multi-band downlink where the base-station (BS) in each cell has multiple transmit antennas and each cell has one active mobile station (MS) with a single receive antenna and treats interference from the other cells as noise. There is a sum transmit power constraint for each BS over all the bands. An alternating maximization (AM) algorithm is proposed to determine the optimal power allocation among the bands and the optimal beamforming vectors for each BS in each band. This algorithm can be implemented in a distributed manner with limited exchange of interference constraints between the BSs. Using simulations, the algorithm is shown to converge to the weighted sum rate optimal point on the Pareto boundary of the achievable rate region. Furthermore, significant performance gain is observed compared to: (i) equal power allocation across bands and weighted sum rate optimization in each band, (ii) zero-forcing (ZF) beamforming, and (iii) maximal ratio transmission (MRT) beamforming.
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9.
  • Mostafavi, Seyed Samie, et al. (författare)
  • ExPECA : An Experimental Platform for Trustworthy Edge Computing Applications
  • 2023
  • Ingår i: 2023 IEEE/ACM SYMPOSIUM ON EDGE COMPUTING, SEC 2023. - : Association for Computing Machinery (ACM). ; , s. 294-299
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidthintensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking.
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10.
  • Olguín Muñoz, Manuel Osvaldo, 1992-, et al. (författare)
  • Ainur : A Framework for Repeatable End-to-End Wireless Edge Computing Testbed Research
  • 2022
  • Ingår i: European Wireless Conference, EW 2022. - : VDE VERLAG GMBH. ; , s. 139-145
  • Konferensbidrag (refereegranskat)abstract
    • Experimental research on wireless networking in combination with edge and cloud computing has been the subject of explosive interest in the last decade. This development has been driven by the increasing complexity of modern wireless technologies and the extensive softwarization of these through projects such as a Open Radio Access Network (O-RAN). In this context, a number of small- to mid-scale testbeds have emerged, employing a variety of technologies to target a wide array of use-cases and scenarios in the context of novel mobile communication technologies such as 5G and beyond-5G. Little work, however, has yet been devoted to developing a standard framework for wireless testbed automation which is hardwareagnostic and compatible with edge- and cloud-native technologies. Such a solution would simplify the development of new testbeds by completely or partially removing the requirement for custom management and orchestration software. In this paper, we present the first such mostly hardwareagnostic wireless testbed automation framework, Ainur. It is designed to configure, manage, orchestrate, and deploy workloads from an end-to-end perspective. Ainur is built on top of cloudnative technologies such as Docker, and is provided as FOSS to the community through the KTH-EXPECA/Ainur repository on GitHub. We demonstrate the utility of the platform with a series of scenarios, showcasing in particular its flexibility with respect to physical link definition, computation placement, and automation of arbitrarily complex experimental scenarios.
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