Biblio
The increasing volume of domestic and foreign trade brings new challenges to the efficiency and safety supervision of transportation. With the rapid development of Internet technology, it has opened up a new era of intelligent Internet of Things and the modern marine Internet of Vessels. Radio Frequency Identification technology strengthens the intelligent navigation and management of ships through the unique identification function of “label is object, object is label”. Intelligent Internet of Vessels can achieve the function of “limited electronic monitoring and unlimited electronic deterrence” combined with marine big data and Cyber Physical Systems, and further improve the level of modern maritime supervision and service.
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.
In this time of ubiquitous computing and the evolution of the Internet of Things (IoT), the deployment and development of Radio Frequency Identification (RFID) is becoming more extensive. Proving the simultaneous presence of a group of RFID tagged objects is a practical need in many application areas within the IoT domain. Security, privacy, and efficiency are central issues when designing such a grouping-proof protocol. This work is motivated by our serial-dependent and Sundaresan et al.'s grouping-proof protocols. In this paper, we propose a light, improved offline protocol: parallel-dependency grouping-proof protocol (PDGPP). The protocol focuses on security, privacy, and efficiency. PDGPP tackles the challenges of including robust privacy mechanisms and accommodates missing tags. It is scalable and complies with EPC C1G2.