In the dynamic realm of the internet of vehicles, ensuring robust Vehicle-to-Everything (V2X) communication is essential for advancing intelligent transportation systems. The IEEE 802.11p standard, while foundational, faces challenges in channel estimation due to its limited pilot structure, especially under high-mobility scenarios. Traditional pilot-aided estimation techniques often grapple with error propagation and diminished accuracy in such environments. Addressing these challenges, this paper introduces ‘HAN’, an innovative hybrid attention-based deep learning model that synergizes time-domain and frequency-domain attention mechanisms to enhance channel estimation. Empirical evaluations reveal that HAN achieves up to a 4 dB performance improvement in high signal-to-noise ratio conditions. Comprehensive testing across various channel conditions, modulation schemes, and vehicular speeds within the vehicle-to-vehicle expressway (VTV-EX) channel underscores its resilience and efficacy. Furthermore, implementation on the Xilinx ZCU102 FPGA platform demonstrates the model’s practicality for real-time applications in resource-constrained environments.

Internet of Things (IoT) security has heavily relied on Machine Learning (ML) techniques in recent years. However, these techniques inherently require a vast amount of labeled data for training, which is challenging. Moreover, these techniques are also prone to data shift problems. A practical approach to address these challenges can be through using self-learning. Hence, this paper proposes XNetIoT, an extremely quantized neural network architecture, to enhance the security of IoT networks sequentially using a Data Plane (DP). The proposed solution is two-fold: primarily, XNetIoT focuses on the binary in-network classification of incoming IoT traffic flows in the DP. Additionally, it accumulates the real-time features for updating the XNetIoT and is sent to the control plane. Within the control plane, the XNetIoT is retrained with accumulated real-time features. Further, the control plane is used to identify the specific attack types in the attack traffic. Subsequently, appropriate action is installed in the DP to mitigate the attacks. The comprehensive evaluation of our proposed solution yields 98.44% and 99.22% accuracy for in-network binary and multiclass classification using XNetIoT. Moreover, to assess the adaptability of XNetIoT, our model was trained using several distinct types of attacks and was subsequently evaluated through testing with unseen attacks, which are not part of training or testing. Interestingly, XNetIoT achieved an accuracy of 98.47% for detecting unknown attacks. Further, the average attack traffic flow classification time is 0.2475ms in XNetIoT.

IoT-based intrusions and network attacks are becoming ever more concerning. As a mitigatory measure, the IETF standardized Manufacturer Usage Description (MUD) which allows IoT device vendors to specify the legitimate communication patterns (as a MUD profile) of an IoT device. A MUD profile allows the validation of the actual communication pattern of an IoT device with the intended behavior at runtime. However, as the number of IoT devices increases, validation at runtime has scalability challenges in terms of the number of switch resources (e.g., TCAM) required to maintain MUD profiles.In this work, we propose a scalable data plane primitive and a system on top of the primitive, which together enforce MUD profiles of thousands of IoT devices in a P4 programmable switch data plane. Our main idea is to avoid inefficiencies because of the repetition of header values while representing MUD profile-based ACL rules. Further, we exploit the characteristics of header values in ACL rules of real IoT devices and carefully partition the rules across multiple hash-based exact match-action tables in the switch data plane. Since hash-based data structures can be implemented using SRAM which is cheap and abundantly available (order of MBs) in commodity programmable switches, our approach scales well for a large IoT network.

LoRa technology endows unprecedented ability to connect isolated geographical landscapes and build community networks that serve specific purposes. As the network grows, coherent routing of messages becomes imperative to meet the network’s objectives and Quality of Service (QoS) requirements. However, despite the recent rise in research and development centered around LoRa networks, not much research addresses the routing mechanisms in LoRa networks. Moreover, the LoRa routing mechanisms must run on low-power and resource-constrained devices, as these networks primarily target far-off locations or volatile environments such as volcanoes. Hence, this work proposes routing mechanisms (LoRaute) for LoRa networks that help route messages considering the messages’ QoS requirements. Also, a multipurpose network hardware is proposed, which serves as a LoRa network base station or a WiFi to LoRa bridge for TCP/IP communication. Additionally, this work furnishes and assesses the implementation of the LoRa network and the routing mechanisms in a real environment. The implementation results indicate a seamless and rapid setup of multihop LoRa networks. Moreover, the implementation achieves a routing table record size of 9 B, 24.45 ms routing latency, and 171 mA peak current consumption by the proposed LoRa node. Finally, the proposed system serves as a backhaul network for essential long-range communications, as demonstrated by the experimental setup.

The social IoMT-based activity-monitoring system comprises several devices with different datasets. It faces challenges like a collection of a global activity dataset which comprises a myriad of activities. In this article, we propose a federated Siamese network-based data-independent group activity segregator—Skipper—which aims to identify anomalies in an activity-monitoring social IoMT system. The novelty of this work is that Skipper does not require any dataset before its deployment, which removes the need for any prior training of the model for activity monitoring. As a proof of concept, we select activities pertaining to school environments to identify low-performing students in a classroom, who would require teachers’ close attention to ensure balanced growth and proper health. Skipper monitors the students independently for their motion signatures through a wearable device that consists of an accelerometer. A federated Siamese network calculates indices that signify the degree of similarity among the students’ activities. Skipper identifies the students who do not perform the same activity. With real-world implementations, we observe that Skipper requires network rates of 10 Kb/s, making it suitable for low bandwidth networks while we achieve just 20% CPU and 10 MB memory utilization on constrained edge devices. Further, with an increasing number of students up to 100, the time delay for final results is limited to 80 s. Hence, Skipper is a fast, easy, and accurate solution for recognizing outliers in IoMT social systems.

