In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

In this paper, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to a group of Internet of Things (IoT) devices. The cognitive UAV shares the wireless spectrums of Primary Users (PUs) where it accesses the spectrums opportunistically when the corresponding channels are available. The UAV predicts the OFF periods of the PU, then it decides the number of consecutive transmission slots to be used for data transmission. To protect the PUs from harmful interference, the number of slots used for transmission should not exceed a certain threshold, i.e., maximum allowable interfering slots. We formulate our problem mathematically, where the objective is to maximize the minimum bits received by the IoT devices. The formulated problem is in a form of Mixed Integer non-Linear Program (MINLP). Therefore, we propose a successive convex approximation based algorithm to solve this problem after approximating it to a convex problem. We show that the algorithm converges to a Karush-Kuhn-Tucker (KKT) point. In the simulation, we examine how the OFF period model of the PUs, maximum number of transmission slots, maximum allowable interference threshold affect the maximum minimum bits received by the IoT devices.

The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning sensitive facilities. Consequently, a critical necessity revolves around the development of a proficient system with the capability to precisely identify UAVs and other flying objects even in challenging scenarios. Although advancements have been made in deep learning models, obstacles such as computational intricacies, precision limitations, and scalability issues persist. To overcome those obstacles, we proposed the concept of MultiFeatureNet (MFNet), a solution that enhances feature representation by capturing the most concentrated feature maps. Additionally, we present MultiFeatureNet-Feature Attention (MFNet-FA), a technique that adaptively weights different channels of the input feature maps. To meet the requirements of multi-scale detection, we presented the versions of MFNet and MFNet-FA, namely the small (S), medium (M), and large (L). The outcomes reveal notable performance enhancements. For optimal bird detection, MFNet-M (Ablation study 2) achieves an impressive precision of 99.8%, while for UAV detection, MFNet-L (Ablation study 2) achieves a precision score of 97.2%. Among the options, MFNet-FA-S (Ablation study 3) emerges as the most resource-efficient alternative, considering its small feature map size, computational demands (GFLOPs), and operational efficiency (in frame per second). This makes it particularly suitable for deployment on hardware with limited capabilities. Additionally, MFNet-FA-S (Ablation study 3) stands out for its swift real-time inference and multiple-object detection due to the incorporation of the FA module. The proposed MFNet-L with the focus module (Ablation study 2) demonstrates the most remarkable classification outcomes, boasting an average precision of 98.4%, average recall of 96.6%, average mean average precision (mAP) of 98.3%, and average intersection over union (IoU) of 72.8%. To encourage reproducible research, the dataset, and code for MFNet are freely available as an open-source project: github.com/ZeeshanKaleem/MultiFeatureNet .

Unmanned Aerial Vehicle (UAV) communications have received considerable attention in recent years as it can be applied to a wide range of applications. To expedite this development, it is essential to be able to model and accurately predict the wireless channels in UAV communication. Thus, in this paper, we compare various state-of-the-art empirical large-scale path loss models and provide quantitative insights into these models. Based on the operating frequency, environment, horizontal distance, and altitude, we recommend the suitable choice of empirical models for UAV communication. These insights not only facilitate more practical theoretical studies but also motivate measurement campaigns in the future.

In Unmanned Aerial Vehicles (UAVs)-assisted communications, there are two significant challenges that need to be addressed - optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service (QoS) requirements of users. To overcome these challenges, a reinforcement learning-based intelligent solution is proposed along with a reward function that associates users with UAVs in an intelligent manner. This solution aims to improve the system's sum rate performance by consuming less energy. Simulation results have been presented to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is more energy-efficient than the benchmark scheme while improving the system's sum rate.