A Feature Fusion Framework for Improved Autism Spectrum Disorder Prediction Using sMRI and Phenotype Information
Article, Journal of Sensor and Actuator Networks, 2026, DOI Link
View abstract ⏷
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and severity, posing significant challenges for accurate diagnosis. Approaches that rely on a single data source, or unimodal data, often fail to capture the disorder’s inherent heterogeneity. A multimodal approach, which integrates diverse data types, can create a more holistic and precise understanding of ASD. This paper introduces the Multimodal ASD (MMASD) framework, a novel predictive model for ASD. The MMASD framework is built upon two distinct input modalities: structural magnetic resonance imaging (sMRI) and corresponding phenotype data. The sMRI data provides detailed neuroanatomical metrics, including brain tissue segmentation, volumetric measurements, and cortical thickness. Complementing this, the phenotype data encompasses the clinical and behavioral characteristics of each individual. In the proposed framework, latent features are independently extracted from both modalities and then fused to generate a comprehensive representation of the multimodal information. These fused features are then used to predict ASD by leveraging the outputs of various classifiers. A majority voting ensemble is employed to determine the final prediction. The MMASD framework achieves a high accuracy of 97.27%, surpassing the performance of current state-of-the-art approaches and demonstrating the efficacy of integrating neuroimaging and clinical data for ASD prediction.
Optimal IoT application placement using quantum inspired evolutionary algorithm in fog computing environment
Article, Discover Computing, 2026, DOI Link
View abstract ⏷
The rapid growth of the Internet of Things (IoT) has led to the generation of massive amounts of data from diverse applications such as smart cities, healthcare, and industrial automation. While fog computing has emerged as a promising paradigm to overcome the latency and bandwidth limitations of traditional cloud-centric models by enabling edge-level processing. Efficient placement of application modules in fog environments remains a critical challenge. This is primarily due to the heterogeneous nature of fog nodes, resource constraints, and the dynamic behavior of applications. Existing solutions often fall short in achieving optimal trade-offs between performance metrics such as latency, energy efficiency, and bandwidth utilization–particularly under real-time constraints. Further, most of these solutions fails to balance between explorations and exploitation. To address this gap, this study introduces a Quantum-Inspired Evolutionary Algorithm (QIEA) designed to optimize application module placement in fog computing. Unlike conventional heuristics, QIEA effectively tackles the NP-hard nature of the placement problem by leveraging quantum-inspired principles to enhance exploration and exploitation in the solution space. The proposed algorithm aims to minimize latency, energy consumption, and execution time, while ensuring the Quality of Service (QoS) requirements of IoT applications. The effectiveness of the method is demonstrated through a real-world use case called smart car parking system. Results show that, on an average, the proposed approach reduces the energy consumption by 9.5%, network usage by 17.2% and execution time by 54%.
ProTSF: IoT-Based Outdoor Air Pollution Forecasting Using Bayesian Optimization-Based LSTM
Conference paper, Lecture Notes in Networks and Systems, 2026, DOI Link
View abstract ⏷
By integrating IoT devices with outdoor air quality monitoring, smart cities can proactively respond to pollution hotspots, optimize resource allocation, and implement targeted interventions to mitigate the impact of poor air quality on citizens and the environment. In this context, deep learning can empower accurate outdoor air quality forecasting by analyzing complex data patterns, enabling proactive measures for pollution mitigation and citizen well-being in smart cities. To achieve the same, we present a unique air pollution forecasting model ProTSF, which is Bayesian optimization-based deep learning model Long Short-Term Memory (LSTM), to ensure high fidelity for any dataset in this article. The assessment findings show this model’s usefulness, which points to future uses in more smart city forecasting scenarios. We attained a remarkable average accuracy of 95.83 percent through daily forecasts, with an average Root Mean Squared Error (RMSE) value of 0.089 parts per million (ppm), which is the highest as compared to other well-established time series forecasting models (e.g., AR, MA, ARIMA, SARIMA) achieved till date.
LUMI: An On-Device LLM Framework for Secure and Cloud-Free Smart Home Automation
Bai H.L., Bhushan C.M., Shaik F., Gazi F., Hussain M.
Conference paper, Proceedings - International Conference on Computational Intelligence and Networks, 2026, DOI Link
View abstract ⏷
Currently, existing voice-assisted smart homes are based on cloud-based computation, which gives rise to several concerns regarding data privacy, latency, and dependency on the network. This paper proposes a novel, secure, and efficient smart home system, namely, Localized User-initiated Machine Intelligence (LUMI), which ensures data privacy, ultra-low-latency voice interaction, and real-time device automation through local speech processing, semantic reasoning via Large Language Model (LLM), and IoT device management through a completely local architecture. Unlike existing cloud-based smart homes, in the proposed system, all computation occurs locally, ensuring data privacy, ultra-low-latency voice interaction, and real-time device automation. By employing local computation of the Mistral LLM, the proposed system can efficiently translate voice commands into device management messages through the MQTT protocol. Experimental results validate the proposed system as a novel, secure, and efficient solution, ensuring 96.7 % accuracy and 412 ms latency.
Multi-Controller TinyML Architecture for Object Detection with Efficient Communication and Applications
Bhushan C.M., Koppuravuri P., Nomitha Prasanthi B., Gazi F., Hussain M.M.
Conference paper, Proceedings - International Conference on Computational Intelligence and Networks, 2026, DOI Link
View abstract ⏷
Real-time object detection in resource-constrained environments poses significant challenges due to the limited computational power of controller units (CUs). Traditional single-controller approaches struggle to process multiple object attributes simultaneously, leading to increased inference latency and reduced efficiency. To address this, we propose a multi-controller TinyML-based architecture where two CUs operate collaboratively each identifying different parameters. Upon detection, one CU transmits its result to the second CU through UART protocol, consolidating both parameters and displaying the final classification output. The final results are then transmitted via TCP or UDP, depending on network traffic conditions, to ensure efficient data exchange. This paper also explores various real-world applications where multiple CUs collaboratively enhance object detection efficiency in resource-limited environments.
Toward Efficient Deployment of Compressed Neural Networks on MCU for EdgeAI Applications
Conference paper, Lecture Notes in Networks and Systems, 2026, DOI Link
View abstract ⏷
Deploying neural network models onto low-power miniaturized computers such as Micro-Controller Unit (MCU) is becoming increasingly important for edge intelligence (EdgeAI) applications due to its potential to reduce the reliance on cloud connectivity and enhance real-time processing capabilities. However, this process often requires compressing the neural network models to fit the limited memory and computational resources of MCUs while maintaining acceptable performance. In this paper, we explore various techniques for compressing neural network models, including quantization, pruning, and model optimization frameworks like TensorFlow Lite and CMSIS-NN. Additionally, it discusses the challenges associated with deploying compressed models onto MCUs, such as limited memory, computational constraints, and the need for hardware acceleration techniques. By addressing these challenges and leveraging efficient deployment strategies, deploying neural network models onto MCUs can significantly improve edge computing performance, reduce power consumption, and enhance scalability for a wide range of applications. We also consider real-time implantation of sensor nodes to capture images.
