For next-generation logistics systems using autonomous vehicles and drones, spoofing of the GNSS location data induces serious problems. Although signal-based anti-spoofing has been studied, it is difficult to apply to current commercial GNSS modules in many cases. We investigate possibilities to detect spoofing of GNSS location data using multiple sensing devices and a decision tree classifier. Multiple features using the GNSS, beacons, and the IMU are defined and create a model to detect spoofing. Experimental results using learning-based classifier indicates the higher performances and generalization capability. The results also show that distance from beacons is useful to detect GNSS spoofing and indicate prospects of installation for the future drone highways.

Distributed ledger technology is becoming popular these days because of its high confidentiality, decentralization, and nontampering. It is suitable for replacing centralized security disadvantaged point transfer systems. Low-power wide area network (LPWAN) is capable for long-range communication with low-power consumption. The iconic features like wide area coverage and long battery-powered duration make it best to combine with large-scale IoT application deployment. In both industry and academic field, such combination of LPWAN and point transfer system is highly attended. However, the ledger management system generates too much data that low-bandwidth network such as LPWAN can hardly handle; meanwhile, the processing power’s requirement for small IoT devices is challenging. Towards addressing these issues, we design a packet transmission optimizing mechanism for a ledger-based point transfer system (LPTS) in LPWAN to reduce overall data traffic and build a simulator to evaluate its performance. Moreover, we have implemented the system and evaluated in field experiment.

In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.