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@inproceedings{bib_Perf_2026, AUTHOR = {Sasidhar, Varada and Mogadali, Usha Sri and Gangadharan, Deepak and Chaudhari, Sachin and Vaidhyanathan, Karthik and Vattem, Anuradha }, TITLE = {Performance Evaluation of Interoperability and Control Implementation for IoT-Based Smart Spaces}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2026}}

The integration of IoT devices and the development of smart cities have brought about significant changes in urban infrastructure. Smart spaces represent a pivotal use case, exemplifying the integration of IoT sensors to enhance automation and decision-making. In these environments, interoperability is critical when incompatible devices interact, enabling seamless communication and optimized performance. To the best of our knowledge, this is the first work that presents a comparative evaluation of systems with and without interoperability, focusing on the end-to-end system performance highlighting the importance of interoperability in real-time smart space control. Towards this, we implemented a multi-layered architecture consisting of a novel Controller Layer (CL) that drives the interactions between air quality sensing and actuation of the window and air purifier. Additionally, the architecture consists of the Device Layer (DL), Data Monitoring Layer (DML), and Data Storage Layer (DSL). The DML uses oneM2M as middleware to achieve interoperability among indoor and outdoor air-quality sensors and actuators, such as a window controller and an air purifier. Our focus is on assessing the end-to-end performance of the interconnected nature of dependent actions and the significance of response time between incompatible devices. Experimental results show correlations between window controller and air-purifier states based on sensor data, offering insights into achieving interoperability in smart spaces and improving real-time air-quality management.

@inproceedings{bib_Effi_2026, AUTHOR = {Jain, Arihant and Geethika, Palla and Gangadharan, Deepak }, TITLE = {Efficient Time Series Transformer Architecture for Generalised Two-Wheeler Driving Event Recognition}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2026}}

Reliable recognition of motorcycle driving events is important for rider safety and intelligent mobility applications. This work addresses the limited generalisability of earlier studies by introducing a more diverse real-world time-series dataset collected using 3-axis accelerometer and gyroscope sensors from both male and female riders, recorded during day and night riding. The dataset captures wider variations in riding behaviour and sensor dynamics, making event recognition more realistic and challenging. To model this increased variability, we focus on a Time-Series Transformer (TST) architecture that can capture long-range temporal dependencies while remaining efficient for embedded deployment. The proposed approach is evaluated on edge hardware, where inference latency, memory usage, and model size are analysed. Experimental results show that the TST achieves strong and consistent class-wise performance while maintaining low computational overhead, making it well suited for real-time on-device two-wheeler event recognition.

@inproceedings{bib_Netw_2025, AUTHOR = {Kumar, K Pavan and Gangadharan, Deepak }, TITLE = {Network Traversal Time (NTT) Analysis of ST Flows with Non-Zero Arrival Jitter in TSN networks}, BOOKTITLE = {Verification and Evaluation of Computer and Communication Systems}. YEAR = {2025}}

Time-Sensitive Networking (TSN) is crucial for ensuring deterministic communication in real-time applications. In TSN, Network Traversal Time (NTT) is a critical metric that quantifies the timeliness of received data. Maintaining low NTT is essential for ensuring accurate system responses and to prevent outdated information from affecting real-time operations. However, the presence of non-zero arrival jitter negatively impacts NTT and poses challenges in providing predictable performance. When jitter increases, packets may arrive inconsistently, causing some to be delayed while others may arrive in bursts. To address this, we analyze the NTT bounds for time-critical flows, influenced by arrival jitter. We propose an analytical framework to estimate best and worst-case ST slots for data transmission, considering interference from higher priority flows and establishing data reachability along the network path across the switches. To validate our analysis, we conducted experiments using both a synthetic task set and an automotive use case. We also analyzed the impact of flow parameters on NTT determination, which, in some scenarios, led to pessimistic bound estimations.

@inproceedings{bib_Real_2025, AUTHOR = {Umar, Khader Zaahid and Gangineni, Swetha and Yerra, Raja Vara Prasad and Shah, Purav and Trestian, Ramona and Venkataraman, Hrishikesh and Gangadharan, Deepak }, TITLE = {Real-Time Performance Analysis of V2V and V2I-2V Architectures for Smart Communities}, BOOKTITLE = {International Conference on Vehicular Electronics and Safety}. YEAR = {2025}}