TinyML-Based AQI Forecasting Using CNN with Butterworth Low-Pass Filter for Edge Devices
Conference paper, Proceedings - International Conference on Computational Intelligence and Networks, 2026, DOI Link
View abstract ⏷
Air pollution, particularly PM2.5, is a major concern for public health in urban areas. In this paper, we propose a TinyML pipeline for PM2.5 forecasting that integrates a lightweight 1D-CNN with zero-phase Butterworth low-pass denoising to suppress sensor noise without phase distortion. Using hourly CPCB data from Perungudi (Chennai, 2024), the denoised CNN achieves strong accuracy on the normalized scale (MSE 0.000061, MAE 0.00389, RMSE 0.0078, R2=0.9908). The model is converted to TensorFlow Lite and deployed on an ARM Cortex-M4 with a 42 KB footprint, 11.4 ms inference latency, and 4,344 B RAM; measured power is 2,501 μ W. Relative to an unfiltered CNN and Multilayer Perceptron (MLP) / Feed-Forward Neural Network (FFNN) baselines, the denoising front end yields substantial error reductions while also lowering latency and memory. These results indicate that principled signal preprocessing paired with compact CNNs enables accurate, low-power, on-device P M2.5 monitoring suitable for smart-city deployments.
Leveraging CNN and Transfer Learning With EfficientNet for Enhanced Optical Coherence Tomography (OCT)
Arekanti S., Bhushan C.M., Alam I., Koppuravuri P., Shaik F., Hussain M.M., Gazi F.
Article, International Journal of Biomedical Imaging, 2026, DOI Link
View abstract ⏷
Optical coherence tomography (OCT) is an important imaging modality for detecting retinal disorders, including diabetic macular edema (DME), choroidal neovascularization (CNV), and drusen, as well as distinguishing healthy retinal patterns. Nevertheless, visual assessment of OCT scans is labor-intensive, observer-dependent, and dependent on expert clinical knowledge, encouraging reliable automated screening tools. This work introduces a hybrid Deep Transferred EfficientNet and Support Vector Machine (DTE-SVM) approach for multiclass OCT image classification. In contrast to standard CNN models that commonly use a softmax layer for final prediction, the proposed framework employs transfer-learned EfficientNet representations with an SVM decision model to strengthen interclass discrimination and robustness within the extracted feature space. Experiments used a working subset of 8767 OCT images selected from the OCT2017 dataset, representing nearly 10.4% of the complete 84,495-image collection. This smaller yet representative subset was adopted to lower computational cost while maintaining class distribution among CNV, DME, drusen, and normal groups. Five EfficientNet architectures, from B0 through B4, were assessed using stratified 10-fold cross-validation. Among them, DTE-SVM-B0 produced the strongest overall results, obtaining an accuracy of 0.9076, precision of 0.9074, recall of 0.9076, and F1-score of 0.9073. DTE-SVM-B2 showed the smallest fold-level standard deviation, reflecting comparatively stable behavior. Comparative experiments indicated that DTE-SVM-B0 surpassed ResNet50, DenseNet-121, and EfficientNetB0 softmax-based classifiers, whereas ablation testing verified that substituting the softmax layer with SVM improved results for every EfficientNet backbone. Class-level clinical evaluation also revealed strong sensitivity and specificity, with CNV identified most accurately and drusen remaining the hardest category. Overall, these results suggest that compact EfficientNet feature extractors integrated with SVM offer accurate, stable, and computationally practical OCT-driven retinal disease classification for clinical decision-support settings.
Mobility aware microservice placement in vehicular edge computing
Surayya A., Hussain M.M., Khan A.U., Jamel L., Alzahrani A.
Article, Journal of Cloud Computing, 2026, DOI Link
View abstract ⏷
Vehicular Edge Computing (VEC) is a decentralized paradigm for processing and managing the vast amount of data generated by connected vehicles and roadside IoT devices. Vehicular Edge Nodes (VENs), such as roadside units (RSUs), onboard vehicular gateways, or micro-data centers deployed near roadways, serve as edge nodes that host and execute vehicular services and applications. Microservices offer promising deployment strategies for future VEC applications such as autonomous driving, vehicular platoons, infotainment, etc. However, a critical challenge in VEC lies in selecting appropriate edge nodes for deploying these microservices, given that the edge nodes are spatially distributed across the vehicular environment and possess constrained computational and storage capacities. These microservices are dynamically deployed on the VENs in order to minimize service latency and optimize resource consumption. However, the high mobility of vehicles and the limited computational capacity of VENs pose significant challenges in maintaining efficient service delivery. To address these issues,we propose a comprehensive microservice placement framework that caters to the mobility of vehicles and facilitates service migration across VENs. Specifically, we formulate the placement decisions as a multi-objective optimization problem, considering latency constraints,VEN resource limitations, vehicular mobility, and service migration among VENs. Our model employs a KDTree-based nearest neighbor strategy for modelling the spatio-temporal dynamics of VENs. The model is solved using standard evolutionary algorithms such as NSGA-II, ACO, PSO, and DE. The framework supports adaptive migration and offloading to ensure seamless service continuity. We evaluate the proposed model using real-world vehicular mobility traces from Luxembourg City. The results show that the best-performing optimizer depends on the target objective: DE achieves the lowest service latency, PSO minimizes resource consumption, and NSGA-II reduces migration and offloading while maximizing edge execution, highlighting the importance of multi-objective placement strategies in dynamic vehicular edge environments.
A Deep Reinforcement Learning Approach for Dynamic Microservice Placement in Vehicular Edge Computing
Conference paper, International Conference on Communication Systems and Networks, COMSNETS, 2026, DOI Link
View abstract ⏷
Vehicular Edge Computing (VEC) offers a promising paradigm for low-latency and high-bandwidth services by extending cloud capabilities to the network edge. A critical challenge in VEC is the dynamic placement of microservices on Edge Servers (ESs) due to the high mobility of vehicles. This paper proposes a Deep reinforcement learning (DRL) approach, specifically using Proximal Policy Optimization (PPO), to address the microservice placement problem in a VEC environment. Our objective is to minimize service latency and resource consumption while adapting to dynamic traffic conditions. Real-world vehicle traces from Luxembourg City are obtained via the SUMO simulator to create a realistic environment for training and evaluation. Experimental results demonstrate that the proposed PPO-based framework achieves superior latency resource trade-offs, adapts effectively to mobility variations, and outperforms traditional heuristic and DRL baselines.
Evolutionary Algorithms for Edge Server Placement in Vehicular Edge Computing
Article, IEEE Access, 2025, DOI Link
View abstract ⏷
Vehicular Edge Computing (VEC) is a critical enabler for intelligent transportation systems (ITS). It provides low-latency and energy-efficient services by offloading computation to the network edge. Effective edge server placement is essential for optimizing system performance, particularly in dynamic vehicular environments characterized by mobility and variability. The Edge Server Placement Problem (ESPP) addresses the challenge of minimizing latency and energy consumption while ensuring scalability and adaptability in real-world scenarios. This paper proposes a framework to solve the ESPP using real-world vehicular mobility traces to simulate realistic conditions. To achieve optimal server placement, we evaluate the effectiveness of several advanced evolutionary algorithms. These include the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Teaching-Learning-Based Optimization (TLBO). Each algorithm is analyzed for its ability to optimize multiple objectives under varying network conditions. Our results show that ACO performs the best, producing well-distributed pareto-optimal solutions and balancing trade-offs effectively. GA and PSO exhibit faster convergence and better energy efficiency, making them suitable for scenarios requiring rapid decisions. The proposed framework is validated through extensive simulations and compared with state-of-the-art methods. It consistently outperforms them in reducing latency and energy consumption. This study provides actionable insights into algorithm selection and deployment strategies for VEC, addressing mobility, scalability, and resource optimization challenges. The findings contribute to the development of robust, scalable VEC infrastructures, enabling the efficient implementation of next-generation ITS applications.
