Faculty/ DEEPAK GANGADHARAN
DEEPAK GANGADHARAN
Assistant Professor
mail_outline
deepak.g[at]iiit.ac.in
language
language
language
language
Areas of Expertise
Scalable Design and Performance Analysis of Edge-based IoT systems
Analysis and Design of Real-Time Systems/Cyber-Physical Systems
Design Methodologies for Embedded Systems
Intelligent Transportation Systems
Selected Publications
RL-ALMS: Reinforcement Learning-based Adaptive and Lightweight Model Selection Framework for Energy-Efficient Lane Detection in Electric Vehicles
Rhuthik P,Deepak Gangadharan
Digital System Design, DSD, 2025
@inproceedings{bib_RL-A_2025, AUTHOR = {Rhuthik P, Deepak Gangadharan}, TITLE = {RL-ALMS: Reinforcement Learning-based Adaptive and Lightweight Model Selection Framework for Energy-Efficient Lane Detection in Electric Vehicles}, BOOKTITLE = {Digital System Design}. YEAR = {2025}}
Lane detection is critical for Advanced Driver Assistance Systems (ADAS) in autonomous vehicles. Deep learning models offer high accuracy but are computationally demanding for resource-constrained edge platforms, while lightweight image processing algorithms are energy-efficient but often falter in complex scenarios. This paper presents RL-ALMS (Reinforcement Learning-based Adaptive and Lightweight Model Selection), a framework that dynamically switches between deep learning and image processing lane detection models in electric vehicles (EVs). RL-ALMS employs a contextual multi-armed bandit with Thompson sampling to intelligently select the optimal model based on road conditions, battery state, and accuracy needs. The novelty lies in its system-level integration of a battery-aware reward function and a sustainability-driven selection mechanism, specifically designed to balance real-time detection accuracy with long-term EV energy constraints. Experimental results on an edge computing platform demonstrate RL-ALMS achieves 90.3% average accuracy over a 50km journey while maintaining 81.2% battery reserve, significantly outperforming fixed high-accuracy (battery depletion) and energy-efficient (64.5% accuracy) strategies.
RL-ALMS maintains consistent performance across diverse conditions, representing a practical advancement for energy-constrained, real-time lane detection in EVs.
Architecting Digital Twins for Intelligent Transportation Systems
Hiya Bhatt,Sahil,Karthik Vaidhyanathan,Rahul Biju,Deepak Gangadharan
IEEE International Conference on Software Architecture Companion, ICSA, 2025
@inproceedings{bib_Arch_2025, AUTHOR = {Hiya Bhatt, Sahil, Karthik Vaidhyanathan, Rahul Biju, Deepak Gangadharan}, TITLE = {Architecting Digital Twins for Intelligent Transportation Systems}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2025}}
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.
Architecting Digital Twin for Smart City Systems: A Case Study
Kanigolla Naga Venkata Bala Likhith,Gaurav Pal,Karthik Vaidhyanathan,Deepak Gangadharan,Anuradha Vattem
International Workshop on Digital Twin Architecture, TwinArch, 2024
@inproceedings{bib_Arch_2024, AUTHOR = {Kanigolla Naga Venkata Bala Likhith, Gaurav Pal, Karthik Vaidhyanathan, Deepak Gangadharan, Anuradha Vattem}, TITLE = {Architecting Digital Twin for Smart City Systems:
A Case Study}, BOOKTITLE = {International Workshop on Digital Twin Architecture}. YEAR = {2024}}
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency,
especially with the rise of Internet of Things (IoT) devices.
Smart cities use these innovations to manage resources better
and enhance resident’s quality of life. However, implementing
smart city initiatives comes with challenges like monitoring,
maintaining, and testing urban infrastructure. Digital Twin
(DT) entails the connection of physical facilities or devices
with their digital counterparts, facilitating real-time monitoring,
manipulation, and predictive analysis of their behavior. This
concept offers a virtual replica of assets, processes, and systems,
enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in
planning maintenance activities and conducting comprehensive
testing, thereby enhancing the resilience and efficiency of smart
city systems. Particularly in the context of managing water
networks, DT technology holds significant promise. Visualization
capabilities provide intuitive insights into the system’s behavior,
facilitating informed decision-making. This visualization, coupled
with actuation capabilities, enables control actions based on
predictive analytics and optimization algorithms, allowing for
proactive management of water resources and infrastructure. To
this end, in this paper, we present the architecture of WaterTwin,
a DT developed for water quality networks in smart city systems.
We demonstrate our approach through the use of a water quality
network at the smart city living lab, IIIT Hyderabad campus.
Preliminary Modeling of Energy-Aware Integrated Allocations in Robotic Mobile Fulfillment Systems
Deepak Gangadharan
International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA, 2024
@inproceedings{bib_Prel_2024, AUTHOR = {Deepak Gangadharan}, TITLE = {Preliminary Modeling of Energy-Aware Integrated Allocations in Robotic Mobile Fulfillment Systems}, BOOKTITLE = {International Conference on Embedded and Real-Time Computing Systems and Applications}. YEAR = {2024}}
The Robotic Mobile Fulfillment System (RMFS) is an automated technology to fulfill various types of orders (e.g., vehicle assembly) in modern warehouses or factories. In particular, battery-equipped mobile robots move racks that contain various components (Stock Keeping Unit) by visiting replenishment or picking stations to complete orders. We formulate the preliminary system model to optimize the order throughput and energy consumption of mobile robots, which consists of (i) rack allocation, (ii) task allocation, and (iii) route routing.
