Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce feature-level data fusion to connected autonomous vehicles for the purpose of enhancing object detection and making realtime edge computing on inter-vehicle data feasible for autonomous vehicles.

Because of the proliferation of wireless technologies, jamming in wireless networks has become a major research problem due to the ease in blocking communication in wireless networks. Jamming attacks are a subset of denial of service (DoS) attacks in which malicious nodes block legitimate communication by causing intentional interference in networks. To better understand this problem, we need to discuss and analyze, in detail, various techniques for jamming and anti-jamming in wireless networks. There are two main aspects of jamming techniques in wireless ad hoc networks: types of jammers and placement of jammers for effective jamming. To address jamming problem, various jamming localization, detection and countermeasure mechanisms are studied. Finally, we describe the open issues in this field, such as energy efficient detection scheme and jammer classification.

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Multi-hop vehicle-to-vehicle communication is useful for supporting many vehicular applications that provide drivers with safety and convenience. Developing multi-hop communication in vehicular ad hoc networks (VANET) is a challenging problem due to the rapidly changing topology and frequent network disconnections, which cause failure or inefficiency in traditional ad hoc routing protocols. We propose an adaptive connectivity aware routing (ACAR) protocol that addresses these problems by adaptively selecting an optimal route with the best network transmission quality based on statistical and real-time density data that are gathered through an on-the-fly density collection process. The protocol consists of two parts: 1) select an optimal route, consisting of road segments, with the best estimated transmission quality, and 2) in each road segment of the chosen route, select the most efficient multi-hop path that will improve the delivery ratio and throughput. The optimal route is selected using our transmission quality model that takes into account vehicle densities and traffic light periods to estimate the probability of network connectivity and data delivery ratio for transmitting packets. Our simulation results show that the proposed ACAR protocol outperforms existing VANET routing protocols in terms of data delivery ratio, throughput and data packet delay. Since the proposed model is not constrained by network densities, the ACAR protocol is suitable for both daytime and nighttime city VANET scenarios.