OUL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques
Article, IEEE Journal on Indoor and Seamless Positioning and Navigation, 2025, DOI Link
View abstract ⏷
Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data.
Integrated Underwater Data Transmission and Object Detection System Using TinyML and Multi-Hop Networks
Bhushan C.M., Laskar S.H., Garikapati J.S., Sai Srihitha P., Hemanth Durga Kumar S., Gazi F., Hussain M.M.
Conference paper, 2025 21st International Conference on Intelligent Environments, IE 2025, 2025, DOI Link
View abstract ⏷
Underwater communication faces challenges like high attenuation, limited bandwidth, and energy constraints. This paper presents an underwater communication system using ultrasonic sensors for image transmission and TinyML for efficient object detection. The architecture comprises a Raspberry Pi-based transmitter node, intermediate repeater nodes and a receiver node. Images are processed using Discrete Cosine Transform (DCT), transmitted as text files, reconstructed via Inverse DCT (IDCT), and analyzed using a lightweight MobileNetV2 model for real-time object detection. The integration of TinyML enables energy-efficient on-device inference, addressing resource constraints in edge devices. The system demonstrates effective data transmission and accurate detection, with applications in underwater surveillance, marine monitoring, and aquaculture. This work underscores TinyML's role in advancing AI-driven Internet of Underwater Things (IoUT) technologies.
Leveraging Edge Resources for Indoor Localization for Improved Accuracy
Conference paper, 2025 21st International Conference on Intelligent Environments, IE 2025, 2025, DOI Link
View abstract ⏷
This paper presents a novel approach to zone prediction through Wi-Fi fingerprinting combined with machine learning, leveraging data collected by a UAV and a robotic car across distinct zones. Among the evaluated models, the Confidence-Aware Framework (ConFi) outperformed state-of-the-art methods, achieving a test accuracy of 91%, compared to Long Short-Term Memory (LSTM) at 73% and Long Range Wide Area Network (LoRaWAN) at 85%. This superior accuracy underscores the ConFi model's capability to effectively manage the complexities of real-world environments. By leveraging the ConFi framework, the proposed system enhances precision, scalability, and adaptability over traditional methods. This study represents a significant advancement in indoor localization, offering a deployable and efficient solution for GPS-denied environments with promising applications in healthcare, logistics, and disaster management.
Resource management in fog computing: Overview and mathematical foundation
Book chapter, Swarm Intelligence: Theory and Applications in Fog Computing, Beyond 5G Networks, and Information Security, 2025,
Deploying TinyML for energy-efficient object detection and communication in low-power edge AI systems
Bhushan C.M., Koppuravuri P., Prasanthi N., Gazi F., Hussain M.M., Abdussami M., Devi A.A., Faizi J.
Article, Scientific Reports, 2025, DOI Link
View abstract ⏷
Edge Artificial Intelligence (Edge AI) is driving the widespread deployment of neural network models on resource-constrained microcontroller units (MCUs), enabling real-time, on-device data processing. This approach significantly reduces cloud dependency, making it ideal for applications in industrial automation and IoT. However, the deployment of deep learning models on such constrained devices poses significant challenges due to limitations in memory, computational power, and energy capacity. This paper presents a real-time object detection system optimized for energy efficiency and scalability, which integrates well-established model compression techniques, such as quantization, with a low-cost MCU-based platform. The system leverages MobileNetV2, a lightweight neural network, quantized to achieve the best trade-offs between accuracy and resource consumption. The proposed solution integrates a camera and Wi-Fi module for capturing and transmitting image data, utilizing dual-mode TCP/UDP communication to balance reliability and low-latency transmission for IoT applications. We present a comprehensive system-level analysis, exploring the trade-offs between latency, memory, energy consumption, and model size. The Visual Wake Words (VWW) dataset is used for this research, which demonstrates the practical performance and scalability of the system for real-time applications in smart devices, industrial monitoring, and environmental sensing. This work emphasizes the integration of TinyML models with constrained hardware and offers a foundation for scalable, autonomous, energy-efficient Edge AI solutions. Quantitatively, 8-bit post-training quantization achieved 3– storage reduction, yielding deployable flash footprints of 286-536 KB within a 1 MB flash / 256 KB SRAM budget, on-device inference latency ranged from 3.47 to 14.98 ms per frame with energy per inference of 10.6–22.1 J, while quantized MobileNet variants maintained accuracy. In wireless reporting, UDP reduced one-way latency relative to TCP, whereas TCP provided higher delivery reliability, underscoring application-dependent protocol trade-offs for real-time embedded deployments.
Optimal deployment of multiple IoT applications on the fog computing: A metaheuristic-based approach
Macha S.S.R.K., Chinta P.K., Katakam P., Hussain M., Georgievski I., Reddy V.D.
Book chapter, Swarm Intelligence: Theory and Applications in Fog Computing, Beyond 5G Networks, and Information Security, 2025,
Towards Efficient IoUT Vision: Simulated Compressed Image Transmission and Detection
Conference paper, 2025 IEEE Future Networks World Forum: Beyond Connectivity: 6g for a Sustainable and Intelligent Future, FNWF 2025, 2025, DOI Link
View abstract ⏷
Underwater communication is constrained by limited bandwidth, high latency, and signal attenuation, particularly in acoustic transmission scenarios. To address these challenges, this paper investigates a simulation-based framework for underwater image transmission using discrete cosine transform (DCT) and quantization-based compression. Grayscale images are compressed at varying quantization levels (3-bit to 8-bit) and transmitted over a simulated underwater acoustic channel with relay-based attenuation and noise. The impact of compression on image fidelity is evaluated using Peak Signal-to-Noise Ratio and Structural Similarity Index (PSNR, SSIM), while object detection robustness is assessed via classification accuracy using a MobileNetV2 model. Experimental results demonstrate a clear trade-off between compression efficiency and detection accuracy, offering insights into optimal quantization strategies for bandwidth-limited underwater environments. This simulation framework, developed entirely in MATLAB, provides a scalable testbed for evaluating vision-centric Internet of Underwater Things (IoUT) applications prior to real-world deployment on resource-constrained devices.