This paper presents a real-time, low-latency communication and energy- efficient framework for IoT-enabled micromobility systems, specifically targeting gated environments such as residential campuses, educational institutions and industrial zones. As a core application platform, an IoT-enabled Segway is deployed and integrated with the proposed framework, serving as a practical micro-mobility solution within gated communities. The proposed system enables both Vehicle-to-Vehicle (V2V) communication using ESP-NOW, and Vehicle-to-Infrastructureto-Vehicle (V2I2V) communication using Wi-Fi-based UDP, implemented using ESP32-S3 and NodeMCU microcontrollers integrated with IMU, ToF, Ultrasonic, and GPS sensors. To evaluate performance, end-to-end latency is measured across varying distances under both Line-of-Sight (LoS) and Non-Lineof-Sight (NLoS) conditions. Along with the latency measurement, power profiling system is also implemented using the INA226 sensor, comparing the energy consumption of ESP-NOW and 4G-based communication. To extend this analysis, Simulation of Urban MObility (SUMO) simulation was performed. Results demonstrate the effectiveness of the proposed framework in supporting reliable micro-mobility communication, offering a practical foundation for intelligent transport in localized smart environments. This work introduces a novel, energy-efficient, and infrastructure-independent V2X communication solution tailored for smart gated communities.

@inproceedings{bib_HAPT_2025, AUTHOR = {Siddharth, Tanniru Abhinav and Gangadharan, Deepak }, TITLE = {HAPTA: Adaptive Learning Based Horizontal Computation Offloading over RSUs in VEC}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2025}}

The rapid evolution of vehicular edge computing (VEC) has made task offloading to roadside units (RSUs) essential for addressing the computational demands of modern vehicular applications. However, efficiently partitioning and scheduling tasks across RSUs in dynamic environments remains challenging. This paper introduces a novel algorithm, HAPTA (Horizontal, Adaptive Learning-based, Partitioned, Timeslot-based Algorithm), a time-slot based task partitioning and offloading approach using the Upper Confidence Bound (UCB) algorithm, to adaptively allocate tasks to RSUs. We consider task offloading horizontally across RSU nodes. Unlike static or heuristic-based scheduling methods, the proposed HAPTA framework dynamically selects RSUs with optimal resource availability while considering vehicular mobility and task deadlines. The framework supports splitting tasks into subtasks, enabling efficient resource utilization and meeting stringent latency constraints. Experimental evaluations on real-world vehicular datasets demonstrate that the HAPTA approach outperforms previously introduced heuristic-based methods, achieving higher task completion rates and lower latency, particularly in high-density scenarios.

@inproceedings{bib_Towa_2025, AUTHOR = {Kyung, KyuJin and Lee, GaHyun and Chu, SeongJae and Cherukara, Joseph John and Suhas, Vaddhiparthy S V S L N Surya and Siddharth, Tanniru Abhinav and Gangadharan, Deepak and Kim, BaekGyu }, TITLE = {Towards energy-aware operational decisions for automated guided vehicles: A review}, BOOKTITLE = {The ITU Journal on Future and Evolving Technologies}. YEAR = {2025}}

Automated Guided Vehicles (AGVs) are widely used for material handling across various industries, such as warehouses, manufacturing plants, and automated container terminals. As AGVs are battery-powered, it is necessary to schedule AGV's tasks considering energy-related aspects. That is, understanding how an AGV's energy is consumed or recharged in a given operational environment is crucial to optimizing both operational efficiency and energy consumption. This article presents a comprehensive analysis of energy-aware AGV operations, emphasizing three critical areas: the impact of the operational environment, energy consumption modeling, and energy-aware decision-making for scheduling and path planning. This article examines the layout structures and energy supply strategies in two different environments: automated container terminals and workshops, to understand the environmental impact on AGV energy efficiency. A comprehensive review of energy consumption models provides insight into the physical characteristics of AGVs' energy consumption. Finally, we provide variations of task scheduling and path planning problems that explicitly take into account such energy aspects and introduce how literature tackles to solve those problems.

@inproceedings{bib_Rein_2025, AUTHOR = {Thadikamalla, Saidulu and Joshi, Piyush and Gangadharan, Deepak }, TITLE = {Reinforcement Learning for Traffic Signal Control: Advancing Efficiency through Hybrid Exploration Strategies}, BOOKTITLE = {Journal of SuperComputing}. YEAR = {2025}}