Online Partitioned Scheduling over RSU for Computation Offloading in Vehicular Edge Computing
Tanniru Abhinav Siddharth,Kethu Sesha Sarath Reddy,Joseph John Cherukara,Deepak Gangadharan
Vehicular Technology Conference, VTC, 2024
@inproceedings{bib_Onli_2024, AUTHOR = {Tanniru Abhinav Siddharth, Kethu Sesha Sarath Reddy, Joseph John Cherukara, Deepak Gangadharan}, TITLE = {Online Partitioned Scheduling over RSU for Computation Offloading in Vehicular Edge Computing}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2024}}
With advances in vehicular communications technology such as Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V), the computation of vehicular tasks has become distributed facilitated by computation offloading from the vehicular platform to road side units (RSUs) or the cloud infrastructure. In this work, due to communication overheads, we do not consider the cloud and only consider offloading horizontally across RSU nodes. However, the advantage of offloading depends upon how the tasks are scheduled across the RSUs. Unlike existing horizontal offloading works, this work explores the benefits of online task partitioned scheduling for computation offloading from multiple vehicles to RSUs or edge nodes. Specifically, we propose a new efficient timeslot-based online hybrid partitioned scheduling algorithm, which splits some tasks into subtasks and schedules them across RSU nodes while considering vehicle flow constraints. We compared and evaluated the effectiveness of our proposed hybrid partitioned scheduling algorithm with the fully partitioned algorithm. We also compared the performance of the aforementioned algorithms with an optimal scheduling algorithm utilizing several experiments conducted on a real-world vehicular dataset.
Architecting Digital Twin for Smart City Systems: A Case Study
Bala Likhith Kanigolla,Gaurav Pal,Karthik Vaidhyanathan,Deepak Gangadharan,Anuradha Vattem
International Conference on Software Architecture Companion, ICSA-C, 2024
@inproceedings{bib_Arch_2024, AUTHOR = {Bala Likhith Kanigolla, Gaurav Pal, Karthik Vaidhyanathan, Deepak Gangadharan, Anuradha Vattem}, TITLE = {Architecting Digital Twin for Smart City Systems: A Case Study}, BOOKTITLE = {International Conference on Software Architecture Companion}. YEAR = {2024}}
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident’s quality of life. However, implementing smart city projects comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin(DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This innovative concept offers a virtual replica of assets, processes and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of Smart City Systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights, while actuation enables control actions based on predictive analytics and optimization algorithms. To this end, in this paper, we present the architecture of WaterTwin, a DT we developed for water quality networks in smart city systems. We demonstrate our approach through the use case of a water quality network in the smart city living lab of the IIIT Hyderabad campus.
Edge-Server Workload Characterization in Vehicular Computation Offloading: Semantics and Empirical Analysis
BaekGyu Kim,Deepak Gangadharan
IEEE Access, ACCESS, 2024
@inproceedings{bib_Edge_2024, AUTHOR = {BaekGyu Kim, Deepak Gangadharan}, TITLE = {Edge-Server Workload Characterization in Vehicular Computation Offloading: Semantics and Empirical Analysis}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}
Edge server-assisted computation offloading enables vehicles to leverage server compute resources to deliver connected services, overcoming the limitations of onboard resources. Understanding the compute workloads of edge servers is crucial for effective resource management and scheduling, yet this task is challenging due to the complex interplay of factors such as vehicle mobility and computation offloading patterns. To address this, we propose an empirical analysis framework that systematically characterizes the compute workloads of edge servers. We begin by formalizing the relationships among three key aspects:
local load (generated by vehicles), composite load (imposed on edge servers), and traffic flow (vehicle mobility patterns). Our framework then uses models of the local load and traffic flow as inputs to generate the
composite loads on edge servers. Experiments were conducted by injecting between 600 and 5,000 vehicles
per hour in two distinct geographical areas, New York City and Tampa. We provide a quantitative analysis demonstrating how the composite loads on edge servers vary with changes in traffic flows, geographical
areas, and offloading patterns.
S2P: Two-Stage Superpixel Algorithm for Enhanced Lane Detection on Resource Constraint Edges
Rhuthik P,Vaddhiparthy S V S L N Surya Suhas,Pranav Kannan,Deepak Gangadharan
International Symposium on Industrial Embedded Systems, SIES, 2024
@inproceedings{bib_S2P:_2024, AUTHOR = {Rhuthik P, Vaddhiparthy S V S L N Surya Suhas, Pranav Kannan, Deepak Gangadharan}, TITLE = {S2P: Two-Stage Superpixel Algorithm for Enhanced Lane Detection on Resource Constraint Edges}, BOOKTITLE = {International Symposium on Industrial Embedded Systems}. YEAR = {2024}}
Lane marker identification is crucial for developing Intelligent Transportation Systems and Autonomous Vehicles. While deep learning models excel in accuracy, their high com- putational requirements make them unsuitable for low-power edge devices. While various image-processing-based algorithms are used to pre-process images at different stages, there is a lack of an efficient algorithm specifically designed to enhance the lane-modeling stage. This paper proposes a two-stage pre- processing algorithm, consisting of a static phase and a dy- namic phase, to implement a divide-and-conquer approach for enhancing existing lane-modeling algorithms. The static phase generates an initial pixel label map and seed locations, while the dynamic phase uses the pre-processed input image and seed locations to generate a dynamic pixel map for localizing lane marking zones. Focusing on the center two lanes, the proposed method improves overall accuracy and recall, which is critical for capturing smaller lane details. Experimental results demonstrate the superior performance of the proposed two-stage algorithm compared to traditional HT and PHT methods, with the two- stage algorithm outperforming the standard HT(0.88 vs 0.92) and PHT(0.89 vs 0.95) in terms of Average Accuracy and Recall. Additionally, the two-stage PHT method significantly reduces power consumption compared to the Ultra Fast Lane Detection (UFLD) model, making it ideal for low-power edge devices like the Raspberry Pi 4B and Jetson Nano.
Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs
Ashish Sharma,Vaddhiparthy S V S L N Surya Suhas,Goparaju Sai Usha Nagasri,Deepak Gangadharan,Harikumar Kandath
International Conference on Automation Science and Engineering, ICASE, 2024
@inproceedings{bib_Atte_2024, AUTHOR = {Ashish Sharma, Vaddhiparthy S V S L N Surya Suhas, Goparaju Sai Usha Nagasri, Deepak Gangadharan, Harikumar Kandath}, TITLE = {Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2024}}
This paper explores the critical issue of enhancing
cybersecurity measures for low-cost, Wi-Fi-based Unmanned
Aerial Vehicles (UAVs) against Distributed Denial of Service
(DDoS) attacks. In the current work, we have explored three
variants of DDoS attacks, namely Transmission Control Proto
col (TCP), Internet Control Message Protocol (ICMP), and TCP
+ ICMPflooding attacks, and developed a detection mechanism
that runs on the companion computer of the UAV system.
As a part of the detection mechanism, we have evaluated
various machine learning, and deep learning algorithms, such as
XGBoost, Isolation Forest, Long Short-Term Memory (LSTM),
Bidirectional-LSTM (Bi-LSTM), LSTM with attention, Bi
LSTM with attention, and Time Series Transformer (TST) in
terms of various classification metrics. Our evaluation reveals
that algorithms with attention mechanisms outperform their
counterparts in general, and TST stands out as the most
efficient model with a run time of ∼0.1 seconds. TST has
demonstrated an F1 score of 0.999, 0.997, and 0.943 for TCP,
ICMP, and TCP + ICMP flooding attacks respectively. In this
work, we present the necessary steps required to build an on
board DDoS detection mechanism. Further, we also present the
ablation study to identify the best TST hyperparameters for
DDoS detection, and we have also underscored the advantage
of adapting learnable positional embeddings in TST for DDoS
detection with an improvement in F1 score from 0.94 to 0.99.
Checkpointing-Aware End-to-End Data Age Analysis of Task Chains under Transient Faults
Sridhar Mallareddy,K Pavan Kumar,Deepak Gangadharan
IEEE International Symposium on Object Oriented Real-Time Distributed Computing, ISORC, 2024
@inproceedings{bib_Chec_2024, AUTHOR = {Sridhar Mallareddy, K Pavan Kumar, Deepak Gangadharan}, TITLE = {Checkpointing-Aware End-to-End Data Age Analysis of Task Chains under Transient Faults}, BOOKTITLE = {IEEE International Symposium on Object Oriented Real-Time Distributed Computing}. YEAR = {2024}}
Abstract—Safety-critical real-time systems are conventionally designed to meet specific hard real-time deadline requirements. Many applications in such systems consist of task chains, which exhibit complex temporal dependencies based on the activation rates of the tasks. As a result, there has been an increasing interest in analyzing the age of data (i.e., the time elapsed since the data was first updated) that propagates through the task chains as an important system performance parameter. Transient faults in safety-critical systems can lead to undesirable consequences. One of the most common methods used for fault tolerance in such systems is checkpointing. However, the interference due to checkpointing can significantly impact the data age, reducing the predictability of the system’s performance. This paper proposes an analytical framework for calculating the data age in real-time task chains with checkpointing for transient faults in both single-core and multi-core platforms. The proposed analysis has been validated by comparing it with extensive simulations of the execution of real-time task chain- based systems under faults. It shows comparable data age values with low time complexity.
Exploratory Study of oneM2M-based Interoperability Architectures for IoT: A Smart City Perspective
Vjs Pranavasri,Leo Francis,Gaurav Pal,Ushasri Mogadali,Anuradha Vattem,Karthik Vaidhyanathan,Deepak Gangadharan
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_Expl_2024, AUTHOR = {Vjs Pranavasri, Leo Francis, Gaurav Pal, Ushasri Mogadali, Anuradha Vattem, Karthik Vaidhyanathan, Deepak Gangadharan}, TITLE = {Exploratory Study of oneM2M-based Interoperability Architectures for IoT: A Smart City Perspective}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
The advent of the Internet of Things (IoT) has ushered in transformative possibilities for smart cities, with the potential to revolutionize urban living through enhanced connectivity and data-driven decision-making. However, the effective realization of IoT in smart cities hinges upon the seamless interoperability of diverse devices and systems. To address this critical need, the oneM2M standards initiative has emerged as a foundational framework for IoT interoperability. In this research paper, we perform an exploratory analysis of three prominent open-source oneM2M based interoperability systems—Mobius, OM2M, and ACME. We leverage an existing large-scale system provided by our Smart City Living Lab deployed at IIIT Hyderabad, sprawling a 66-acre campus featuring over 370 nodes across eight verticals. We investigate the architectural characteristics of each solution, considering their strengths and limitations in facilitating IoT interoperability. Through this analysis, our paper aims to provide valuable insights for stakeholders seeking to implement IoT interoperability solutions in the context of smart cities. By evaluating the strengths and limitations of Mobius, OM2M, and ACME, we seek to offer guidance for selecting the most suitable solution. Our analysis reveals that the optimal framework choice depends on specific quality constraints: Mobius excels in performance, while ACME offers advantages in ease of setup for smaller-scale implementations.