Dependency-Aware Microservice Placement in Dynamic Vehicular Edge Computing
Conference paper, 2025 IEEE Future Networks World Forum: Beyond Connectivity: 6g for a Sustainable and Intelligent Future, FNWF 2025, 2025, DOI Link
View abstract ⏷
Vehicular Edge Computing (VEC) is a promising paradigm to support latency-sensitive and computation-intensive vehicular applications such as object detection, trajectory prediction, and hazard alerts. Microservice architectures enable scalability and modularity, but placing dependent microservices across heterogeneous edge service providers (ESPs) remains a critical challenge. Existing placement strategies often treat services independently, ignoring inter-service dependencies, which leads to increased inter-node communication delays and degraded performance. In this paper, we propose a dependency-aware microservice placement framework for VEC using deep reinforcement learning. Our approach leverages a Proximal Policy Optimization (PPO) based actor-critic agent to learn placement decisions that jointly minimize end-to-end service latency and maximize resource utilization while respecting capacity and SLA constraints. Experiments on real vehicular mobility traces from Luxembourg City demonstrate that the proposed method significantly reduces service latency and SLA violations compared to random and greedy baselines, while efficiently utilizing edge resources.
A Blockchain-based DDoS Attack Mitigation Framework for Mission-critical IIoT Environments
Conference paper, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2025, DOI Link
View abstract ⏷
The Mission-critical IIoT (MC-IIoT) networks are highly interconnected and distributed to ensure real-time monitoring and control, where security, reliability, and availability are paramount. These features make MC-IIoT networks vulnerable to Distributed Denial of Service (DDoS) attacks that must be mitigated while combating single-point of failure of attack detection systems, limited scalability of industrial control systems, high latency and low reliability. In this paper, we propose a Blockchain-based DDoS attack mitigation framework for MC-IIoT networks which persuades these requirements. Initially, a machine learning-based cascaded model consisting of an autoencoder-enhanced decision tree model and a lightweight isolation forest algorithm performs attack detection and trust score evaluation of IIoT devices. A consortium Blockchain network of IIoT gateways on which this cascaded model is installed performs attack mitigation using a chaincode based on previously evaluated trust scores. The proposed model is evaluated on Hyperledger Fabric and the performance is analyzed with Hyperledger caliper benchmarking. The comparative analysis of the proposed framework with state-of-the-art proves its effectiveness in terms of attack detection accuracy (99.93%), CPU utilization (30%), decreased latency (12.5 ms), increased throughput (21%), chaincode execution time (3 ms) and attack detection rate (99.6%).
A UAV-Assisted Architecture for Fault-Tolerant LoRa-Based V2I Communication
Siripalli H.D.K., Shaik F., Devi M.N.N., Bhushan C.M., Gazi F., Hussain M.M.
Conference paper, 2025 IEEE Future Networks World Forum: Beyond Connectivity: 6g for a Sustainable and Intelligent Future, FNWF 2025, 2025, DOI Link
View abstract ⏷
Reliable data exchange between vehicles and road-side units (RSUs) is critical for intelligent transportation systems, yet communication is often disrupted by low signal strength, intermittent connectivity, and missing acknowledgments. This paper proposes a deterministic, UAV-assisted architecture for fault-tolerant V2I communication using LoRa and IEEE 802.11 backhaul links. In the proposed system, UAVs are triggered when RSUs detect degraded RSSI or ACK loss, thereby providing a redundant relay path to maintain session continuity. All logic is implemented on off-the-shelf embedded hardware without reliance on cloud or simulation platforms. Experimental results from real hardware deployments demonstrate a 14% improvement in message delivery ratio, steady latency of approximately 34 ms, and enhanced SNR performance in low-coverage areas. The architecture is lightweight, interpretable, and immediately deployable for real-world ITS applications.
ConFi: A Confidence-Aware Framework for Ultra-Precise Indoor Localization in 6G Networks Using UAV-Assisted Wi-Fi Fingerprints
Devi M.N.N., Bhushan Ch.M., Kumar S.H.D., Gazi F., Hussain M.M., Devi A.A.
Conference paper, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2025, DOI Link
View abstract ⏷
Indoor localization is essential for various modern applications, particularly in environments where traditional GPS-based positioning is unreliable. This paper introduces the ConFi framework, a confidence-aware system that combines UAV-assisted Wi-Fi fingerprinting with machine learning models to achieve ultra-precise indoor localization. By leveraging the high-speed, low-latency capabilities of 6G networks, ConFi ensures real-time, scalable, and adaptive positioning even in highly dynamic environments. The system utilizes UAVs equipped with Wi-Fi modules to dynamically collect accurate signal data, which is processed through a convolutional neural network (CNN) optimized for local environmental variations. Reinforcement learning is integrated into the system to enable continuous adaptation, enhancing the robustness of the framework. Experimental results show that ConFi achieves a test accuracy of 92%, outperforming traditional models like LSTM and LoRaWAN. The ConFi framework is suitable for real-world applications in complex indoor environments such as hospitals, airports, and multi-storey buildings, where precise and reliable indoor localization is required.
Transparent Intrusion Detection Using ML: Integrating Explainable Models for Cybersecurity
Abdussami M., Sri Pranav N., Ramani M.S.R., Singothu K.M., Gurram S.K., Aguru A.D., Hussain M.M., Gazi F.
Conference paper, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2025, DOI Link
View abstract ⏷
In today's world of connected systems, protecting those systems is imperative. Intrusion Detection Systems (IDS) have an important function to perform to protect these systems by detecting and monitoring malicious activity and policy breaches. This project develops an intelligent IDS based on Artificial Intelligence (AI) and Machine Learning (ML) to identify potential security threats online. The system applies sound ML methods to examine the traffic on a system and separate benign and malicious behavior. A variety of ML methods / Algorithms exist to differentiate bad and good data, and among those methods are supervised and unsupervised learning models to enhance known and unknown threat detection. Benchmark data sets are used to train the system so that it can detect a variety of attacks, including distributed denial of service (DDoS), probing, and remote-to-local (R2L) attacks efficiently. There is also use of feature selection and dimensionality reduction methods to achieve maximum computational performance and efficiency. The suggested AI and ML-based IDS provides scalable and flexible security through continuous learning to respond to emerging threats. It is a potent choice for securing networks of today because it supports scalability and real-time analytics.
A Novel Authentication Scheme for Smart Homes
Alam I., Ashraf Z., Hussain M.M., Rahman A., Khan J., Siddiqui M.A.
Conference paper, International Conference on Computer and Applications, ICCA 2025 - Proceedings, 2025, DOI Link
View abstract ⏷
Today's life cannot be imagined without smart homes, which are designed to provide both convenience and automation. Unfortunately, the security gaps that exist in the infrastructure frameworks of smart homes can lead to significant loss of property and even life. These settings depend on devices on the Internet of Things (IoT), which have some of the most serious security issues, especially with respect to device authentication and key management. Although traditional cryptographic methods could work, they are often too slow and overly complicated for basic IoT gadgets. This article proposes a lightweight authentication scheme based on Shamir's Secret Sharing (SSS), cryptographic hash functions (SHA-3), and an XOR based session key generation. The proposed system is capable of securely distributing the secrets of authentication and provides pseudonyms to the reconstructed secrets through Lagrange interpolation. The system exploits hashing and XOR to deliver fast authentication while being not vulnerable to MITM, replay, or key compromise attacks. The security evaluation proves the effectiveness and dependability of the protocol and its applicability in IoT settings with limited resources.
