Biblio
The Internet of Things (IoT) is a novel paradigm that enables the development of a slew of Services for the future of technology advancements. When it comes to IoT applications, the cyber and physical worlds can be seamlessly integrated, but they are essentially limitless. However, despite the great efforts of standardization bodies, coalitions, companies, researchers, and others, there are still a slew of issues to overcome in order to fully realize the IoT's promise. These concerns should be examined from a variety of perspectives, including enabling technology, applications, business models, and social and environmental consequences. The focus of this paper is on open concerns and challenges from a technological standpoint. We will study the differences in technical such Sigfox, NB-IoT, LoRa, and 6LowPAN, and discuss their advantages and disadvantage for each technology compared with other technologies. Demonstrate that each technology has a position in the internet of things market. Each technology has different advantages and disadvantages it depends on the quality of services, latency, and battery life as a mention. The first will be analysis IoT technologies. SigFox technology offers a long-range, low-power, low-throughput communications network that is remarkably resistant to environmental interference, enabling information to be used efficiently in a wide variety of applications. We analyze how NB-IoT technology will benefit higher-value-added services markets for IoT devices that are willing to pay for exceptionally low latency and high service quality. The LoRa technology will be used as a low-cost device, as it has a very long-range (high coverage).
Because the underwater acoustic communication network transmits data through the underwater acoustic wireless link, the Underwater Acoustic Communication Network is easy to suffer from the external artificial interference, in this paper, the detection algorithm of wormhole attack in Underwater Acoustic Communication Network based on Azimuth measurement technology is studied. The existence of wormhole attack is judged by Azimuth or distance outliers, and the security performance of underwater acoustic communication network is evaluated. The influence of different azimuth direction errors on the detection probability of wormhole attack is analyzed by simulation. The simulation results show that this method has a good detection effect for Underwater Acoustic Communication Network.
This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.
With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).
Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.
This paper exploits the possibility of exposing the location of active eavesdropper in commodity passive RFID system. Such active eavesdropper can activate the commodity passive RFID tags to achieve data eavesdropping and jamming. In this paper, we show that these active eavesdroppers can be significantly detrimental to the commodity passive RFID system on RFID data security and system feasibility. We believe that the best way to defeat the active eavesdropper in the commodity passive RFID system is to expose the location of the active eavesdropper and kick it out. To do so, we need to localize the active eavesdropper. However, we cannot extract the channel from the active eavesdropper, since we do not know what the active eavesdropper's transmission and the interference from the tag's backscattered signals. So, we propose an approach to mitigate the tag's interference and cancel out the active eavesdropper's transmission to obtain the subtraction-and-division features, which will be used as the input of the machine learning model to predict the location of active eavesdropper. Our preliminary results show the average accuracy of 96% for predicting the active eavesdropper's position in four grids of the surveillance plane.