Leveraging Latent Temporal Features for Robust Fault Detection and Isolation in Hexacopter UAVs
Shivaan Sehgal,Aakash Maniar,Harikumar K,Deepak Gangadharan
International Conference on Automation, Robotics and Applications, ICARA, 2024
@inproceedings{bib_Leve_2024, AUTHOR = {Shivaan Sehgal, Aakash Maniar, Harikumar K, Deepak Gangadharan}, TITLE = {Leveraging Latent Temporal Features for Robust Fault Detection and Isolation in Hexacopter UAVs}, BOOKTITLE = {International Conference on Automation, Robotics and Applications}. YEAR = {2024}}
This paper introduces a novel approach for fault detection and localization in a motor of a Hexacopter UAV. The proposed two-stage architecture leverages Long Short-Term Memory (LSTM) networks for latent temporal feature extraction and Random Forest for localization. By combining them, we see improved fault detection and isolation performance. Our evaluations show the robustness of this approach in varying noise levels and real-world-like environments. Analysis of computational efficiency in rapid detection shows that the model identified faults within 2-5 time steps of the flight. Finally, we show that the proposed method surpasses classical statistical models and deep learning techniques in terms of overall accuracy (96.78%).
Time-Series based Fall Detection in Two-Wheelers
Goparaju Sai Usha Nagasri,Deepak Gangadharan,Keerthi Pothalaraju,Shriya Dullur,ARIHANT JAIN
Vehicular Technology Conference, VTC, 2023
@inproceedings{bib_Time_2023, AUTHOR = {Goparaju Sai Usha Nagasri, Deepak Gangadharan, Keerthi Pothalaraju, Shriya Dullur, ARIHANT JAIN}, TITLE = {Time-Series based Fall Detection in Two-Wheelers}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2023}}
Driving event recognition plays a crucial role in understanding and enhancing road safety. This research focuses on developing efficient time-series based models for Fall detection in two-wheelers. Traditional machine learning models proved inadequate in accurately classifying Fall scenarios due to their inability to capture temporal transitions in kinematic states. To address this limitation, time-series based Deep Learning (DL) models are proposed, utilizing Long Short-Term Memory (LSTM) networks. These networks enable direct learning from raw time series data, eliminating the need for manual feature engineering. Additionally, Bi-LSTMs were employed to capture contextual information from both past and future timesteps, further improving the model’s understanding of driving events. The architecture was enhanced with an attention mechanism to boost accuracy. Experimental results showcased that the proposed Bi-LSTM model achieved an overall accuracy of 97%, with a specific accuracy of approximately 92% in detecting Fall scenarios. This research contributes to the development of an accurate Time- series based system for Fall detection, facilitating improved road safety in the context of two-wheelers.
Dynamic Data Delivery Framework for Connected Vehicles via Edge Nodes with Variable Routes
Joseph John Cherukara,Vaddhiparthy s V S L N Surya Suhas,Deepak Gangadharan,Baek Gyu Kim
Vehicular Technology Conference, VTC, 2023
@inproceedings{bib_Dyna_2023, AUTHOR = {Joseph John Cherukara, Vaddhiparthy s V S L N Surya Suhas, Deepak Gangadharan, Baek Gyu Kim}, TITLE = {Dynamic Data Delivery Framework for Connected Vehicles via Edge Nodes with Variable Routes}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2023}}
With increasing connectivity and sophisticated software, modern vehicles are able to leverage different kinds of services provided by the environment. One such service recommended by the Automotive Edge Computing Consortium (AECC) is downloading high-definition map data by vehicles. This high volume of data can be provided to the vehicles when moving by pre-allocating resources on edge server nodes or roadside units if the routes are known apriori. However, this is not a realistic assumption to make in general. Therefore, in this work, we propose a two-stage optimization framework for efficient data delivery to connected vehicles via edge nodes while considering dynamic route changes. We have evaluated the efficiency of this proposed approach (considering a real-world dataset) with respect to (a) offline optimization strategies considering fixed routes and (b) a greedy approach considering route changes. Our proposed approach works considerably better than the existing approaches in the context of dynamic route changes.
Collision-Aware Data Delivery Framework for Connected Vehicles via Edges
Vaddhiparthy s V S L N Surya Suhas,Joseph John Cherukara,Deepak Gangadharan,Baek Gyu Kim
Vehicular Technology Conference, VTC, 2023
@inproceedings{bib_Coll_2023, AUTHOR = {Vaddhiparthy s V S L N Surya Suhas, Joseph John Cherukara, Deepak Gangadharan, Baek Gyu Kim}, TITLE = {Collision-Aware Data Delivery Framework for Connected Vehicles via Edges}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2023}}
Unmanned Aerial Vehicles (UAVs), particularly low-cost UAVs, have become increasingly important due to their wide range of applications and ease of use. However, with the rapid growth of the UAV market, the rising security concerns pose a greater risk. One such primary concern is location spoofing attacks which can compromise UAV's navigation system, making it crucial to analyze various location-based attacks. In this paper, we identify 16 such GPS spoofing scenarios based on environmental conditions, attack type, and spoof signal propagation path. We evaluate these scenarios based on various GPS parameters like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), GPS satellite count in view, and avg signal-to-noise power density (CN0). We then analyze the variations in GPS parameters for various such attack scenarios. Further, we analyze the impact of distance on average CN0 and the effect of satellite count on effective spoofable distance. We also discuss several critical insights which are empirically observed during our experimental trials. Our experiments revealed that the natural conditions within indoor and outdoor scenarios can vary considerably, and effective spoofable distance can be up to 100 meters when the satellite count is less than 10.