EfficientNet B0 Model Architecture for Brain Tumor Detection and Classification Using CNN
Book chapter, Advancements in Artificial Intelligence and Machine Learning, 2025, DOI Link
View abstract ⏷
Brain tumors are a life-threatening disease, and a lot of people are losing their lives. These brain tumors are abnormal cells that develop in and around the brain. This research explores the cutting edge of medical imaging processing, focusing on enhancing the detection and categorization of brain tumors. EfficientNetB0 is the most advanced deep learning architecture that has been thoroughly compared with other deep learning models in order to improve brain tumor classification accuracy using the Kaggle MRI image dataset with 7023 images. The drawbacks of manual tumor identification techniques are discussed, and precise classification using deep neural networks is proposed, with special attention to the transition from binary to multiclassification. This chapter's primary focus is on improving and optimizing the EfficientNetB0 model through the addition of trainable layers on top of its basic architecture. Several techniques are used like global average pooling for spatial and dimensionality reduction with reduced parameters, dropout to drop layers, and dense net with softmax for multiclass classification. Concurrently, strategic layer freezing is used to refine the deep learning models for foundation design. The results show that the finetuned EfficientNetB0 model with hyper-parameter optimization guarantees exceptional brain tumor accuracy. EfficientNetB0 has achieved a good accuracy of 99.7% and a precision of 99.5% compared to Resnet50, VGG16, InceptionV3 and Xception. This work presents a unique deep-learning method in accordance with a transfer learning strategy for assessing brain cancer categorization accuracy using the enhanced ResNet50 model. As we advance the state-of-the-art, this chapter offers researchers, medical professionals, and patients a solid foundation for accurate and timely brain tumor diagnoses, thus contributing to the research community.
Swarm intelligence: Theory and applications in fog computing, beyond 5G networks, and information security
Book, Swarm Intelligence: Theory and Applications in Fog Computing, Beyond 5G Networks, and Information Security, 2025, DOI Link
View abstract ⏷
This book offers a comprehensive overview of the theory and practical applications of swarm intelligence in fog computing, beyond 5G networks, and information security. The introduction section provides a background on swarm intelligence and its applications in real-world scenarios. The subsequent chapters focus on the practical applications of swarm intelligence in fog-edge computing, beyond 5G networks, and information security. The book explores various techniques such as computation offloading, task scheduling, resource allocation, spectrum management, radio resource management, wireless caching, joint resource optimization, energy management, path planning, UAV placement, and intelligent routing. Additionally, the book discusses the applications of swarm intelligence in optimizing parameters for information transmission, data encryption, and secure transmission in edge networks, multi-cloud systems, and 6G networks. The book is suitable for researchers, academics, and professionals interested in swarm intelligence and its applications in fog computing, beyond 5G networks, and information security. The book concludes by summarizing the key takeaways from each chapter and highlighting future research directions in these areas.
Facility Location in 6G-aware Vehicular Edge Computing
Conference paper, International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2024, DOI Link
View abstract ⏷
This paper tackles the facility location problem in 6G -enabled Vehicular Edge Computing (VEC) systems, focusing on the optimal placement of Roadside Units (RSUs) and Unmanned Aerial Vehicles (UAVs). The goal is to minimize Quality of Service (QoS) degradation by addressing challenges like dynamic vehicle mobility, traffic variations, and real-time task offloading. A mathematical optimization model is proposed, considering latency, energy consumption, packet loss, and handover costs. To solve this complex problem, heuristic algorithms such as Hill Climbing, Tabu Search, Simulated Annealing, and A∗ search are introduced. Extensive simulations evaluate their performance on energy efficiency and cumulative latency across various traffic and network conditions. The results reveal the strengths of each algorithm, offering valuable insights for their application in VEC scenarios. These findings contribute to scalable, energy-efficient solutions for 6G-aware VEC networks, particularly in dynamic vehicular environments, advancing research in edge computing and network optimization.
Music Generation Using Deep Learning
Vemula D.R., Tripathi S.K., Sharma N.K., Hussain M.M., Swamy U.R., Polavarapu B.L.
Conference paper, Lecture Notes in Electrical Engineering, 2023, DOI Link
View abstract ⏷
In this paper, we explore the usage of char-RNN which is special type of recurrent neural network (RNN) in generating music pieces and propose an approach to do so. First, we train a model using existing music data. The generating model mimics the music patterns in such a way that we humans enjoy. The generated model does not replicate the training data but understands and creates patterns to generate new music. We generate honest quality music which should be good and melodious to hear. By tuning, the generated music can be beneficial for composers, film makers, artists in their tasks, and it can also be sold by companies or individuals. In our paper, we focus more on char ABC-notation because it is reliable to represent music using just sequence of characters. We use bidirectional long short-term memory (LSTM) which takes input as music sequences and observer that the proposed model has more accuracy compared with other models.
Enhanced resource provisioning and migrating virtual machines in heterogeneous cloud data center
Article, Journal of Ambient Intelligence and Humanized Computing, 2023, DOI Link
View abstract ⏷
Data centers have become an indispensable part of modern computing infrastructures. It becomes necessary to manage cloud resources efficiently to reduce those ever-increasing power demands of data centers. Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to map workloads onto servers in a way that requires the least resources possible. It is an efficient way to improve resources utilization and reduce energy consumption in cloud data centers. Virtual machine (VM) consolidation involves host overload/underload detection, VM selection, and VM placement. If a server becomes overloaded, we need techniques to select the proper virtual machines to migrate. By considering the migration overhead and service level of agreement (SLA) violation, we investigate design methodologies to reduce the energy consumption for the whole data center. We propose a novel approach that optimally detects when a host is overloaded using known CPU utilization and a given state configuration. We design a VM selection policy, considering various resource utilization factors to select the VMs. In addition, we propose an improved version of the JAYA approach for VM placement that minimizes the energy consumption by optimally pacing the migrated VMs in a data center. We analyze the performance in terms of energy consumption, performance degradation, and migrations. Using CloudSim, we run simulations and observed that our approach has an average improvement of 24% compared to state-of-the-art approaches in terms of power consumption.
Post-quantum distributed ledger technology: a systematic survey
Parida N.K., Jatoth C., Reddy V.D., Hussain M.M., Faizi J.
Article, Scientific Reports, 2023, DOI Link
View abstract ⏷
Blockchain technology finds widespread application across various fields due to its key features such as immutability, reduced costs, decentralization, and transparency. The security of blockchain relies on elements like hashing, digital signatures, and cryptography. However, the emergence of quantum computers and supporting algorithms poses a threat to blockchain security. These quantum algorithms pose a significant threat to both public-key cryptography and hash functions, compelling the redesign of blockchain architectures. This paper investigates the status quo of the post-quantum, quantum-safe, or quantum-resistant cryptosystems within the framework of blockchain. This study starts with a fundamental overview of both blockchain and quantum computing, examining their reciprocal influence and evolution. Subsequently, a comprehensive literature review is conducted focusing on Post-Quantum Distributed Ledger Technology (PQDLT). This research emphasizes the practical implementation of these protocols and algorithms providing extensive comparisons of characteristics and performance. This work will help to foster further research at the intersection of post-quantum cryptography and blockchain systems and give prospective directions for future PQDLT researchers and developers.
SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks
Hussain M.M., Azar A.T., Ahmed R., Umar Amin S., Qureshi B., Dinesh Reddy V., Alam I., Khan Z.I.
Article, Sensors, 2023, DOI Link
View abstract ⏷
With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.
CODE-V: Multi-hop computation offloading in Vehicular Fog Computing
Article, Future Generation Computer Systems, 2021, DOI Link
View abstract ⏷
Vehicular Fog Computing (VFC) is an extension of fog computing in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency-aware and energy-aware application deployment in ITS. In this paper, we consider the problem of multi-hop computation offloading in a VFC network, where the client vehicles are connected to fog computing nodes by multi-hop LTE access points. Our scheme addresses three key aspects in a VFC architecture namely: (i) Optimal decision on local or remote task execution, (ii) Optimal fog node assignment, and (iii) Optimal path (multi-hop) selection for computation offloading. Considering the constraints on service latency, hop-limit, and computing capacity, the process of workload allocation across host vehicles, stationary and mobile fog nodes, and the cloud servers is formulated into a multi-objective, non-convex, and NP-hard Quadratic Integer Problem (QIP). Accordingly, an algorithm named Computation Offloading with Differential Evolution in VFC (CODE-V) is proposed. For each client task, CODE-V takes into account inter-fog cooperation, fog node acceptance probability, and the topological variations in the transportation fleets, towards optimal selection of a target fog node. We conduct extensive simulations on the real-world mobility traces of Shenzhen, China, to show that CODE-V reduces the average service latency and energy consumption by approximately 28% and 61%, respectively, compared to the state-of-the-art. Moreover, the CODE-V also gives better solution quality compared to standard DE∕rand∕1∕bin algorithm and the solutions generated by a CPLEX solver.
Fog Computing for Smart Grid Transition: Requirements, Prospects, Status Quos, and Challenges
Conference paper, EAI/Springer Innovations in Communication and Computing, 2021, DOI Link
View abstract ⏷
As a result of the tech advancements, which have not been realized, IT segments, viz. smart transportation and information technology modern smart grid (SG) frameworks, are incorporated with intelligent entities and devices. This form of infrastructure, when implemented in the Internet of Things (IoTs), including the sensor networks, creates a space of online and active objects. The ancient cloud involvement results in meeting more computational and analytical advancements that are decentralized and dynamically consume the resourceful SG environment. This research categorically analyzes the measure through which cloud computing facilities can effectively accomplish the vision and crucial necessities of SG environments, and the services and subdomain calls for fog-centered computing models. The main rationale of this research is to evaluate the capabilities of the fog computing algorithm in effectively interplaying with the foundational positioned cloud computing sustenance, which enables the introduction of novel breeds of latency and actual-time free SG network services. This research also considers the problems and thrusts illustrated over the viabilities of the fog computing for effective SG change.
Fog Computing for Big Data Analytics in IoT Aided Smart Grid Networks
Article, Wireless Personal Communications, 2020, DOI Link
View abstract ⏷
The recent integration of Internet of Things and Cloud Computing (CC) technologies into a Smart Grid (SG) revolutionizes its operation. The scalable and unlimited Store Compute and Networking (SCN) resources offered by CC enables efficient Big Data Analytics of SG data. However, due to remote location of Cloud Data Centers and congested network traffic, the cloud often gives poor performance for latency and energy critical SG applications. Fog Computing (FC) is thus proposed as a model that distributes the SCN resources at the intermediary devices, termed as Fog Computing Nodes (FCN), viz. network gateways, battery powered servers, access points, etc. By executing application specific logic at those nodes, the FC astonishingly reduces the response time as well as energy consumption of network elements. In this paper, we propose a mathematical framework that explains the Planning and Placement of Fog computing in smart Grid (PPFG). Basically, the PPFG model is formulated as an Integer Linear Programming problem that determines the optimal location, the capacity and the number of FCNs, towards minimizing the average response delay and energy consumption of network elements. Since this optimization problem is trivially NP-Hard, we solve it using an evolutionary Non-dominated Sorting Genetic Algorithm. By running the model on an exemplary SG network, we demonstrate the operation of proposed PPFG model. In fact, we perform a complete analysis of the obtained Pareto Fronts (PF), in order to better understand the working of design constraints in the PPFG model. The PFs will enable the SG utilities and architectural designers to evaluate the pros and cons of each of the trade-off solutions, leading to intelligent planning, designing and deployment of FC based SG applications.
Sparse two level topic model for extraction of general summary words
Article, Journal of Interdisciplinary Mathematics, 2020, DOI Link
View abstract ⏷
Extractive multi-document summarization methods based on topic models find relevant general concepts or topics that are most representative of the documents. These topics are used for sentence ranking and selection. In this paper, a two level topic model using spike and slab prior is proposed that identify better general topics for summarization. Spike and slab prior is used earlier for finding aspect specific topics. Proposed two level model uses spike and slab prior to achieve better general topics at high level of topic hierarchy. Experiments conducted on DUC2007 dataset show that proposed model is able to identify more summary oriented general words and improve ROUGE score.
Vehicular Fog Computing-Planning and Design
Conference paper, Procedia Computer Science, 2020, DOI Link
View abstract ⏷
With the advent of Internet of Vehicles (IoV), coupled with enormous number of devices performing computational and storage tasks between the cloud and users, Vehicular Fog Computing (VFC) can be an answer to the surging challenges in today's Intelligent Transportation Systems (ITS). However, the decentralized and heterogeneous nature of VFC infrastructures makes Vehicular Fog Network Planning (VFNP) problem complex and challenging. To deal with this problem, we propose an Integer Linear Programming (ILP) model that determines the optimal location, the capacity and the number of Fog Computing Nodes (FCN) towards minimizing the overall network delay and energy consumption. By running an example problem on default settings of GAMS CPLEX solver, we demonstrate the working of VFNP model and the associated constraints. We also analyzed the delay and energy variation for different problem sizes. The results show that, as the input size increases the overall delay increases linearly, the energy consumption follows parabolic path and the solution time shows a non-deterministic polynomial (NP) behavior.
Extractive multi-document summarization using relative redundancy and coherence scores
Akhtar N., Sufyan Beg M.M., Hussain M.M.
Conference paper, Journal of Intelligent and Fuzzy Systems, 2020, DOI Link
View abstract ⏷
Most extractive multi-document summarization (MDS) methods relies on extraction of content relevant sentences ignoring sentence relationships. In this work, we propose a unified framework for extractive MDS that also considers sentence relationships. We argue that adding a sentence to the summary increases summary score by relevance score of the new sentence plus some additional score which depends on the relationships of new sentence with other summary sentences. The quantification of additional score depends on how coherent the new sentence is with respect to the existing sentences in the summary. Simultaneously, some score is decreased from the summary score due to the redundancy which depends on overlap between new and existing summary sentences. To find the exact solution, sentence extraction problem is modeled as integer linear problem. The sentence relevance score is found using content and surface features of the sentence using topic model and regression framework. To find the relative coherence score, transition probabilities in the entity grid model are used. Redundancy between sentences is found using support vector regression that uses sentence overlapping features. The proposed method is evaluated on DUC datasets over query based multi-document summarization task. DUC 2006 dataset is used as training and development set for tuning parameters. Experimental results produce ROUGE score comparable to the state-of-the-art methods demonstrating the effectiveness of the proposed method.
