Abstract

Memory bandwidth contention in multi-core systems severely impacts application performance and quality-of-service (QoS) guarantees. Regulating the shared memory bandwidth mitigates the memory performance uncertainty thereby making it a manageable resource and improving trustworthiness of multi-core systems. In this work we propose a memory bandwidth regulation mechanism LMS-AR, i.e., LMS Prediction-based Adaptive Regulator within a Linux kernel module to distribute the memory bandwidth as a resource among the CPU cores. We describe a design in which both monitoring and regulation is enforced from outside by a master core - which is not a dedicated controller for regulation. This allows for plugging in computationally heavy prediction and regulation algorithms without interfering with the regulated core. An adaptive filtering technique was employed for prediction of per-core bandwidth requirement. We conducted several experiments with SPEC CPU 2017 benchmarks distributed across multiple cores. Our proposed approach demonstrated significant improvement over Memguard with respect to slowdown ratios caused due to memory contention

Abstract

State-of-the-art traffic flow forecasting models, including Graph Convolutional Networks and graph-less MLPs, require centralized GPU training across all sensors, making them impractical for resource-constrained intelligent transportation deployments. We show that much of this complexity is unnecessary. A parametric analysis of the recent graph-less model GLMST [1] reveals that reducing its internal embedding
dimension from 64 to 4 degrades MAPE by less than one percentage point, suggesting that the model's effective capacity far exceeds what the task requires. Motivated by this finding, we replace the neural architecture entirely with per-sensor Ridge
regression using horizon-aligned periodic features, combined with Recursive Least Squares (RLS) for online adaptation. With only 444 parameters per sensor (80× fewer than GLMST) and test-time online adaptation, our method achieves the best MAPE
on three of four PEMS benchmarks and remains within one percentage point on the fourth. Because each sensor's model is self-contained, and involves only elementary linear algebra, the entire pipeline (training, inference, and online adaptation) runs on edge hardware without a GPU. An ESP32 microcontroller
(160 MHz, 520 KB SRAM) completes cold-start training in 7.4 s and each predict-and-update in under 2 ms with zero heap allocation; a single Raspberry Pi 5 core completes cold start training in 0.21 s and each predict-and-update in 0.26 ms.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.