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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.

Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs' energy more efficiently.

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