Review various technologies used to manage Traffic Management System using IOT

Internet of things (IoT) is the network of the devices includes the updating in technology, various devices are using sensors, actuators, embedded computing and cloud computing. This type of system leads to smart architecture in the home, cities and smart world. IoT plays an important role in traffic controlling and managing. In this paper, we give an overview of the various methods of traffic control management. With the help of this IOT kit, which includes different sensors to collect the data and process it accordingly with the help of big data analysis and deep learning algorithms, most accurate and efficient results are obtained for traffic management.

Science & technology trends in cognitive radar concept

Currently, human operators provide cognition in a radar system. However, advances in the “digitization” of radar front-ends, including digital arbitrary waveform generators (AWG) and advanced high performance embedded computing (HPEC) make it possible to vary all key radar parameters (power, pulse length, number of pulses, pulse repetition frequency (PRF), modulation, frequency, polarization) on a pulse-by-pulse basis within ns or ms and over a wide operating range. This timescale is much faster than the decision-making ability of a human operator. The cognitive-inspired techniques in radar, that are intensively developing last years, mimic elements of human cognition and the use of external knowledge to use the available system resources in an optimal way for the current goal and environment. Radar systems based on the perception-action cycle of cognition that senses the environment, learns relevant information from it about the target and the background and then adapts the radar to optimally satisfy the needs of the mission according to a desired goal are called cognitive radars. In the article, recent ideas and applications of cognitive radars were analyzed.

Breaking TrustZone memory isolation and secure boot through malicious hardware on a modern FPGA-SoC

FPGA-SoCs are heterogeneous embedded computing platforms consisting of reconfigurable hardware and high-performance processing units. This combination offers flexibility and good performance for the design of embedded systems. However, allowing the sharing of resources between an FPGA and an embedded CPU enables possible attacks from one system on the other. This work demonstrates that a malicious hardware block contained inside the reconfigurable logic can manipulate the memory and peripherals of the CPU. Previous works have already considered direct memory access attacks from malicious logic on platforms containing no memory isolation mechanism. In this work, such attacks are investigated on a modern platform which contains state-of-the-art memory and peripherals isolation mechanisms. We demonstrate two attacks capable of compromising a Trusted Execution Environment based on ARM TrustZone and show a new attack capable of bypassing the secure boot configuration set by a device owner via the manipulation of Battery-Backed RAM and eFuses from malicious logic.

A Pervasive Collaborative Architectural Model at the Network’s Periphery

Pervasive collaborative computing within the Internet of Things (IoT) has progressed rapidly over the last decade. Nevertheless, emerging architectural models and their applications still suffer from limited capacity in areas like power, efficient computing, memory, connectivity, latency and bandwidth. Technological development is still in progress in the fields of hardware, software and wireless communications. Their communication is usually done via the Internet and wireless via base stations. However, these models are sometimes subject to connectivity failures and limited coverage. The models that incorporate devices with peer-to-peer (P2P) communication technologies are of great importance, especially in harsh environments. Nevertheless, their power-limited devices are randomly distributed on the periphery where their availability can be limited and arbitrary. Despite these limitations, their capabilities and efficiency are constantly increasing. Accelerating development in these areas can be achieved by improving architectures and technologies of pervasive collaborative computing, which refers to the collaboration of mobile and embedded computing devices. To enhance mobile collaborative computing, especially in the models acting at the network’s periphery, we are interested in modernizing and strengthening connectivity using wireless technologies and P2P communication. Therefore, the main goal of this paper is to enhance and maintain connectivity and improve the performance of these pervasive systems while performing the required and expected services in a challenging environment. This is especially important in catastrophic situations and harsh environments, where connectivity is used to facilitate and enhance rescue operations. Thus, we have established a resilient mobile collaborative architectural model comprising a peripheral autonomous network of pervasive devices that considers the constraints of these resources. By maintaining the connectivity of its devices, this model can operate independently of wireless base stations by taking advantage of emerging P2P connection technologies such as Wi-Fi Direct and those enabled by LoPy4 from Pycom such as LoRa, BLE, Sigfox, Wi-Fi, Radio Wi-Fi and Bluetooth. Likewise, we have designed four algorithms to construct a group of devices, calculate their scores, select a group manager, and exchange inter- and intra-group messages. The experimental study we conducted shows that this model continues to perform efficiently, even in circumstances like the breakdown of wireless connectivity due to an extreme event or congestion from connecting a huge number of devices.

Embedded YOLO: A Real-Time Object Detector for Small Intelligent Trajectory Cars

YOLO-Tiny is a lightweight version of the object detection model based on the original “You only look once” (YOLO) model for simplifying network structure and reducing parameters, which makes it suitable for real-time applications. Although the YOLO-Tiny series, which includes YOLOv3-Tiny and YOLOv4-Tiny, can achieve real-time performance on a powerful GPU, it remains challenging to leverage this approach for real-time object detection on embedded computing devices, such as those in small intelligent trajectory cars. To obtain real-time and high-accuracy performance on these embedded devices, a novel object detection lightweight network called embedded YOLO is proposed in this paper. First, a new backbone network structure, ASU-SPP network, is proposed to enhance the effectiveness of low-level features. Then, we designed a simplified version of the neck network module PANet-Tiny that reduces computation complexity. Finally, in the detection head module, we use depthwise separable convolution to reduce the number of convolution stacks. In addition, the number of channels is reduced to 96 dimensions so that the module can attain the parallel acceleration of most inference frameworks. With its lightweight design, the proposed embedded YOLO model has only 3.53M parameters, and the average processing time can reach 155.1 frames per second, as verified by Baidu smart car target detection. At the same time, compared with YOLOv3-Tiny and YOLOv4-Tiny, the detection accuracy is 6% higher.