Towards minimizing delay and energy consumption in vehicular fog computing (VFC)
Hussain M., Saad Alam M., Sufyan Beg M.M., Akhtar N.
Conference paper, Journal of Intelligent and Fuzzy Systems, 2020, DOI Link
View abstract ⏷
Vehicular Fog Computing (VFC) is a natural extension of Fog Computing (FC) in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency aware and energy aware application deployment in ITS. However, due to heterogeneity, scale and dynamicity of vehicular networks (VN), deployment of VFC is a challenging task. In this paper, we propose a multi-objective optimization model towards minimizing the response time and energy consumption of VFC applications. Using the concepts of probability and queuing theory, we propose an efficient offloading scheme for the fog computing nodes (FCN) used in VFC architecture. The optimization model is then solved using a modified differential evolution (MDE) algorithm. Extensive experimentations performed on real-world vehicular trace of Shenzhen, reveals the superiority of proposed VFC framework over generic cloud platforms.
Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities: Computing Platforms for Platforms for Electric Vehicle Integration in Smart Cities
Hussain M.M., Beg M.M.S., Alam M.S., Laskar S.H.
Book chapter, Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications, 2020, DOI Link
View abstract ⏷
Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.
Fog computing for internet of things (IoT)-aided smart grid architectures
Article, Big Data and Cognitive Computing, 2019, DOI Link
View abstract ⏷
The fast-paced development of power systems necessitates the smart grid (SG) to facilitate real-time control and monitoring with bidirectional communication and electricity flows. In order to meet the computational requirements for SG applications, cloud computing (CC) provides flexible resources and services shared in network, parallel processing, and omnipresent access. Even though CC model is considered to be efficient for SG, it fails to guarantee the Quality-of-Experience (QoE) requirements for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends CC by deploying localized computing and processing facilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG applications. By deploying localized computing facilities at the premise of users, it pre-stores the cloud data and distributes to SG users with fast-rate local connections. In this paper, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as a technology enabler for real-time SG analytics. We also present a three layer FC-based SG architecture, characterizing its features towards integrating massive number of Internet of Things (IoT) devices into future SG. We then propose a cost optimization model for FC that jointly investigates data consumer association, workload distribution, virtual machine placement and Quality-of-Service (QoS) constraints. The formulated model is a Mixed-Integer Nonlinear Programming (MINLP) problem which is solved using Modified Differential Evolution (MDE) algorithm. We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm. We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.
Fog computing model for evolving smart transportation applications
Book chapter, Fog and Edge Computing: Principles and Paradigms, 2019, DOI Link
View abstract ⏷
This chapter introduces the needs and prospects of adopting data-drive transportation architectures and the landscape of smart applications supported over adoption of such data-driven mobility models. It discusses which computer requirements can be best fulfilled through cloud computing and which require fog rollout. The chapter identifies the fog computing requirements of intelligent transportation systems (ITS) such as mission-critical architectures. It assesses the state of cloud platforms to store and compute support for such applications and discusses the proper mix of both computational models to best meet the mission-critical computing needs of smart transportation applications. The chapter presents a fog computing framework customized to support latency sensitive ITS applications. The fog orchestrating requirements in ITS domain are substantiated through an intelligent traffic lights management (ITLM) system case study. The chapter outlines the key big data issues, challenges, and future research opportunities, while developing a viable fog orchestrator for smart transportation applications.
Feasibility of Fog Computing in Smart Grid Architectures
Book chapter, Lecture Notes in Networks and Systems, 2019, DOI Link
View abstract ⏷
Contemporary Smart Grid (SG) systems are enticed by smart devices and entities due to unfolded developments in both the IT sectors viz. Intelligent Transportation and Information Technology. The intelligent transportation infrastructure elements when bestowed with Internet of Things (IoT) and sensor network of latter IT (Information Technology), makes every object active and brings them online. In such scenario, the traditional cloud deployment perishes to meet the analytics and computational exigencies for such dynamic cum resource-time critical subsystems. Starting with highlighting the mission-critical requirements of an idealized SG infrastructure, this work proposes an edge-centered FOG (From cOre to edGe) computing model primarily focused to realize the processing and computational objectives of SG. The objective of this work is to comprehend the applicability of FOG computing algorithms to interplay with the core-centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free utilities. Further, for demonstrating the feasibility of the proposed framework, the SG use case is considered and an exemplary FOG Service-Oriented Architecture (SOA) is depicted. Finally, the potential adoption challenges elucidated in the realization of the proposed framework are highlighted along with nascent research domains that call for efforts and investments in successfully guiding the FOG approaches into a pinnacle.
A FOG Computing Based Battery Swapping Model for Next Generation Transport
Hussain M.M., Alam M.S., Sufyan Beg M.M.
Book chapter, Lecture Notes in Networks and Systems, 2019, DOI Link
View abstract ⏷
It has been a consensus persuasion from automotive industries, policymakers, R&Ds and vehicle vendors that electric vehicle is the powertrain archetype for future transport. The current Electric, plug in electric and plug in hybrid electric vehicles (xEVs) no longer remain only a means of commute, but can act as prime actors to have active business participation with various markets in the power system such as V2G, demand side management (DSM) etc. The modern development in the information and communication technology (ICT) evolves such vehicle into intelligent vehicle (IV) and augments their utility to provide diverse services for Intelligent Transportation (ITS) infrastructure. However, due to lack of viable charging infrastructures the contemporary power system fails to accommodate the incoming xEV flux. The inability is manifested in the form poor quality of service, which causes customer dissatisfaction and ultimately lower adoption of xEVs. This work proposes an energy efficient battery swapping topology (BSS) adopting the notion of Internet of Things (IoT). The work introduced the innovative notion of integrating internet of things (IoT) into smart charging infrastructures and proposed a data driven IoT-BSS model whose operation is regulated through Fog computing and Big Data analytics. Further, a four layer fog computing execution stack is developed to set up the service oriented architecture (SOA) for an efficient and real-time decision making framework for next generation intelligent transportation. The work also highlights the data science prospects and challenges that can elucidate in course of realization the proposed infrastructure.
Big data analytics platforms for electric vehicle integration in transport oriented smart cities: Computing platforms for platforms for electric vehicle integration in smart cities
Hussain M.M., Beg M.M.S., Alam M.S., Laskar S.H.
Article, International Journal of Digital Crime and Forensics, 2019, DOI Link
View abstract ⏷
Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.
