Ad hoc networks are made up of a collection of wireless mobile nodes that form a temporary network with no pre-existing infrastructure or centralized management.Routing policies are crucial in determining how traffic is forwarded across a network. Adhoc networks necessitate a routing method that is very adaptable. Finding the shortest path (SP) between source and destination in a specific period of time to meet Quality of Service standards is one of the most common issues in these networks (QoS). QoS routing is difficult in an Ad hoc network because the topology changes frequently and it takes time since many QoS criteria such as distance, cost, and energy are all variable, and the state information supplied for routing is inherently faulty. The optimum path for Adhoc networks is found using a Genetic Algorithm (GA) in this paper.GA uses natural evolution-inspired methodologies to find answers to optimization problems. Crossover and mutation operations, as well as the proper chromosome structure, are all defined.
One of the fastest growing sectors is wireless technology, which is evolving in all areas of mobile and wireless communications. Wireless technology has increased greatly in the last decade. 7.5 Generation (G) represents the history of wireless technology today. With 6G and 7G, data transmission rates will be higher over Future Generation wireless technology. With new technologies emerging in all fields of mobile and wireless communications, wireless technology continues to emerge as one of the hottest sectors with a high rate of development. Currently, 5G mobile communications systems are just getting started. Our current infrastructure supports a number of technologies, including voice over IP (VoIP), broadband data access over wireless, and more. This paper discusses several generations of wireless technologies from 0G to 7G. Wireless technology is important and beneficial to society. In this paper, we compare all of the generations and explain how each generation uses technology in its execution, application, and usage.
Modern network and Industrial Internet of Things (IIoT) technologies are quite advanced. Networks experience data breaches annually. As a result, an Intrusion Detection System is designed for enhancing the IIoT security protection under privacy laws. The Internet of Things' structural system and security performance criteria must meet high standards in an adversarial network. The network system must use a system that is very stable and has a low rate of data loss. The basic deep learning network technology is picked after analysing it with a huge number of other network configurations. Further, the network is upgraded and optimised by the Convolutional Neural Network technique. Additionally, an IIoT anti-intrusion detection system is built by combining three network technologies. The system's performance is evaluated and confirmed. The proposed model gives a better detection rate with a minimum false positive rate, and good data correctness. As a result, the proposed method can be used for securing an IIoT data privacy under the law.
Clustering in VANETs, which dynamically evolve into wireless networks, is difficult due to the networks' frequent disconnection and fast changing topology. The stability of the cluster head (CH) has a huge impact on the network's robustness and scalability. The overhead is decreased. The stable CH assures that intra- and inter-cluster communication is minimal. Because of these difficulties, the authors seek a CH selection technique based on a weighted combination of four variables: community neighborhood, quirkiness, befit factor, and trust. The stability of CH is influenced by the vehicle's speed, distance, velocity, and change in acceleration. These are considered for in the befit factor. Also, when changing the model, the precise location of the vehicle is critical. Thus, the predicted location is used to evaluate CH stability with the help of the Kalman filter. The results showed that the befit factor performed better than the latest developments. Because of the high speed of the vehicle, dynamic changes and frequent communication link breaks are unavoidable. In order to fully perceive issue, a graphing approach employed to assess the eccentricity then the communal neighborhood. Using Eigen gap heuristic, the link dependability is determined. Trust is the final important parameter that has not yet been taken into account in the weighted method. The trust levels are specifically being evaluated for the primary users using an adaptive spectrum sensing. Long short-term memory (LSTM), a deep recurrent learning network, used to train the likelihood of detection under diverse signal and noise situations. By using LSTM model, significantly decreased the false rate. The cluster head stability has improved for high traffic density, significantly improved according to the comparative analysis with the weighted and individual metrics. The efficiency of the network has also greatly increased in terms of throughput, packet delay, packet delay ratio, and energy consumption.
Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular these days. One among the major technological developments of today are UAVs or drones. The coordination and coverage capabilities of large clusters of UAVs, or their cooperative capabilities for such goals as terrain mapping, make them of particular interest. This paper explores the use of unmanned aerial vehicles in smart and modern cities in depth. Future wireless networks will likely include UAVs to facilitate wireless broadcasting and support high-speed transmissions. Various layer techniques are discussed in this paper. Moreover, an overview of the latest UAV communication technologies and network topologies has been presented. Military and commercial applications have attracted a lot of interest in unmanned aerial vehicles. Due to their low cost and flexible deployment, UAVs are considered valuable in 5G and 6G networks due to their communication capabilities. Like aerial base stations, relays, or mobile users in cellular networks, UAVs can provide airborne wireless coverage in a variety of ways. Wireless links can only be established temporarily with UAVs. A great challenge is to extend UAV communication's lifetime and develop low-power, green UAV communication. A comprehensive study of green UAV communications has been presented in this paper. Furthermore, an overview of UAV applications is also illustrated. Additionally, some promising research topics and methods are being discussed.
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