Connected vehicles (CV) and automated vehicles (AV) are promising technologies for reducing road accidents and improving road efficiency. Significant advances have been achieved for AV and CV technologies, but they both have inherent shortcomings such line of sight sensing for AV. Connected autonomous vehicles (CAV) has been proposed to address the problems through sharing sensing and cooperative driving. While the focus of the research on CAV has been on the vehicles so far, cooperative and connected smart road infrastructure can play a critical role to enhance CAV and safe driving. In this paper we present an investigation of connected smart road infrastructure and AVs (CRAV). We discuss the potentials and challenges of CRAV, then propose a scalable simulation framework for the CRAV to facilitate fast, economic and quantitative study of CRAV. A case study of CRAV on smart road side unit (RSU) assisted vulnerable road users (VRU) collision warning is conducted, where the identification of VRU such as pedestrians on the road by the AVs is compared with and without RSU assistance. The impact of the location of RSUs on avoiding potential collisions is evaluated for vehicles with different sensor configurations. Preliminary simulation results show that with the support of smart RSUs, the CAVs could be notified of the existence of the VRUs on the road by the RSUs much earlier than they can detect with their own onboard sensors, and collisions with VRUs can be reduced. This study demonstrates the effectiveness of the proposed CRAV simulation framework and the great potentials of CRAV.

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper, we propose three enhancements for CNN-based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. The experimental results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.

Higher order sectorization (HOS), which splits macrocells into a larger number of smaller sectors, are receiving significant interest as a cost-effective means of improving network capacity. Potentially, the capacity gain with HOS is proportionally linear to the number of sectors per cell due to spatial reuse, but factors such as non-ideal antenna radiation patterns together with inter-cell interference can significantly reduce this capacity gain. We develop a statistical model to theoretically characterize the performance of HOS deployments in wireless networks using orthogonal frequency division multiple access. Moreover, a fractional frequency reuse scheme is considered, which aids to mitigate inter-cell interference. The model provides a fast and effective tool for studying network performance in terms of user signal quality, site throughput, and outage probability, and it can be used to speed up network planning and optimization. In addition, we consider the impact of user classification methods in the analysis, and propose a new spectrum efficiency-based user classification method that improves resource utilization and allocation fairness. Performance results indicate that the proposed model is accurate, and shows a diminishing performance gain of HOS deployments with the number of sectors. The proposed user classification method improves network performances with respect to the state-of-the-art approaches.

Machine to machine (M2M) communication has raised significant interests. However, due to the massive number of machine type communication (MTC) devices that are anticipated to communicate using cellular networks, there is a major problem on efficient accommodation of the heavy Random Access (RA) loads from the MTC devices. Use of small cells has been specified to provide network densification by 3GPP. In this paper we investigate the use of small cells to support RA and the allocation of Zadoff-Chu sequences to the small cells, which are used to generate preambles for the RA procedure. Small cells can be deployed on demand to handle mainly RA loads from MTC devices, which may generate much less data traffic compared to human devices. It is demonstrated that, with small cell support, more random channel access opportunities are provided and this can effectively support a massive number of machine devices. Using both simulations and analytical model the proposed implementation is evaluated and compared to two existing random access schemes without small cell support (the basic random access scheme and the access class barring (ACB) scheme). It is observed that the capacity of the networks in terms of the number of supported machine devices with small cell support can be increased significantly. The proposed implementation shows large potential to handle random channel access for massive machine devices.

IEEE 802.15.4 standard is a relatively new standard designed for low power low data rate wireless sensor networks (WSN), which has a wide range of applications, e.g., environment monitoring, e-health, home and industry automation. In this paper, we investigate the problems of hidden devices in coverage overlapped IEEE 802.15.4 WSNs, which is likely to arise when multiple 802.15.4 WSNs are deployed closely and independently. We consider a typical scenario of two 802.15.4 WSNs with partial coverage overlapping and propose a Markov-chain based analytical model to reveal the performance degradation due to the hidden devices from the coverage overlapping. Impacts of the hidden devices and network sleeping modes on saturated throughput and energy consumption are modeled. The analytic model is verified by simulations, which can provide the insights to network design and planning when multiple 802.15.4 WSNs are deployed closely.

