The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.
With the increasing and diversified Internet of Things (IoT) devices, more IoT heterogeneous wireless networks have emerged, providing more network services for IoT devices, especially mobile phones and other roaming devices. However, there are also some malicious users who use different means to attack the security of the network, so that more users begin to pay attention to the identity authentication and privacy protection of the Internet of Things. This paper designs an IoT node roaming authentication model, which is used to enhance the security authentication capability of the Internet of Things to roaming devices. In order to effectively prevent malicious nodes from connecting to the network, this paper proposes a roaming authentication protocol based on heterogeneous fusion mechanism (HFM-IoT). The authentication protocol uses the remote authentication server in the local and remote areas to perform interactive authentication on the roaming device, which increases the difficulty of attacking or infecting multiple network areas by malicious nodes. According to the security analysis, the protocol can protect against multiple network attacks, and it can be seen from the experimental simulation results that the protocol has lower energy burden and authentication delay. INDEX TERMS Internet of things, heterogeneous network, node roaming, identity authentication.
The target detection algorithms have the problems of low detection accuracy and susceptibility to occlusion in existing smart cities. In response to this phenomenon, this paper presents an algorithm for target detection in a smart city combined with depth learning and feature extraction. It proposes an adaptive strategy is introduced to optimize the algorithm search windows based on the traditional SSD algorithm, which according to the target operating conditions change, strengthening the algorithm to enhance the accuracy of the objective function which is combined with the weighted correlation feature fusion method, and this method is a combination of appearance depth features and depth features. Experimental results show that this algorithm has a better antiblocking ability and detection accuracy compared with the conventional SSD algorithms. In addition, it has better stability in a changing environment.
In cognitive relay networks, the cognitive user opportunistically accesses the authorized spectrum segment of the primary user and simultaneously serves as the data relay node of the primary user while sharing the spectrum resource of the primary user. This not only improves the utilization efficiency of the network spectrum resources but also improves the throughput of the primary users. However, if the primary user randomly selects the relay node, there is no guarantee for an optimal throughput. Moreover, the system power consumption may increase. In order to improve the throughput of cognitive relay network and optimize system utility, this paper proposes a cognitive relay network throughput optimization algorithm based on deep reinforcement learning. For the system model of cognitive relay networks, the Markov decision process is used to describe the channel transition probability of the system model in the paper. The algorithm proposes a cooperative wireless network cooperative relay strategy, analyzes the system outage probability under different transmission modes, and optimizes the system throughput by minimizing the outage probability. Then, the maximum utility optimization strategy based on deep reinforcement learning is proposed to maximize the system utility revenue by selecting the optimal behavior. The experimental results show that the proposed algorithm has a good effect in improving system throughput and optimizing system energy efficiency.
This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of “excessive strangulation,” and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.
Vehicle mobile Internet of Things uses sensor technology, mobile Internet technology, and intelligent computing technology to effectively monitor and provide comprehensive services for vehicle operation status. It is an important part of building a smart city, making urban transportation more efficient, environmentally friendly, intelligent, and safety. In the vehicle mobile Internet of Things, when in-vehicle sensors are in communication with each other, they are often affected by factors, such as network mobility, transmission range, and signal interference. Therefore, in order to ensure the continuity of communication between nodes and improve the quality of network communication, this paper proposes a vehicle mobile Internet of Things coverage enhancement algorithm (PANM). First, we derive the probabilistic analysis model of communication duration by analyzing the functional relationship of vehicle initial velocity, acceleration, spacing distance, and communication duration. Then, under the premise of ensuring the communication duration, in order to improve the coverage of the vehicle mobile Internet of Things, the network overlap ratio is introduced in the probability analysis model. Finally, we improve the network coverage performance of omnidirectional radiation and fan-shaped radiation communication models of vehicle mobile Internet of Things by limiting the overlap ratio threshold. The experimental simulation results indicate that the PANM algorithm can reduce the packet loss rate of the vehicle network and have a shorter communication delay.INDEX TERMS Vehicle mobile Internet of Things, communication duration, probability analysis, overlap ratio, network coverage enhancement algorithm. I. INTRODUCTIONCyber Physics System (CPS) is a multi-dimensional complex system integrating computing, network and physical environment. Vehicle mobile internet of things as one of the main research fields of CPS, has received the attention of many researchers in recent years. VMIT (Vehicle Mobile Internet of Things) is an overlay network established by a vehicle equipped with a remote communication device or a communication device with a certain sensing range [1], [2]. The VMIT can collect the data of the location of the vehicle and its surroundings and transmit the data to other vehicles, The associate editor coordinating the review of this manuscript and approving it for publication was Chunsheng Zhu.
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