Fog computing for ubiquitous transportation applications—A smart parking case study
Book chapter, Lecture Notes in Electrical Engineering, 2019, DOI Link
View abstract ⏷
The current transportation architectures are heavily populated with smart devices and entities due to unfolded technological evolutions in Intelligent Transportation Systems (ITS). The ITS ecosystem, when introduced to Internet of Things (IoT) makes every object active and brings them online. Such devices generate data deluge that demand scalable storage and computational resources. Though centralized cloud-based solutions significantly circumvent those demands, but the current deployments still have silos and cease to meet the analytics and computational exigencies for such dynamic ITS subsystems. In this work, we investigate the current state of cloud-based solutions for fulfilling the mission-critical store and compute requirements of IoT-aided ITS architectures and revisit the motivations for adopting edge-centered fog computing paradigms. We also proposed a fog computing topology customized to ITS architectures. Further, the viability of proposed fog framework is demonstrated through a smart parking case study. Results show a significant improvement performance in terms of probabilistic QoS guarantees for private parking land owners, at the expense of a relatively small number of reserve premium spaces.
Public opinion on viability of xEVs in India
Saqib M., Hussain M.M., Alam M.S., Beg M.M.S., Sawant A.
Conference paper, Lecture Notes in Electrical Engineering, 2018, DOI Link
View abstract ⏷
This work demonstrates a smart charging system of electric vehicle using information technology and cloud computing. xEVs (electric plugin hybrid, battery electric vehicles) charging management system will be very helpful for the varying charging infrastructure demands, namely perspectives from automakers, electricity providers, vehicle owners, and charging service providers. Through dedicated interface, the developed system will provide real-time information to xEV users regarding nearest charging station with minimum queuing delay and with minimum charging cost through a secured online accessing mechanism for accessing state of charge (SOC) of the xEV’s battery being charged. The system not only provide an execution framework for the xEV users but also provide an optimal energy trading solution to all entities involved in a smart charging infrastructure such as charging station, aggregators, smart grid. The work also explains the cloud-enabled bidding strategies that look for day-ahead and term-ahead markets. The aggregators will use the smart decisions undertaken by cloud analytics to execute their bidding strategies in way to maximize the profit. Further, the work also assesses the possible cybersecurity aspects of such architectures along with providing possible solutions.
Fog Computing for Next Generation Transport- a Battery Swapping System Case Study
Article, Technology and Economics of Smart Grids and Sustainable Energy, 2018, DOI Link
View abstract ⏷
Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. Efficient EV charging control strategies that can affect the impacts and benefits is a potential research problem. Adopting the notion of IoT, in this paper, we present a Cloud-Fog based Battery Swapping Topology (BSS). A QoS ensuring timing model is proposed for defining the charging management of EV batteries across the BSS. For optimal BSS infrastructure planning, we also present a cost optimization framework, considering the timing and architectural constraints. The potential solution approaches for the given optimization formulation is also discussed.
Computing platforms for big data analytics in electric vehicle infrastructures
Hussain M.M., Beg M.M.S., Alam M.S., Krishnamurthy M., Ali Q.M.
Conference paper, Proceedings - 2018 4th International Conference on Big Data Computing and Communications, BIGCOM 2018, 2018, DOI Link
View abstract ⏷
With the emergence of ever-growing smart vehicular applications and ubiquitous deployment of IoT devices across different architectural layers of Intelligent Transportation System (ITS), data-intensive analysis emerges to be a major challenge. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. In this paper, we consider the case of Electric Vehicle (EV) to Smart Grid (SG) integration. The EVs are key players for Transport Oriented Smart Cities (TOSC) as they help cities to become greener by reducing emissions and carbon footprint. We analyze different use-cases in EV to SG integration to show how Big Data Analytics (BDA) platforms can play a vital role towards successful EV rollout. We then present two computing platforms namely, distributed cloud computing and edge/fog computing. We highlighted the distinguishing features of each towards supporting BDA activities in EV integration. Finally, we provide a detailed overview of opportunities, trends, and challenges of both these computing techniques.
Cognitive Fuzzy Rank Aggregation for Non-Transitive Rankings: An Institute Recommendation System Case Study
Hussain M.M., Rahman S.A., Beg M.M.S., Ali R.
Conference paper, Proceedings of 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018, 2018, DOI Link
View abstract ⏷
In this work, we used the notion of Rank Aggregation (RA) to develop a software prototype for Institute Recommendation System (IRS). Specifically, the objective is to devise an institute recommendation system that takes diverse rankings from various institute ranking websites as inputs and use cognitive functions to collate and aggregate them such that the resulting ranking is more consensus and reliable. Since the institute rankings provided by different academic ranking websites are partial lists, existing full-list based algorithms fail to provide a consensus ranking. In this regard, we proposed fuzzy Shimura Preference Order Rank Aggregation (SPORA) algorithm that works efficiently for both partial as well a full list. The notion is to integrate subjective measures prevalent in realworld rankings. Though obtaining an ideal ranking is computationally NP hard, the validity of the proposed aggregation algorithm is ascertained by evaluating the resultant rankings at multiple precision points (at top-10, at top-20 and at top-100 positions) using Normalized Modified Correlation Coefficient (NMCC). The performance of the SPORA function is further evaluated by comparing the values of the NMCC with the existing baseline algorithms. Results reflect the soundness of the proposed algorithm over the existing counterparts and prove productive when used (to be used) for developing any recommendation software.
A risk averse business model for smart charging of electric vehicles
Conference paper, Smart Innovation, Systems and Technologies, 2018, DOI Link
View abstract ⏷
Smart collaborations among the smart grid, electric vehicles, and aggregators will provide range of benefits to stakeholders involved in an intelligent transportation system (ITS). The EVs, nowadays, are becoming the epicenter of smart power system research towards the electrification of transport. However, massive penetration of EVs will pose management threats to the supporting smart grid in the foreseeable future. This work proposes a risk averse optimization framework for smart charging management of electric vehicles. Adopting conditional value at risk (CVaR) for estimating the risks, the work attempts to propose an optimized bidding strategy for the smart charging stations (SCS) that act on behalf of aggregators for managing the financial risk caused by the uncertainties. Finally, a fuzzified translation model is discussed along with notable methodologies as a solution strategy to the risk averse cost optimization problem.
Smart Electric Vehicle Charging Through Cloud Monitoring and Management
Saqib M., Hussain M.M., Alam M.S., Beg M.M.S., Sawant A.
Article, Technology and Economics of Smart Grids and Sustainable Energy, 2017, DOI Link
View abstract ⏷
Smart charging system of electric vehicle using cloud based monitoring and management is demonstrated in this work. xEVs (electric plugin hybrid, battery electric vehicles) Charging Management System is crucial for the dynamic demands of charging infrastructure, namely perspectives from automakers, electricity providers, vehicle owners and charging service providers. Through dedicated interface, the developed system is capable of providing real time information to xEVs users regarding nearest charging station with minimum queuing delay, with minimum charging cost through a secured online accessing mechanism for accessing Sate of the Charge (SOC) of the xEV’s battery being charged. The system not only provide an execution framework for the xEVs users but also provide an optimal energy trading solution to all entities involved in a smart charging infrastructure such as charging station, aggregators, smart grid etc. The work also explains the cloud enabled bidding strategies that look for day-ahead and term-ahead markets. The aggregators will use the smart decisions undertaken by cloud analytics to execute their bidding strategies in way to maximize the profit. Further, the work also assesses the possible cyber security aspects of such architectures along with providing possible solutions.
