On Detection of Sybil Attack in Large-Scale VANETs Using Spider-Monkey Technique

Sybil security threat in vehicular ad hoc networks (VANETs) has attracted much attention in recent times. The attacker introduces malicious nodes with multiple identities. As the roadside unit fails to synchronize its clock with legitimate vehicles, unintended vehicles are identified, and therefore erroneous messages will be sent to them. This paper proposes a novel biologically inspired spider-monkey time synchronization technique for large-scale VANETs to boost packet delivery time synchronization at minimized energy consumption. The proposed technique is based on the metaheuristic stimulated framework approach by the natural spider-monkey behavior. An artificial spider-monkey technique is used to examine the Sybil attacking strategies on VANETs to predict the number of vehicular collisions in a densely deployed challenge zone. Furthermore, this paper proposes the pseudocode algorithm randomly distributed for energy-efficient time synchronization in two-way packet delivery scenarios to evaluate the clock offset and the propagation delay in transmitting the packet beacon message to destination vehicles correctly. The performances of the proposed technique are compared with existing protocols. It performs better over long transmission distances for the detection of Sybil in dynamic VANETs' system in terms of measurement precision, intrusion detection rate, and energy efficiency

Misbehavior aware on-demand intrusion detection system to enhance security in VANETs with efficient rogue nodes detection and prevention techniques

Vehicular ad-hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The goal behind sharing the information through beacon messages is to disseminate network state or emergency information. The exchange of information is susceptible to security attacks of different kinds. Amongst various problems to be solved in VANETs is the issue of rogue nodes and their impact on the network. Rogue nodes are malicious vehicles that are vicious to cause severe damage to the network by modifying or altering false data in beacon messages that could lead to catastrophic consequences like trapping a group of vehicles, road accidents, vehicle collisions, etc. This thesis discusses the problems associated with the security VANETs in the presence of rogue nodes. We proposed three novel intrusion detection frameworks to detect the rogue nodes responsible for false information, Sybil, and platoon control maneuver attacks only by analyzing and comparing the beacon messages broadcast over the network. The novelty of our frameworks lies in containing network damage and securing VANETs from the harmful impact of rogue nodes. The proposed frameworks are simulated using SUMO, OMNET++, and VENTOS, and the results obtained have been presented, discussed, and compared to existing frameworks. Results show that the developed methods improve the systems’ performance compared to existing methods even when the number of rogue nodes increases in the region

Optimization of Intelligent Transportation System using Biologically-Inspired Vehicular Ad hoc Networks for Achieve the Desired Performance

Many innovations made possible by the Intelligent Transportation System (ITS), such as media apps, encrypted financial transactions, and effective traffic management, rely heavily on vehicular ad hoc networks (VANETs). Using bio-inspired methodologies, This study looks back at the past and forward to the future to examine all of the routing challenges in VANETs, whether they are associated with a chain of related routing tasks or are aimed at a group of distinct approaches to routing. The high node mobility and unpredictable vehicle distribution (on the road) lead to major issues for VANETs, including the design of a network's physical architecture and unstable connections. VANET's provision of reliable and appropriate vehicular contact in situations requiring good service is crucial. As a result, effective means of navigation are desperately needed in VANET. Hence, in this paper, we examine the Bio-Inspired vehicular ad hoc networks (Bio-VANETs), wherein, should a suggested algorithmic network fail at any given node or vehicle, the remaining vehicles may be able to take over the task of relaying the data to the necessary nodes to achieve the desired performance. Route lifetime increases, and connection failures are decreased when the shortest way is selected using the fewest possible hops over highly connected links. In addition, the received signal intensity fluctuations due to vehicle density and speed are assessed. Packet Delivery Ratio, Optimal Performance, Accuracy and Efficiency of Bio-VANET are discussed and simulated against other methods that are existing models