The adoption of Software-Defined Networks (SDN) and the shift towards programmable data planes have led to better network management. However, this has not been accompanied with the implementation of robust host authentication or access control mechanisms to improve network security and prevent unauthorized access to the network. The current literature has explored the implementation of the widely adopted authentication mechanism - port knocking in SDN to address the former. However, they suffer from two major drawbacks making them vulnerable to MITM (Man-In-The-Middle) attacks: unsecured transfer of the port knocking sequences between the SDN controller and hosts, and the lack of host identity verification mechanisms post port knocking authentication. This paper introduces P4-sKnock: a P4 based two level host authentication and access control mechanism. The first level introduces encrypted dynamic port knocking to secure the transfer of port knocking sequences over a compromised channel by encrypting them. Further, a challenge-response host identity verification mechanism is introduced as a second level authentication measure following which a host can be authorized, quarantined or blocked owing to the programmability of the P4 switch providing robust access control. Experimental analysis shows that P4-sKnock can authenticate a new SDN host within 500 ms and mitigate MITM attacks like IP spoofing and replay attacks making it significantly more secure than previous P4 based port knocking authentication systems.

Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviF L solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection.

The paper presents a novel, self-sufficient, Internet of Medical Things-based model called iNAP to address the shortcomings of anemia and polycythemia detection. The proposed model captures eye and fingernail images using a smartphone camera and automatically extracts the conjunctiva and fingernails as the regions of interest. A novel algorithm extracts the dominant color by analyzing color spectroscopy of the extracted portions and accurately predicts blood hemoglobin level. A less than 11.5 gdL \( ^{-1} \) value is categorized as anemia while a greater than 16.5 gdL \( ^{-1} \) value as polycythemia. The model incorporates machine learning and image processing techniques allowing easy smartphone implementation. The model predicts blood hemoglobin to an accuracy of \( \pm \) 0.33 gdL \( ^{-1} \) , a bias of 0.2 gdL \( ^{-1} \) , and a sensitivity of 90 \( \% \) compared to clinically tested results on 99 participants. Furthermore, a novel brightness adjustment algorithm is developed, allowing robustness to a wide illumination range and the type of device used. The proposed IoMT framework allows virtual consultations between physicians and patients, as well as provides overall public health information. The model thereby establishes itself as an authentic and acceptable replacement for invasive and clinically-based hemoglobin tests by leveraging the feature of self-anemia and polycythemia diagnosis.

In the past few years, some fusion-based approaches have been proposed to constitute discriminatory features for iris liveness detection. However, several methods exist in the literature for iris feature extraction and, thus, identifying an optimal composite of such features is still a vital challenge. This article also proposes a score-level fusion of two distinct domain-specific features, i.e., multiple binarized statistical image feature (BSIF) and DenseNet-based features. However, instead of randomly scrutinizing such features, statistical tests are executed on six predominant iris features to identify the optimal feature set to combine. Particularly, this work emphasizes textured-lens-based presentation attacks and aims to identify the type of contact lenses within the iris samples. The experimental analysis depicts that the domain-specific features substantially outperform the generic features while discriminating live iris from the artifacts. Furthermore, the proposed fusion-based approach is assessed on three iris datasets and the outcomes are compared with various state of the arts using three validation protocols in terms of equal error rate (EER). The comparative analysis perceived that the proposed method obtains a significant performance gain over the existing approaches and offers an improved benchmark for both, iris liveness detection and contact lens identification.

Improving the network lifetime is a major concern in Wireless Sensor Networks (WSNs) due to the limited network resources. As the sensor nodes are usually deployed in a random fashion across the network area, network-wide energy optimization becomes a challenge. An energy-optimized WSN offers improved fault tolerance, and this can be further enhanced with the help of Software Defined Networking (SDN). Hence, a Software Defined WSN (SDWSN) based energy efficient approach is proposed in this paper to improve the performance of the network. The proposed approach discusses an Energy Optimized Multi-Constrained Sustainable Routing (EOMCSR) model. This model formulates a Mixed Integer Linear Programming (MILP) problem to optimize the network resource based energy consumption in SDWSN. The simulation results are compared with the existing SDWSN and traditional WSN approaches with respect to the performance metrics for different numbers of rounds. The experimental results verify that EOMCSR achieves an efficiency of around8%and48%for average energy per node in comparison to the SDWSN approach (MES) and traditional approach (E-TORA) respectively, after100rounds for200nodes. Similarly, an efficiency of around36%and60%is achieved for the number of dead nodes. In addition to this, the proposed approach is also tested under different network scenarios w.r.t. multiple network performance metrics, and substantial improvements have been obtained w.r.t. each performance metric.

Advanced technology in agriculture can help to know about suitable environmental conditions, soil health status, water and fertilizer requirements, and crop monitoring at every plant growth stage, resulting in higher yield. In the past few decades, many countries have witnessed different rain and temperature patterns due to change in environmental conditions. The plantation schedule imparts mark-able effects on the crop yield. The correct and well-planned schedule can result in getting maximum productivity with limited resources. This study presents rule-based fuzzy classification method, for predicting the sowing fuzziness based on environmental conditions. The proposed study is a three-step procedure that identifies the sowing time of Cotton, Maize, and Groundnut. First, the knowledge and rule base of the fuzzy inference system is designed. In the second step rule base of the fuzzy inference system is optimized using multi-objective evolutionary algorithm NSGA-2, which helps maximize the accuracy and minimize the number of fuzzy rules taken for classification. Finally, the fuzziness of crop sowing in different slots is determined. Set of solutions in NSGA-2 are validated through a cross-validation approach. Further, the fuzziness of the sowing slot of Cotton, Maize, and Groundnut is correlated to yield in a given year to measure the model's effectiveness.

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children’s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.