Empirical Composite Workload Analysis for RSU-Assisted Computation Offloading in Connected Vehicle Services
BaekGyu Kim,Deepak Gangadharan
IEEE International Symposium on Workload Characterization, IISWC, 2023
Abs | | bib Tex
@inproceedings{bib_Empi_2023, AUTHOR = {BaekGyu Kim, Deepak Gangadharan}, TITLE = {Empirical Composite Workload Analysis for RSU-Assisted Computation Offloading in Connected Vehicle Services}, BOOKTITLE = {IEEE International Symposium on Workload Characterization}. YEAR = {2023}}
The RSU(Road-Side Unit)-assisted computation offloading allows vehicles to use the RSU’s compute resources to provide connected services which cannot be done due to on-board resource limitations. We propose the empirical analysis framework that can systematically characterize such RSU’s compute workloads. We first formalize the relationship of the three aspects: the local load (generated from vehicles), the composite load (imposed on RSUs) and the traffic flow (mobility patterns of vehicles). Then, our framework takes the models of the local load and the traffic flow as input, and produces the RSUs’ composite loads as an output. We provide the quantitative analysis to show how the RSU’s composite loads change in varying traffic flows in some areas of New York City with different offload patterns.
Scalable and Interoperable Distributed Architecture for IoT in Smart Cities
Vjs Pranavasri,Leo Francis,Ushasri Mogadali,Gaurav Pal,Vaddhiparthy s V S L N Surya Suhas,Anuradha Vattem,Karthik Vaidhyanathan,Deepak Gangadharan
Technical Report, arXiv, 2023
@inproceedings{bib_Scal_2023, AUTHOR = {Vjs Pranavasri, Leo Francis, Ushasri Mogadali, Gaurav Pal, Vaddhiparthy s V S L N Surya Suhas, Anuradha Vattem, Karthik Vaidhyanathan, Deepak Gangadharan}, TITLE = {Scalable and Interoperable Distributed Architecture for IoT in Smart Cities}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
The increase of IoT devices and the emergence of smart cities have revolutionized urban development, offering numerous benefits while addressing environmental concerns. This has caused an increase in the usage of IoT frameworks, and the need for efficient architecture and standardized ontology is imminent. In this regard, we propose a distributed, multi-layered data platform architecture comprising the Data Monitoring Layer (DML), Data Storage Layer (DSL), Data Enhancement Layer (DEnL), and Data Exchange Layer (DEL). Our architecture achieves interoperability, facilitates data transfer between nodes, enables telemetry data retrieval, and ensures cross-platform and cross-device compatibility. It addresses the challenges of handling increased sensor data and user demands by providing high throughput and scalability support. We investigated Smart City Living Lab at IIIT Hyderabad an existing large-scale system deployed within a 66-acre campus. This system consists of 291 nodes. By studying this deployed system, we were able to gather valuable real-world data, allowing us to analyze the challenges and potential solutions related to data architecture. Our results show improvements of up to 41.23\% in throughput and a decrease in latency by 29.19\% for data insertion from the sensor nodes. The retrieval by the data client gives an increase of over 800\% in both throughput and number of requests through DENL. These metrics are compared to a centralised data platform architecture. We conclude by discussing the implications of our findings and suggesting future work.
Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals
Avijit Kumar Ashe,Srikanth Goli,Harikumar K,Deepak Gangadharan
International conference on Unmanned Aircraft Systems, ICUAS, 2023
@inproceedings{bib_Mult_2023, AUTHOR = {Avijit Kumar Ashe, Srikanth Goli, Harikumar K, Deepak Gangadharan}, TITLE = {Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals}, BOOKTITLE = {International conference on Unmanned Aircraft Systems}. YEAR = {2023}}
Multivariate Data Analysis for Motor Failure Detection and Isolation in a Multicopter UAV using Real-Flight Attitude Signals
Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction
Goparaju Sai Usha Nagasri,Rahul Biju,Mukkiri Pravalika,M C Bhavana,Deepak Gangadharan,Bappaditya Mandal,Pradeep C
Vehicular Technology Conference, VTC, 2023
@inproceedings{bib_Opti_2023, AUTHOR = {Goparaju Sai Usha Nagasri, Rahul Biju, Mukkiri Pravalika, M C Bhavana, Deepak Gangadharan, Bappaditya Mandal, Pradeep C}, TITLE = {Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2023}}
Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However, this requires the prediction algorithm to consider several complex characteristics of traffic flow data. These complex characteristics are an amalgamation of the spatial, temporal and periodic features exhibited by traffic flow data. To extract and leverage these features for traffic flow prediction, several hybrid deep learning models have been developed recently; however, there are still some challenges to determine the optimal architecture considering both spatial and temporal features. In this work, we perform an extensive comparison of hybrid deep learning models with and without periodicity to understand the prediction accuracy of these popular models. We propose an optimization framework that unifies genetic algorithm (GA) embedded optimization with prediction models in order to derive optimized deep learning architectures for hybrid traffic flow prediction exploring 2D spatial and temporal information. The framework enables the improvement of prediction performance and eliminates the handtuning process. An improved temporal convolutional network (TCN) architecture is derived using the GA driven optimization, which achieves superior traffic flow prediction accuracy compared to all other existing hybrid deep learning models on the freeway and urban traffic data from the PeMS traffic data set. We also evaluate the performance of the derived hybrid deep learning algorithms on the Raspberry PI embedded platform. Index Terms—Traffic flow prediction, Hybrid deep learning models, Temporal convolutional networks
A Comprehensive Evaluation on the Impact of Various Spoofing Scenarios on GPS Sensors in a Low-Cost UAV
Vaddhiparthy s V S L N Surya Suhas,Garapati Sreya,Prudhvi Raj Turlapati,Deepak Gangadharan,Harikumar K
International Conference on Automation Science and Engineering, ICASE, 2023
@inproceedings{bib_A_Co_2023, AUTHOR = {Vaddhiparthy s V S L N Surya Suhas, Garapati Sreya, Prudhvi Raj Turlapati, Deepak Gangadharan, Harikumar K}, TITLE = {A Comprehensive Evaluation on the Impact of Various Spoofing Scenarios on GPS Sensors in a Low-Cost UAV}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2023}}
Unmanned Aerial Vehicles (UAVs), particularly low-cost UAVs, have become increasingly important due to their wide range of applications and ease of use. However, with the rapid growth of the UAV market, the rising security concerns pose a greater risk. One such primary concern is location spoofing attacks which can compromise UAV's navigation system, making it crucial to analyze various location-based attacks. In this paper, we identify 16 such GPS spoofing scenarios based on environmental conditions, attack type, and spoof signal propagation path. We evaluate these scenarios based on various GPS parameters like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), GPS satellite count in view, and avg signal-to-noise power density (CN0). We then analyze the variations in GPS parameters for various such attack scenarios. Further, we analyze the impact of distance on average CN0 and the effect of satellite count on effective spoofable distance. We also discuss several critical insights which are empirically observed during our experimental trials. Our experiments revealed that the natural conditions within indoor and outdoor scenarios can vary considerably, and effective spoofable distance can be up to 100 meters when the satellite count is less than 10.