Efficient traffic signal control is critical for reducing urban congestion, yet traditional rule-based and machine learning approaches fail to adapt to dynamic conditions. Reinforcement Learning (RL) provides a data-driven alternative, and this study evaluates classical exploration strategies Epsilon-Greedy, Thompson Sampling, Upper Confidence Bound (UCB), and Softmax alongside hybrid variants including Softmax with Temperature Annealing (SMX-TA), Softmax with Entropy Regularization (SMX-ER), and their combinations with UCB and Thompson Sampling. Our framework is based on value-based RL (Double Deep Q-Networks), chosen for scalability and stability compared to on-policy methods such as PPO and A2C, and implemented with parallel simulation in SUMO and CityFlow across synthetic grids ( 1 × 1 , 4 × 4 , 6 × 6 ) and real-world datasets (Hyderabad, New York). Benchmarking against classical baselines (FixedTime, MaxPressure) and recent RL models (FRAP, CoLight, GCN) shows that the proposed SMX-ER+UCB consistently yields the lowest average travel times and robust adaptability across traffic conditions. An ablation study confirms the complementary benefits of entropy regularization and UCB, while integration into scalable controllers such as CoLight demonstrates network-level generalization. These results highlight hybrid exploration as an effective and practical strategy for real-time, city-wide traffic optimization. Such large-scale optimization requires high-performance computing (HPC) to support parallel simulations, GPU-accelerated training, and multi-agent coordination, underscoring the necessity of HPC frameworks for intelligent transportation systems.

@inproceedings{bib_Sens_2025, AUTHOR = {Bhatt, Munjaal Tarunkumar and Hitesh, Maniar Aakash and Mulgundkar, Aditya Srinivas and Kandath, Harikumar and Gangadharan, Deepak }, TITLE = {Sensor Based Active Fault Tolerant Controller for Hexacopters}, BOOKTITLE = {International Conference on Robotics and Automation}. YEAR = {2025}}

In this work, we conduct performance analysis of a Fault Tolerant Control (FTC) scheme for hexacopter actuator failure through hardware-based fault detection and dynamic control reallocation. Hall effect sensors are used to monitor motor speeds and compared with the reference signals generated by the flight controller (FC) for anomaly detection. Experimental flight tests demonstrate the efficacy of the proposed FTC in detecting the faults and subsequent flight stabilization. The performance of the proposed FTC (quantified in terms of tracking error in attitude dynamics) is compared with the ideal fault detection (no delay between fault occurrence and control reallocation) case.

@inproceedings{bib_RL-A_2025, AUTHOR = {P, Rhuthik and Gangadharan, Deepak }, 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.

@inproceedings{bib_Arch_2025, AUTHOR = {Bhatt, Hiya and Sahil, and Vaidhyanathan, Karthik and Biju, Rahul and Gangadharan, Deepak }, 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.

@inproceedings{bib_Arch_2024, AUTHOR = {Likhith, Kanigolla Naga Venkata Bala and Pal, Gaurav and Vaidhyanathan, Karthik and Gangadharan, Deepak and Vattem, Anuradha }, 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.

@inproceedings{bib_Prel_2024, AUTHOR = {Gangadharan, Deepak }, 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.

@inproceedings{bib_Onli_2024, AUTHOR = {Siddharth, Tanniru Abhinav and Reddy, Kethu Sesha Sarath and Cherukara, Joseph John and Gangadharan, Deepak }, 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.

@inproceedings{bib_Arch_2024, AUTHOR = {Kanigolla, Bala Likhith and Pal, Gaurav and Vaidhyanathan, Karthik and Gangadharan, Deepak and Vattem, Anuradha }, 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.

@inproceedings{bib_Edge_2024, AUTHOR = {Kim, BaekGyu and Gangadharan, Deepak }, 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.

@inproceedings{bib_S2P:_2024, AUTHOR = {P, Rhuthik and Suhas, Vaddhiparthy S V S L N Surya and Kannan, Pranav and Gangadharan, Deepak }, 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.

@inproceedings{bib_Atte_2024, AUTHOR = {Sharma, Ashish and Suhas, Vaddhiparthy S V S L N Surya and Nagasri, Goparaju Sai Usha and Gangadharan, Deepak and Kandath, Harikumar }, 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.

@inproceedings{bib_Chec_2024, AUTHOR = {Mallareddy, Sridhar and Kumar, K Pavan and Gangadharan, Deepak }, 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.

@inproceedings{bib_Expl_2024, AUTHOR = {Pranavasri, Vjs and Francis, Leo and Pal, Gaurav and Mogadali, Ushasri and Vattem, Anuradha and Vaidhyanathan, Karthik and Gangadharan, Deepak }, 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.

@inproceedings{bib_Leve_2024, AUTHOR = {Sehgal, Shivaan and Maniar, Aakash and K, Harikumar and Gangadharan, Deepak }, 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%).

@inproceedings{bib_Time_2023, AUTHOR = {Nagasri, Goparaju Sai Usha and Gangadharan, Deepak and Pothalaraju, Keerthi and Dullur, Shriya and JAIN, ARIHANT }, 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.

