Recent autonomous vehicles and Advanced Driver Assistance Systems (ADAS) are equipped with several sensing technologies, including cameras, LiDAR, radar, and ultrasonic. Due to its exceptional features, radar is increasingly utilized in a range of ADAS applications. Unfortunately, this increases the likelihood of radar-to-radar interference, which hinders radar functionality. Numerous research studies have investigated interference mitigation using various traditional signal processing or deep learning techniques. This paper presents a new technique utilizing the U-Net deep neural network (DNN) model for interference mitigation via semantic segmentation in such ADAS scenarios. By comparing the performance of the proposed model to previously published deep-learning-based approaches, our new model has demonstrated promising improvements based on standard evaluation criteria.

The process of identifying and authenticating Internet of Things (IoT) devices based on the electromagnetic characteristics of their wireless interfaces is a topic that has been receiving a lot of attention lately due to the continuous growth in the market of embedded and wearable wireless devices. Securing the networks to whom these devices are connecting daily has become a significant security challenge, which is threatening the security and safety of thousands, maybe millions of private and public networks due to the vulnerable nature of wireless devices to a well-known set of possible attacks.In this paper, we present the initial results acquired from our work-in-progress to develop new radio-features-extraction-based technique to identify wireless devices, specifically Internet of Things (IoT) ZigBee and LoRa devices. This paper summarizes our initial experimental setup to gather and analyze the devices signals to extract the desired features and describes the signal pre-processing approach and the machine learning methods that we attempted to date.

In this paper, we propose and evaluate a new communication-efficient design technique for solving the linear quadratic regulator (LQR) optimal control problem. We propose this design to minimize the communication overhead and resulting energy consumption in wireless distributed control scenarios in the context of cooperative vehicular networking (CVN). We introduce the LQR optimal control design problem, then we propose our new design technique using the alternating direction method of multipliers (ADMM). Our numerical results demonstrate that our communication-efficient control design can outperform the previous methods namely the sparsity-promoting optimal control design and the conventional LQR optimal control design. We also provide simulation results to evaluate the resilience of proposed technique under various wireless connectivity conditions.

Dynamic spectrum access (DSA) has emerged as an enabling technology to allow more intensive sharing of the radio spectrum. A requirement for most proposed DSA techniques is prior knowledge of the primary user's access pattern or the ability to predict primary user activities. Therefore, spectrum surveys are taking place on an even wider scale to provide data on spectrum usage and occupancy for developing new prediction models and for spectrum planning by regulators. This paper investigates the potential of mining spectrum data for correlation between human activities in a neighborhood and the resulting spectrum occupancy across different bands. We propose a systematic approach based on two clustering techniques: Gaussian mixture models (GMMs) and self-organizing map neural networks (SOMNNs). We mine spectrum measurements gathered by our network of spectrum observatories in Virginia and Illinois. The results confirm the existence of observable correlation and show that our proposed techniques detect correlation across various land mobile radio (LMR) and cellular bands under a wide range of scenarios with a high detection ratio. These results inspire us to develop more efficient prediction models for applications in opportunistic spectrum access (OSA) or self-organized networks.

This paper describes the Flexible Internetwork Stack (FINS) Framework, an open-source tool to enable implementation-based experimental research in computer networking. The FINS Framework uses a module-based architecture that allows cross-layer behavior and runtime reconfiguration of the protocol stack. The FINS Framework is general enough to enable experimental setups under various network architectures (e.g., MANET, infrastructure, mesh) and to accelerate prototyping solutions for evolving areas (e.g., cognitive networks, cross-layer design, context-aware applications). Version 1.0 of the framework makes use of existing physical and data link layer functionality, while enabling modifications to the stack at the network layer and above, or even the implementation of a clean-slate, non-layered protocol architecture. Protocols, stubs for communicating with intact layers, and management and supervisory functions are implemented as FINS Framework modules, interconnected by a central switch. This paper describes the FINS Framework architecture, presents an initial assessment along with experiments enabled by the tool, and documents an intuitive mechanism for transparently intercepting socket calls that maintains efficiency and flexibility. Performance testing shows that the FINS Framework is capable of supporting experiments requiring IEEE 802.11g hardware speeds and operating in varying networking architecture and experimental scenarios on both Ubuntu laptops and Android devices.

The volume of video streaming traffic over the Internet has increased dramatically over the last 10 years. By the end of 2014, it is expected that 77% of the US internet traffic will be video streaming [1]. In parallel, the last ten years have seen an incredible deployment of IEEE 802.11 technologies, also known as WiFi, at homes and public places. Although, the industry and research communities have given a lot attention to address the challenges of video streaming over wireless access networks (e.g., Cellular, WiMAX, WiFi), there are still interesting problems to address to enhance the user quality of experience (QoE). In this paper, we highlight the challenges that face adaptive video streaming over wireless access networks. Then, we propose a a heuristic cross-layer adaptation algorithm named E2E-MAC to improve the video client application response to changing conditions in WiFi environments. The algorithm, which runs on the client side, combines end to end (E2E) throughput measurements with MAC-based measurements from the WiFi link to choose the appropriate streaming quality-to request from the server. Simulation results have shown that our algorithm reduces the occurrence of buffer under-run, increases the average achieved streaming quality, and lowers the rate of lost video frames. This is achieved with a minor overhead on the client application's side.

Simulation is still the most widely adopted performance evaluation technique in mobile ad hoc network research, in spite of a growing number of questions about the fidelity of this technique. Implementation-based testing and evaluation of wireless networks tends to produce believable results, but the technique sometimes suffers from poor repeatability, high implementation cost, and complex experimental logistics. The topic of this paper is software tools to enable implementation-based experimental research on wireless networks. We review some of the existing tools and propose the Flexible Internetwork Stack (FINS) framework, our open-source solution for network protocol implementation, integration, and testing. FINS aims to provide researchers with monitoring, logging, and reconfiguring utilities similar to the ones provided by simulation environments or emulation testbeds.1

In this paper, we investigate stochastic multichannel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.