Fault Detection and Isolation on a Hexacopter UAV using a Two-stage classification method
Aditya Srinivas Mulgundkar,Mayank Singh,Munjaal Tarunkumar Bhatt,Prudhvi Raj Turlapati,Deepak Gangadharan,Harikumar K
International Conference on Automation Science and Engineering, ICASE, 2023
@inproceedings{bib_Faul_2023, AUTHOR = {Aditya Srinivas Mulgundkar, Mayank Singh, Munjaal Tarunkumar Bhatt, Prudhvi Raj Turlapati, Deepak Gangadharan, Harikumar K}, TITLE = {Fault Detection and Isolation on a Hexacopter UAV using a Two-stage classification method}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2023}}
This paper presents the analysis and results of a data-driven approach for fault detection and isolation in case of complete failure of a single motor on a hexacopter. The proposed approach consists of a two-stage architecture using the Rotation Forest algorithm, which can detect faults without any false alarms and achieve a true positive classification rate of 92.6% and a false positive classification rate of 0.06%. The classification results are compared to other methods such as Logistic Regression, Gaussian Naive Bayes, AdaBoost, and Random Forest. Over 120 datasets containing approximately 21,000 data points are generated in simulation - divided into two sets for training and validation of the model. Outdoor flight tests are performed to validate the classifier algorithm further. We can detect and classify the fault within 60ms of its occurrence. A dataset is published in the open-source domain and can be used for training similar models. The work presented in this paper is data-driven (or model free) since the classifier has no knowledge of the parameters of the UAV and is derived only based on the functional relationship between input and output variables.
Time Series-based Driving Event Recognition for Two Wheelers
Goparaju Sai Usha Nagasri,L Lakshmanan,N Abhinav,B.Rahul,B Lovish,Deepak Gangadharan,Aftab M. Hussain
Design, Automation & Test in Europe Conference & Exhibition, DATE, 2023
Abs | | bib Tex
@inproceedings{bib_Time_2023, AUTHOR = {Goparaju Sai Usha Nagasri, L Lakshmanan, N Abhinav, B.Rahul, B Lovish, Deepak Gangadharan, Aftab M. Hussain}, TITLE = {Time Series-based Driving Event Recognition for Two Wheelers}, BOOKTITLE = {Design, Automation & Test in Europe Conference & Exhibition}. YEAR = {2023}}
Classification of a motorcycle's driving events can provide deep insights to detect issues related to driver safety. In order to perform the above, we developed a hardware system with 3-D accelerometer/gyroscope sensors that can be deployed on a motorcycle. The data obtained from these sensors is used to identify various driving events. We firstly investigated several machine learning (ML) models to classify driving events. However, in this process, we identified that though the overall accuracy of these traditional ML models is decent enough, the class-wise accuracy of these models is poor. Hence, we have developed time-series-based classification algorithms using LSTM and Bi-LSTM to classify various driving events. The experiments conducted have demonstrated that the proposed models have surpassed the state-of-the-art models in the context of driving event recognition with better class-wise accuracies …
Global edge bandwidth cost gradient-based heuristic for fast data delivery to connected vehicles.
Akshaj Gupta,Joseph John Cherukara,Deepak Gangadharan,BaekGyu Kim,Oleg Sokolsky,Insup Lee
Vehicular Technology Conference, VTC, 2022
@inproceedings{bib_Glob_2022, AUTHOR = {Akshaj Gupta, Joseph John Cherukara, Deepak Gangadharan, BaekGyu Kim, Oleg Sokolsky, Insup Lee}, TITLE = {Global edge bandwidth cost gradient-based heuristic for fast data delivery to connected vehicles.}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2022}}
The emergence of vehicle connectivity technologies
and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call vehicle overlaps). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in
resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.