@inproceedings{bib_Dyna_2023, AUTHOR = {Cherukara, Joseph John and Suhas, Vaddhiparthy s V S L N Surya and Gangadharan, Deepak and Kim, Baek Gyu }, 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.

@inproceedings{bib_Coll_2023, AUTHOR = {Suhas, Vaddhiparthy s V S L N Surya and Cherukara, Joseph John and Gangadharan, Deepak and Kim, Baek Gyu }, 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.

@inproceedings{bib_Empi_2023, AUTHOR = {Kim, BaekGyu and Gangadharan, Deepak }, 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.

@inproceedings{bib_Scal_2023, AUTHOR = {Pranavasri, Vjs and Francis, Leo and Mogadali, Ushasri and Pal, Gaurav and Suhas, Vaddhiparthy s V S L N Surya and Vattem, Anuradha and Vaidhyanathan, Karthik and Gangadharan, Deepak }, 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.

@inproceedings{bib_Mult_2023, AUTHOR = {Ashe, Avijit Kumar and Goli, Srikanth and K, Harikumar and Gangadharan, Deepak }, 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

@inproceedings{bib_Opti_2023, AUTHOR = {Nagasri, Goparaju Sai Usha and Biju, Rahul and Pravalika, Mukkiri and Bhavana, M C and Gangadharan, Deepak and Mandal, Bappaditya and C, Pradeep }, 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

@inproceedings{bib_A_Co_2023, AUTHOR = {Suhas, Vaddhiparthy s V S L N Surya and Sreya, Garapati and Turlapati, Prudhvi Raj and Gangadharan, Deepak and K, Harikumar }, 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.

@inproceedings{bib_Faul_2023, AUTHOR = {Mulgundkar, Aditya Srinivas and Singh, Mayank and Bhatt, Munjaal Tarunkumar and Turlapati, Prudhvi Raj and Gangadharan, Deepak and K, Harikumar }, 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.

@inproceedings{bib_Time_2023, AUTHOR = {Nagasri, Goparaju Sai Usha and Lakshmanan, L and Abhinav, N and B.Rahul, and Lovish, B and Gangadharan, Deepak and Hussain, Aftab M. }, 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 …

@inproceedings{bib_Glob_2022, AUTHOR = {Gupta, Akshaj and Cherukara, Joseph John and Gangadharan, Deepak and Kim, BaekGyu and Sokolsky, Oleg and Lee, Insup }, 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.

@inproceedings{bib_Hier_2022, AUTHOR = {Kim, Jin Hyun and Gangadharan, Deepak and Kim, Kyong Hoon and Shim, Insik and Lee, Insup }, 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.

@inproceedings{bib_A_Mu_2022, AUTHOR = {Prakash, Mante Shubham and Suhas, Vaddhiparthy s V S L N Surya and Ruthwik, Muppala and Gangadharan, Deepak and Hussain, Aftab M. and Vattem, Anuradha }, 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.

@inproceedings{bib_Glob_2022, AUTHOR = {Gupta, Akshaj and Cherukara, Joseph John and Gangadharan, Deepak and Kim, BaekGyu and Sokolsky, Oleg and Lee, Insup }, 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.

@inproceedings{bib_5D-I_2022, AUTHOR = {Mante, Shubham and Hernandez, Nathalie and Hussain, Aftab M. and Chaudhari, Sachin and Gangadharan, Deepak and Monteil, Thierry }, 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.

@inproceedings{bib_Desi_2021, AUTHOR = {Nagasri, Goparaju Sai Usha and Suhas, Vaddhiparthy s V S L N Surya and C, Pradeep and Vattem, Anuradha and Gangadharan, Deepak }, 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

@inproceedings{bib_E-PO_2021, AUTHOR = {Gupta, Akshaj and Cherukara, Joseph John and Gangadharan, Deepak and Kim, BaekGyu and Sokolsky, Oleg and Lee, Insup }, 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.

@inproceedings{bib_Band_2018, AUTHOR = {Gangadharan, Deepak and Sokolsky, Oleg and Lee, Insup and , BaekGyu Kim and , Chung-Wei Lin and Shiraishi, Shinichi }, 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.

@inproceedings{bib_Data_2018, AUTHOR = {Golomb, Dagaen and Gangadharan, Deepak and Chen, Sanjian and Sokolsky, Oleg and Lee, Insup }, 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

@inproceedings{bib_A_de_2018, AUTHOR = {Weichslgartner, Andreas and Wildermann, Stefan and Gangadharan, Deepak and Glaß, Michael and Teich, Jürgen }, 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.