As recently studied, FPGAs (Field Programmable Gate Arrays) suffer from growing HT (Hardware Trojan) attacks and many techniques, e.g., RTL (Register-Transfer Level) codebased analyzing, have been presented to detect HTs on FPGAs. However, for most of the FPGA end users, they can only obtain bitstream rather than RTL code. Therefore, we present a new FPGA reverse engineering tool-chain. It can precisely transform FPGA bitstream to RTL code and therefore assists HT detection. In detail, we first construct an integrated database involving FPGA architecture information and bitstream mapping information. Then, we build two tools, namely, BRT (Bitstream Reversal Tool) and NRT (Netlist Reversal Tool). They can be combined together to retrieve RTL code from FPGA bitstream in moderate time. To demonstrate the effectiveness of our toolchain, we evaluate it qualitatively and quantitatively by using two benchmarks (ISCAS’85, ISCAS’89) and three real applications (8051 core, 68HC08 and AES). Our tool-chain is comprehensive since it covers all the reverse engineering stages, from bitstream to netlist and from netlist to code, without any support from other tools. Moreover, it rebuilds netlist with 100% correct rate and retrieves RTL code which is exactly functionally equivalent to the original one for all our benchmarks. To the best of our knowledge, it is the first tool which can perform integrated, precise reverse engineering for FPGAs, paving the way for netlist/code-based HT detection.

With the significant advance of connected vehicles, future demand for vehicular infotainment services will be greatly increased. Traditional content distribution approaches from typical cellular architecture suffer from long latency and unstable connections in vehicular environments and even may cause congestion on the backhaul network due to a large amount of requested data. In the paper, we propose a novel content delivery framework by leveraging the 5G edge networks, in which the content caching and data prefetching techniques are exploited accordingly. We investigate the comprehensive dynamic link utilization problem in 5G edge networks from the perspectives of vehicle users and network operator, respectively. For the vehicle users' perspective, our aim is to maximize the vehicular content distribution throughput through optimal scheduling of vehicular access link slots, and also the utilization problem of backhaul link slots in edge networks is studied to reduce the data access delay of vehicles. For the network operator's perspective, the objective is to maximize the total profit of the network operator, and therefore, the auction model is utilized for the vehicular access link slot scheduling and the backhaul link utilization is analyzed in terms of compensations and costs. Finally, extensive simulations are conducted to demonstrate the efficiency of the proposed solutions for edge content delivery of connected vehicles.

Nowadays, FPGA (Field Programmable Gate Array) has been widely used in IoT since it can provide flexible and scalable solutions to various IoT requirements. Meanwhile, hardware trojan, which may lead to undesired chip function or leak sensitive information, has become a great challenge for FPGA security. Therefore, distinguishing the trojan-infected FPGAs is quite crucial for reinforcing the security of IoT. To achieve this goal, we propose a clock-tree-concerned technique to detect the hardware trojans on FPGA. First, we present an experimental framework which helps us to collect the EM (Electromagnetic) radiation emitted by FPGA clock tree. Then, we propose a trojan identifying approach which extracts the mathematical feature of obtained EM traces, i.e., 2DPCA in this paper, and automatically isolates the trojan-infected FPGAs from the trojan-free ones by using a BP Neural Network. Finally, we perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid in detecting hardware trojans on FPGA. Specifically, for the Trust-hub benchmarks, we can find out the FPGA with always on trojans (100% detection rate) while identifying the triggered trojans with high probability (by up to 92%). In addition, we give a thorough discussion on how the experimental setup, such as probe step size, scanning area and chip ambient temperature, affects the trojan detection rate.

Reconfigurable metasurfaces have emerged as a promising alternative to the conventional transmitter of wireless communication systems, due to their abilities of encoding digital information onto electromagnetic properties without complex radio‐frequency chains. However, most of them are still limited to narrow operation bandwidth. Here, we propose a broadband metasurface‐based wireless communication system that can actively adapt to multiple users located at versatile directions through joint modulation of digital signals in the time domain and wave scatterings in the space domain. As exemplary demonstrations, highly directive beams are generated to enhance regional signals in real‐time customized for users in desired directions and reduce the signal leakage in undesired directions. Experiments are carried out to verify that the system can provide stable wireless communication service in a broad band of 3.7–5.1 GHz, within which the transmitted color picture enabled by the time‐varying spatial modulation of metasurface can be successfully recovered at the user terminals. The proposed system may offer untapped potentials for next‐generation communications and radar systems where regional signal enhancement, active adaption to users, and large channel capacities are required.

The deployment cost of advanced metering infrastructure (AMI), one of the key elements for smart grid, often arises from three sub-systems, respectively referred to as data management system, communication network, and smart devices. Among them, the costs of communication network account for a considerable proportion. In smart grid, smart devices are generally fixed in a certain position and schedule reading of interval meter data. They do not only cover smart meters, but also extend gas, water meters, and other sensors measuring light, humidity, and temperature. Many of them are often with battery supply, and this will bring utilization constraints especially in communication network. The deployment needs of reducing cost and the energy-constrained network for an extremely large-scale AMI infrastructure both require optimum communication technologies that consider energy consumption, minimize energy use, simplify network topology, and prolong network lifetime. As an emerging 3GPP radio interface technology specifically designed for low power wide area networks (LPWANs), Narrowband Internet of Things (NB-IoT) inherits from the existing LTE but does many simplifications. Its most prominent features, enhanced coverage and low power consumption, are being pursued for a large-scale AMI communication network. In this paper, we provide a survey of AMI communication, emphasize on the key technologies of NB-IoT, analyze the performance of NB-IoT network over a real-world, and demonstrate the potential of NB-IoT to support and boost AMI in smart grid.

HT (Hardware Trojan) is a malicious modification in the chip circuitry, which may lead to undesired chip function changing or sensitive information leaking once activated. As recently studied, HT has become one of the main threats for IoT security, and therefore, protecting IoT against HT attack attracts growing attentions from IoT researchers. In this paper, we propose an HT detection technique which makes use of chip temporal thermal information and SOM (Self-Organizing Map) neural network to automatically isolate the Trojan-infected chips with the Trojan-free ones, and meanwhile, confirm the Trojan location at the infected chips. The experimental results reveal that our method is effectiveness. Specifically, for the Trust-hub benchmarks, it can detect HTs which increase only 0.02% power consumption of the original design, and localize the Trojan positions precisely without any error. In addition, we demonstrate the advantages of our method over two existing HT detection methods, namely, TPM (Thermal and Power Map) and RON (Ring Oscillator Net), and make a thorough discussion on how the thermal image resolution, chip technology and clustering algorithm affect the Trojan detection results.