Assays in analgesic studies

Pain is a multiplex uncomfortable experience consisting of sensory cases including emotion, time, cognition, time and motivation. It can be persistent, chronic or momentary i.e. lasting for a very short period of time and its location can either be muscular or visceral. Analgesics are molecules that particularly mitigate pain by acting on the peripheral pain mechanism or central nervous system without remarkably responsiveness.Different animals have been used as experimental models for the direct investigation and study of neuronal activity in animals which have been administered with anaesthesia using either invasive procedures or by the study of their behaviour

Designing a wind energy harvester for connected vehicles in green cities

\ua9 2021 by the authors. Licensee MDPI, Basel, Switzerland. Electric vehicles (EVs) have recently gained momentum as an integral part of the Internet of Vehicles (IoV) when authorities started expanding their low emission zones (LEZs) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs, not only to support the smooth and sustainable operation of EVs, but also to ensure connectivity between the vehicle and the infrastructure in the critical times such as disaster recovery operation. In this context, renewable energy sources (such as wind energy) have an important role to play in the automobile sector towards designing energy-harvesting electric vehicles (EH-EV) to mitigate energy reliance on the national grid. In this article, a novel approach is presented to harness energy from a small-scale wind turbine due to vehicle mobility to support the communication primitives in electric vehicles which enable plenty of IoV use cases. The harvested power is then processed through a regulation circuitry to consequently achieve the desired power supply for the end load (i.e., battery or super capacitor). The suitable orientation for optimum conversion efficiency is proposed through ANSYS-based aerodynamics analysis. The voltage-induced by the DC generator is 35 V under the no-load condition while it is 25 V at a rated current of 6.9 A at full-load, yielding a supply of 100 W (on constant voltage) at a speed of 90 mph for nominal battery charging

A metaheuristic optimization approach for energy efficiency in the IoT networks

© 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches

Designing a wind energy harvester for connected vehicles in green cities

Electric vehicles (EVs) have recently gained momentum as an integral part of the Internet of Vehicles (IoV) when authorities started expanding their low emission zones (LEZs) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs, not only to support the smooth and sustainable operation of EVs, but also to ensure connectivity between the vehicle and the infrastructure in the critical times such as disaster recovery operation. In this context, renewable energy sources (such as wind energy) have an important role to play in the automobile sector towards designing energy-harvesting electric vehicles (EH-EV) to mitigate energy reliance on the national grid. In this article, a novel approach is presented to harness energy from a small-scale wind turbine due to vehicle mobility to support the communication primitives in electric vehicles which enable plenty of IoV use cases. The harvested power is then processed through a regulation circuitry to consequently achieve the desired power supply for the end load (i.e., battery or super capacitor). The suitable orientation for optimum conversion efficiency is proposed through ANSYS-based aerodynamics analysis. The voltage-induced by the DC generator is 35 V under the no-load condition while it is 25 V at a rated current of 6.9 A at full-load, yielding a supply of 100 W (on constant voltage) at a speed of 90 mph for nominal battery charging

Advanced Energy Harvesting Technologies

Energy harvesting is the conversion of unused or wasted energy in the ambient environment into useful electrical energy. It can be used to power small electronic systems such as wireless sensors and is beginning to enable the widespread and maintenance-free deployment of Internet of Things (IoT) technology. This Special Issue is a collection of the latest developments in both fundamental research and system-level integration. This Special Issue features two review papers, covering two of the hottest research topics in the area of energy harvesting: 3D-printed energy harvesting and triboelectric nanogenerators (TENGs). These papers provide a comprehensive survey of their respective research area, highlight the advantages of the technologies and point out challenges in future development. They are must-read papers for those who are active in these areas. This Special Issue also includes ten research papers covering a wide range of energy-harvesting techniques, including electromagnetic and piezoelectric wideband vibration, wind, current-carrying conductors, thermoelectric and solar energy harvesting, etc. Not only are the foundations of these novel energy-harvesting techniques investigated, but the numerical models, power-conditioning circuitry and real-world applications of these novel energy harvesting techniques are also presented