From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Opportunistic Spectrum Utilization for Vehicular Communication Networks

Recently, vehicular networks (VANETs), has become the key technology of the next-generation intelligent transportation systems (ITS). By incorporating wireless communication and networking capabilities into automobiles, information can be efficiently and reliably disseminated among vehicles, road side units, and infrastructure, which enables a number of novel applications enhancing the road safety and providing the drivers/passengers with an information-rich environment. With the development of mobile Internet, people want to enjoy the Internet access in vehicles just as anywhere else. This fact, along with the soaring number of connected vehicles and the emerging data-craving applications and services, has led to a problem of spectrum scarcity, as the current spectrum bands for VANETs are difficult to accommodate the increasing mobile data demands. In this thesis, we aim to solve this problem by utilizing extra spectrum bands, which are not originally allocated for vehicular communications. In this case, the spectrum usage is based on an opportunistic manner, where the spectrum is not available if the primary system is active, or the vehicle is outside the service coverage due to the high mobility. We will analyze the features of such opportunistic spectrum, and design efficient protocols to utilize the spectrum for VANETs. Firstly, the application of cognitive radio technologies in VANETs, termed CR-VANETs, is proposed and analyzed. In CR-VANETs, the channel availability is severely affected by the street patterns and the mobility features of vehicles. Therefore, we theoretically analyze the channel availability in urban scenario, and obtain its statistics. Based on the knowledge of channel availability, an efficient channel access scheme for CR-VANETs is then designed and evaluated. Secondly, using WiFi to deliver mobile data, named WiFi offloading, is employed to deliver the mobile data on the road, in order to relieve the burden of the cellular networks, and provide vehicular users with a cost-effective data pipe. Using queueing theory, we analyze the offloading performance with respect to the vehicle mobility model and the users' QoS preferences. Thirdly, we employ device-to-device (D2D) communications in VANETs to further improve the spectrum efficiency. In a vehicular D2D (V-D2D) underlaying cellular network, proximate vehicles can directly communicate with each other with a relatively small transmit power, rather than traversing the base station. Therefore, many current transmissions can co-exist on one spectrum resource block. By utilizing the spatial diversity, the spectrum utilization is greatly enhanced. We study the performance of the V-D2D underlaying cellular network, considering the vehicle mobility and the street pattern. We also investigate the impact of the preference of D2D/cellular mode on the interference and network throughput, and obtain the theoretical results. In summary, the analysis and schemes developed in this thesis are useful to understand the future VANETs with heterogeneous access technologies, and provide important guidelines for designing and deploying such networks

Electrical and Computer Engineering Research Report 2008

Department Research New Chair Publications Enterprisehttps://digitalcommons.mtu.edu/ece-annualreports/1005/thumbnail.jp

Distributed Adaptation Techniques for Connected Vehicles

In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero

A Cognitive Radio Spectrum Sensing Implementation Based on Deep Learning and Real Signals

In a cognitive radio environment, spectrum sensing is an essential phase for improving spectrum resources management. Based on a deep learning method and real signals, a new spectrum sensing implementation is proposed in this work. The real signals are artificially generated, using an ARDUINO UNO card and a 433 MHz wireless transmitter, in ASK and FSK modulation types. The reception interface is constructed using an RTL-SDR receiver connected to MATLAB software. The signals classification is carried out by a convolutional neural network (CNN) classifier. Our proposed model’s main objective is to identify the spectrum state (free or occupied) by classifying the received signals into a licensed user (primary user) signals or noise signals. Our proposed model’s performance evaluation is evaluated by two metrics: the probability of detection (Pd) and the false alarm probability (PFA). Finally, the proposed sensing method is compared with other used techniques for signal classification, such as energy detection, artificial neural network, and support vector machine. The experimental results show that CNN could classify the real signals better than traditional methods and machine learning methods

On Employing a Savitzky-Golay Filtering Stage to Improve Performance of Spectrum Sensing in CR Applications Concerning VDSA Approach

Abstract In this paper, a filtering stage based on employing a Savitzky-Golay (SG) filter is proposed to be used in the spectrum sensing phase of a Cognitive Radio (CR) communication paradigm for Vehicular Dynamic Spectrum Access (VDSA). It is used to smooth the acquired spectra, which constitute the input for a spectrum sensing algorithm. The sensing phase is necessary, since VDSA is based on an opportunistic approach to the spectral resource, and the opportunities are represented by the user-free spectrum zones, to be detected through the sensing phase. Each filter typology presents peculiarities in terms of its computational cost, de-noising ability and signal shape reconstruction. The SG filtering properties are compared with those of the linear Moving Average (MA) filter, widely used in the CR framework. Important improvements are proposed