Hierarchical Scheduling
Jin Hyun Kim,Deepak Gangadharan,Kyong Hoon Kim,Insik Shim,Insup Lee
Handbook of Real-Time Computing, HRTC, 2022
Abs | | bib Tex
@inproceedings{bib_Hier_2022, AUTHOR = {Jin Hyun Kim, Deepak Gangadharan, Kyong Hoon Kim, Insik Shim, Insup Lee}, TITLE = {Hierarchical Scheduling}, BOOKTITLE = {Handbook of Real-Time Computing}. YEAR = {2022}}
Home Handbook of Real-Time Computing Reference work entry Hierarchical Scheduling Jin Hyun Kim, Deepak Gangadharan, Kyong Hoon Kim, Insik Shin & Insup Lee Reference work entry First Online: 09 August 2022 793 Accesses Abstract This chapter presents two hierarchical scheduling analysis paradigms for a uniprocessor hierarchical scheduling system: compositional framework (CF) and real-time calculus (RTC). Each paradigm uses different techniques for resource models and schedulability analysis: CF uses supply-bound functions (sbf) and demand-bound functions (dbf), whereas RTC computes a lower-bound of service curve that satisfies the demand of a given workload. This chapter describes both CF and RTC approaches and various schedulability analysis techniques for hierarchical scheduling systems. These techniques are described based on bounded delay resource model, periodic resource model, explicit deadline periodic model, and arrival and service curves. Finally, this chapter also describes optimality results and compares CF and RTC approaches.
A Multi Layer Data Platform Architecture for Smart Cities using oneM2M and IUDX
Mante Shubham Prakash,Vaddhiparthy s V S L N Surya Suhas,Muppala Ruthwik,Deepak Gangadharan,Aftab M. Hussain,Anuradha Vattem
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_A_Mu_2022, AUTHOR = {Mante Shubham Prakash, Vaddhiparthy s V S L N Surya Suhas, Muppala Ruthwik, Deepak Gangadharan, Aftab M. Hussain, Anuradha Vattem}, TITLE = {A Multi Layer Data Platform Architecture for Smart Cities using oneM2M and IUDX}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
Smart cities play a vital role in limiting the ill-effects of rapid urbanization on the environment without compromising on benefits such as improving infrastructure, standard of living, and productivity. However, the collection, storage, and sharing of data from the plethora of sensor networks in a typical smart city deployment warrants a well-defined data platform architecture. In this paper, we propose a multi layer architecture compliant with the oneM2M standards and the Indian Urban Data Exchange (IUDX) framework. The proposed architecture consists of Data Monitoring (DML), Data Storage (DSL), and Data Exchange (DEL) layers. The DML employs oneM2M as the middleware platform to achieve interoperability. The DSL uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The DEL utilizes standard data schemas and open APIs of IUDX to avoid data silos, and enables secure data sharing. Further, we present a proof-of-concept implementation of our architecture deployed in a university campus using OM2M, PostgreSQL, and Django. Finally, simulations mimicking real-time data insertion and retrieval showed that the DML can handle 600 concurrent users with an average latency under 100 milli seconds. The DSL improved the latency compared to a single database architecture and the DEL could handle 100 concurrent users with zero failed requests.
Global Edge Bandwidth Cost Gradient-based Heuristic for Fast Data Delivery to Connected Vehicles under Vehicle Overlaps
Akshaj Gupta,Joseph John Cherukara,Deepak Gangadharan,BaekGyu Kim,Oleg Sokolsky,Insup Lee
Vehicular Technology Conference, VTC, 2022
@inproceedings{bib_Glob_2022, AUTHOR = {Akshaj Gupta, Joseph John Cherukara, Deepak Gangadharan, BaekGyu Kim, Oleg Sokolsky, Insup Lee}, TITLE = {Global Edge Bandwidth Cost Gradient-based Heuristic for Fast Data Delivery to Connected Vehicles under Vehicle Overlaps}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2022}}
The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call textit{vehicle overlaps}). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.
5D-IoT, a Semantic Web Based Framework for Assessing IoT Data Quality
Shubham Mante,Nathalie Hernandez,Aftab M. Hussain,Sachin Chaudhari,Deepak Gangadharan,Thierry Monteil
ACM Symposium on Applied Computing, SAC, 2022
@inproceedings{bib_5D-I_2022, AUTHOR = {Shubham Mante, Nathalie Hernandez, Aftab M. Hussain, Sachin Chaudhari, Deepak Gangadharan, Thierry Monteil}, TITLE = {5D-IoT, a Semantic Web Based Framework for Assessing IoT Data Quality}, BOOKTITLE = {ACM Symposium on Applied Computing}. YEAR = {2022}}
Due to the increasing number of Internet of Things (IoT) devices, a large amount of data is being generated. However, factors such as hardware malfunctions, network failures, or cyber-attacks affect data quality and result in inaccurate data generation. Therefore, to facilitate the data usage, we propose a novel 5D-IoT framework for heterogeneous IoT systems that provides uniform data quality assessment with meaningful data descriptions. Based on the quality assessment result, a data consumer can directly access data from any IoT source, which ultimately speeds up the analysis process and helps gain important insights in less time. The framework relies on semantic descriptions of sensor observations and SHACL shapes assessing the quality of such data. Evaluations carried out on real-time data show the added value of such a framework.
