A behavior-based malware spreading model for vehicle-to-vehicle communications in VANET networks

Network attacking using malware has become very popular on the Internet and in many other networks, namely Vehicular Ad-hoc Network (VANET) networks. It is required to have the model describing the malware spreading based on factors, which directly affect this process to limit its influences. In this paper, we propose a mathematical model called SEIR-S (Susceptible– Exposed–Infectious–Recovered–Susceptible) based on the characteristics of the VANET network and the well-known disease-spreading model SIR (Susceptible–Infectious–Recovered). We take into account possible behaviors of malware and provide the corresponding states to vehicles: Susceptible (S), Exposed (E), Infectious (I), Recovered (R). We evaluate the basic reproduction number R0 of the model and perform a stability analysis of the proposed model. The results show that, when R0 < 1, the malware spreading will gradually decrease, and, when R0 > 1, that spreading cannot be extinguished. We also point out the condition that we can control the endemic in the VANET network. In addition, the correctness of the proposed model is verified using both numerical analysis and agent-based simulation on NetLogo. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Intelligent microservice based on blockchain for healthcare applications

Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques. This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment. The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT. To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study. Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness. Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan. Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilizationwith varying peers and orderer nodes. The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios. © 2021 Tech Science Press. All rights reserved

Energy-Efficient Relay-Based Void Hole Prevention and Repair in Clustered Multi-AUV Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) enable various oceanic applications which require effective packet transmission. In this case, sparse node distribution, imbalance in terms of overall energy consumption between the different sensor nodes, dynamic network topology, and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a relay-based void hole prevention and repair (ReVOHPR) protocol by multiple autonomous underwater vehicles (AUVs) for UWSN. ReVOHPR is a global solution that implements different phases of operations that act mutually in order to efficiently reduce and identify void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as ocean depth (levels)-based equal cluster formation, dynamic sleep scheduling, virtual graph-based routing, and relay-Assisted void hole repair. For energy-efficient cluster forming, entropy-based eligibility ranking (E2R) is presented, which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented by the dynamic kernel Kalman filter (DK2F) algorithm in which sleep and active modes are based on the node's current status. Intercluster routing is performed by maximum matching nodes that are selected by dual criteria, and also the data are transmitted to AUV. Finally, void holes are detected and repaired by the bicriteria mayfly optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes and data packet loss and maximizes the quality of service (QoS) and energy efficiency of the network. This protocol is timely dealing with node failures in packet transmission via multihop routing. Simulation is implemented by the NS3 (AquaSim module) simulator that evaluates the performance in the network according to the following metrics: Average energy consumption, delay, packet delivery rate, and throughput. The simulation results of the proposed REVOHPR protocol comparing to the previous protocols allowed to conclude that the REVOHPR has considerable advantages. Due to the development of a new protocol with a set of phases for data transmission, energy consumption minimization, and void hole avoidance and mitigation in UWSN, the number of active nodes rate increases with the improvement in overall QoS. © 2021 Amir Chaaf et al