論文

© 2013 IEEE. End-to-end (E2E) physical layer authentication for multi-hop wireless networks is still not well-explored by now. Like one step forward in this direction, this paper focuses on the E2E physical layer authentication in a dual-hop wireless network with an untrusted relay and proposes a corresponding physical layer authentication scheme. The scheme fully utilizes the location-specific features of both channel amplitude and delay interval of cascaded channels and adopts the artificial jamming technique, so that it is not only resistant to the impersonate attack from an unauthorized transmitter but also resilient to the replay attack from the untrusted relay. Theoretical analysis is further conducted to derive the expressions for the probabilities of false alarm and missed detection, which are two fundamental metrics of authentication performance. Finally, the numerical and simulation results are provided to illustrate both the efficiency of these theoretical results and the E2E authentication performance of dual-hop wireless networks under the proposed scheme.

Automotive sensing is a novel and appealing sensing technology in which sensors are installed into vehicles to utilize vehicles' mobility to conduct urban sensing in smart cities. The coverage performance is a major metric to characterizing the quality-of-service of sensing systems. Despite of the coverage metrics studied in static sensor networks that consist of only spatial dimension, the lack of a proper metric to characterizing the spatio-temporal coverage of automotive sensing systems is stunting the research, development and commercialization of such mobile sensing systems. To address this issue, we propose, for the first time to the best of our knowledge, a spatio-temporal coverage metric for automotive sensing. The proposed metric characterizes the coverage in both spatial and temporal dimensions in order to provide a guideline for smart cities application developers to evaluate the quality-of-service that can be expected from an automotive sensing system. Evaluation based on an automotive sensing system implemented in Fujisawa City, Japan is conducted to demonstrate the usage of this metric in a realistic scenario.

© 2018 IEEE. This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan.

© 2018 IPSJ. We propose in this paper an outdoor positioning system based on Wi-SUN FAN network, the goal of which is to protect the elderly, young children and even pets via estimating their locations in a city. In order to achieve long-term portability and network-side positioning, the system does not directly rely on GPS receiver mounted on terminals but use machine learning for location estimation via the received signal strength indication (RSSI) measurements. In particular, the system consists of Wi-SUN beacons, Wi-SUN base-stations and vehicular devices. A beacon, attached to the one to be positioned, broadcasts wireless signal periodically so that its location can be estimated using machine learning algorithms from the RSSIs measured at multiple base-stations that are densely deployed over a city to construct an ad hoc network. Using the mobility of vehicles that roam over a city routinely, such like garbage collection trucks, buses and taxies. Vehicular devices containing both a Wi-SUN beacon and a GPS are used to collect RSSIs and the corresponding GPS coordinates to train the estimation models. We develop a prototype system consisting of 9 base-stations and deploy it to our university campus to conduct a field experiment to validate the proposed approach. Offline analysis on the data collected from the experiment showed that a RandomForest learner performs best among four selected learning algorithms using the default parameters of Weka 3.8, which achieves a mean absolute error of 35.43m and a root mean squared error of 44.21m, respectively. Evaluation on network performance is also conducted.

© 2013 IEEE. The remarkable success of machine learning (ML) in a variety of research domains has inspired academic and industrial communities to explore its potential to address hardware Trojan (HT) attacks. While numerous works have been published over the past decade, few survey papers, to the best of our knowledge, have systematically reviewed the achievements and analyzed the remaining challenges in this area. To fill this gap, this article surveys ML-based approaches against HT attacks available in the literature. In particular, we first provide a classification of all possible HT attacks and then review recent developments from four perspectives, i.e., HT detection, design-for-security (DFS), bus security, and secure architecture. Based on the review, we further discuss the lessons learned in and challenges arising from previous studies. Despite current work focusing more on chip-layer HT problems, it is notable that novel HT threats are constantly emerging and have evolved beyond chips and to the component, device, and even behavior layers, therein compromising the security and trustworthiness of the overall hardware ecosystem. Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security threats and requirements in each layer to provide a guideline for future research in this direction.