Design of an IoT System for Machine Learning Calibrated TDS Measurement in Smart Campus
Goparaju Sai Usha Nagasri,Vaddhiparthy s V S L N Surya Suhas,Pradeep C,Anuradha Vattem,Deepak Gangadharan
World Forum on Internet of Things, WF-IoT, 2021
@inproceedings{bib_Desi_2021, AUTHOR = {Goparaju Sai Usha Nagasri, Vaddhiparthy s V S L N Surya Suhas, Pradeep C, Anuradha Vattem, Deepak Gangadharan}, TITLE = {Design of an IoT System for Machine Learning Calibrated TDS Measurement in Smart Campus}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2021}}
This paper focuses on designing a low-cost and robust IoT-based TDS measurement system for the smart campus. The objective of this low-cost design problem is to find a solution that guarantees precise and uninterrupted output data. The dynamic reading of data, storage capacity, and calibration errors of sensors are the major challenges for IoT-based TDS measurement systems. These challenges are combated in the proposed design using a non-invasive mechanism for data collection, wireless connectivity to the data server, and machine learning calibration of sensor nodes. The TDS data of various water stations located inside the campus is used for the experimental study to develop a regression model for temperature compensation and calibration. The value of TDS sensor voltage variation against temperature is analyzed. The evaluation of the model was performed based on the R 2 and the root mean square error. By using 3 rd degree polynomial regression, we have obtained an R 2 value of 93.96 % and an RMSE of 27.93
E-PODS A Fast Heuristic for Data/Service Delivery in Vehicular Edge Computing
Akshaj Gupta,Joseph John Cherukara,Deepak Gangadharan,BaekGyu Kim,Oleg Sokolsky,Insup Lee
Vehicular Technology Conference, VTC, 2021
@inproceedings{bib_E-PO_2021, AUTHOR = {Akshaj Gupta, Joseph John Cherukara, Deepak Gangadharan, BaekGyu Kim, Oleg Sokolsky, Insup Lee}, TITLE = {E-PODS A Fast Heuristic for Data/Service Delivery in Vehicular Edge Computing}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2021}}
With the rise in state-of-the-art communication modes for vehicles such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to cloud (V2C), modern vehicles are increasingly being connected to cloud and fog/edge nodes. These vehicle connectivity modes have enabled the realization of Vehicular Edge Computing (VEC) paradigm, whereby vehicles can leverage fog/edge node resources for storage/computation. In a VEC system, vehicles receive very important and large quantity of data from edge nodes, which is termed as data delivery. In addition, edge nodes can execute some services and send the results back to the vehicle, which is called service delivery. Fast and efficient edge resource allocation for data/service delivery is important in order to serve as many vehicles as possible in the VEC system. However, edge resource allocation is complex with large number of edges and vehicles, while also considering vehicle flow parameters. In this work, we propose Edge-Pairwise Optimal Data/Service Delivery (E-PODS), which is a fast and efficient heuristic for data/service delivery. Through experiments with synthetic and real vehicular traces, we demonstrate that E-PODS is considerably faster than the optimal approach, while making resource allocations that are close to optimal in terms of total edge bandwidth cost and number of serviced vehicles.
Bandwidth Optimal Data/Service Delivery for Connected Vehicles via Edges
Deepak Gangadharan,Oleg Sokolsky,Insup Lee,BaekGyu Kim ,Chung-Wei Lin ,Shinichi Shiraishi
IEEE International Conference on Cloud Computing, CLOUD, 2018
@inproceedings{bib_Band_2018, AUTHOR = {Deepak Gangadharan, Oleg Sokolsky, Insup Lee, BaekGyu Kim , Chung-Wei Lin , Shinichi Shiraishi}, TITLE = {Bandwidth Optimal Data/Service Delivery for Connected Vehicles via Edges}, BOOKTITLE = {IEEE International Conference on Cloud Computing}. YEAR = {2018}}
he paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.
Data Freshness Over-Engineering: Formulation and Results
Dagaen Golomb,Deepak Gangadharan,Sanjian Chen,Oleg Sokolsky,Insup Lee
IEEE International Symposium on Object Oriented Real-Time Distributed Computing, ISORC, 2018
@inproceedings{bib_Data_2018, AUTHOR = {Dagaen Golomb, Deepak Gangadharan, Sanjian Chen, Oleg Sokolsky, Insup Lee}, TITLE = {Data Freshness Over-Engineering: Formulation and Results}, BOOKTITLE = {IEEE International Symposium on Object Oriented Real-Time Distributed Computing}. YEAR = {2018}}
In many application scenarios, data consumed by real-time tasks are required to meet a maximum age, or freshness, guarantee. In this paper, we consider the end-to-end freshness constraint of data that is passed along a chain of tasks in a uniprocessor setting. We do so with few assumptions regarding the scheduling algorithm used. We present a method for selecting the periods of tasks in chains of length two and three such that the end-to-end freshness requirement is satisfied, and then extend our method to arbitrary chains. We perform evaluations of both methods using parameters from an embedded benchmark suite (E3S) and several schedulers to support our
A design-time/run-time application mapping methodology for predictable execution time in MPSoCs
Andreas Weichslgartner,Stefan Wildermann,Deepak Gangadharan,Michael Glaß,Jürgen Teich
ACM Transactions on Embedded Computing Systems, TECS, 2018
@inproceedings{bib_A_de_2018, AUTHOR = {Andreas Weichslgartner, Stefan Wildermann, Deepak Gangadharan, Michael Glaß, Jürgen Teich}, TITLE = {A design-time/run-time application mapping methodology for predictable execution time in MPSoCs}, BOOKTITLE = {ACM Transactions on Embedded Computing Systems}. YEAR = {2018}}
Executing multiple applications on a single MPSoC brings the major challenge of satisfying multiple quality requirements regarding real-time, energy, and so on. Hybrid application mapping denotes the combination of design-time analysis with run-time application mapping. In this article, we present such a methodology, which comprises a design space exploration coupled with a formal performance analysis. This results in several resource reservation configurations, optimized for multiple objectives, with verified real-time guarantees for each individual application. The Pareto-optimal configurations are handed over to run-time management, which searches for a suitable mapping according to this information. To provide any real-time guarantees, the performance analysis needs to be composable and the influence of the applications on each other has to be bounded. We achieve this either by spatial or a novel temporal isolation for tasks and by exploiting composable networks-on-chip (NoCs). With the proposed temporal isolation, tasks of different applications can be mapped to the same resource, while, with spatial isolation, one computing resource can be exclusively used by only one application. The experiments reveal that the success rate in finding feasible application mappings can be increased by the proposed temporal isolation by up to 30% and energy consumption can be reduced compared to spatial isolation.
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