Congestion control is critical for the provisioning of quality of services (QoS) over dedicated short range communications (DSRC) vehicle networks for road safety applications. In this paper we propose a congestion control method for DSRC vehicle networks at road intersection, with the aims of providing high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method a offline simulation based approach is used to find out the best possible configurations of message rate and MAC layer backoff exponent (BE) for a given number of vehicles equipped with DSRC radios. The identified best configurations are then used online by an roadside access point (AP) for system operation. Simulation results demonstrated that this adaptive method significantly outperforms the fixed control method under varying number of vehicles. The impact of estimation error on the number of vehicles in the network on system level performance is also investigated.

Dedicated Short Range Communication (DSRC) is a promising technique for vehicle ad-hoc network (VANET) and collaborative road safety applications. As road safety applications require strict quality of services (QoS) from the VANET, it is crucial for DSRC to provide timely and reliable communications to make safety applications successful. In this paper we propose two adaptive message rate control algorithms for low priority safety messages, in order to provide highly available channel for high priority emergency messages while improve channel utilization. In the algorithms each vehicle monitors channel loads and independently controls message rate by a modified additive increase and multiplicative decrease (AIMD) method. Simulation results demonstrated the effectiveness of the proposed rate control algorithms in adapting to dynamic traffic load.

Abstract Dedicated short range communications (DSRC) has been regarded as one of the most promising technologies to provide robust communications for large scale vehicle networks. It is designed to support both road safety and commercial applications. Road safety applications will require reliable and timely wireless communications. However, as the medium access control (MAC) layer of DSRC is based on the IEEE 802.11 distributed coordination function (DCF), it is well known that the random channel access based MAC cannot provide guaranteed quality of services (QoS). It is very important to understand the quantitative performance of DSRC, in order to make better decisions on its adoption, control, adaptation, and improvement. In this paper, we propose an analytic model to evaluate the DSRC‐based inter‐vehicle communication. We investigate the impacts of the channel access parameters associated with the different services including arbitration inter‐frame space (AIFS) and contention window (CW). Based on the proposed model, we analyze the successful message delivery ratio and channel service delay for broadcast messages. The proposed analytical model can provide a convenient tool to evaluate the inter‐vehicle safety applications and analyze the suitability of DSRC for road safety applications. Copyright © 2009 John Wiley & Sons, Ltd.

Link adaptation is a critical component of IEEE 802.11 systems, which adapts transmission rates to dynamic wireless channel conditions. In this paper we investigate a general cross-layer link adaptation algorithm which jointly considers the physical layer link quality and random channel access at the MAC layer. An analytic model is proposed for the link adaptation algorithm. The underlying wireless channel is modeled with a multiple state discrete time Markov chain. Compared with the pure link quality based link adaptation algorithm, the proposed cross-layer algorithm can achieve considerable performance gains of up to 20%.

In this paper a Markov chain based analytical model is proposed to evaluate the slotted CSMA/CA algorithm specified in the MAC layer of IEEE 802.15.4 standard. The analytical model consists of two two-dimensional Markov chains, used to model the state transition of an 802.15.4 device, during the periods of a transmission and between two consecutive frame transmissions, respectively. By introducing the two Markov chains a small number of Markov states are required and the scalability of the analytical model is improved. The analytical model is used to investigate the impact of the CSMA/CA parameters, the number of contending devices, and the data frame size on the network performance in terms of throughput and energy efficiency. It is shown by simulations that the proposed analytical model can accurately predict the performance of slotted CSMA/CA algorithm for uplink, downlink and bi-direction traffic, with both acknowledgement and non-acknowledgement modes.

Distributed source coding (DSC) has recently been considered as an efficient approach to data compression in wireless sensor networks (WSN). Using this coding method multiple sensor nodes compress their correlated observations without inter-node communications. Therefore energy and bandwidth can be efficiently saved. In this paper, we investigate a random-binning based DSC scheme for remote source estimation in WSN and its performance of estimated signal to distortion ratio (SDR). With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption of the DSC scheme. We further propose a novel energy-aware transmission protocol for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption, by adapting the source coding and transmission parameters to the network conditions. Simulations validate the energy efficiency of the proposed adaptive transmission protocol.