Design and evaluation of a framework for cooperative and adaptive QoS control of DSRC network for road safety applications

Dedicated short-range communications (DSRC) are a promising vehicle communication technique for collaborative road safety applications (CSA). However, road safety applications require highly reliable and timely wireless communications, which present big challenges to DSRC based vehicle networks on effective and robust quality of services (QoS) provisioning due to the random channel access method applied in the DSRC technique. In this paper we examine the QoS control problem for CSA in the DSRC based vehicle networks and presented an overview of the research work towards the QoS control problem. After an analysis of the system application requirements and the DSRC vehicle network features, we propose a framework for cooperative and adaptive QoS control, which is believed to be a key for the success of DSRC on supporting effective collaborative road safety applications. A core design in the proposed QoS control framework is that network feedback and cross-layer design are employed to collaboratively achieve targeted QoS. A design example of cooperative and adaptive rate control scheme is implemented and evaluated, with objective of illustrating the key ideas in the framework. Simulation results demonstrate the effectiveness of proposed rate control schemes in providing highly available and reliable channel for emergency safety messages. © 2013 Wenyang Guan et al.

Performance evaluation of adaptive context aware rate selection algorithm (ACARS) for road safety applications in vehicular network

One of the major drawbacks for mobile nodes in wireless networks is power management. Our goal is to evaluate the performance power control scheme to be used to reduce network congestion, improve quality of service and collision avoidance in vehicular network and road safety application. Some of the importance of power control (PC) are improving spatial reuse, and increasing network capacity in mobile wireless communications. In this simulation we have evaluated the performance of existing rate algorithms compared with context Aware Rate selection algorithm (ACARS) and also seen the performance of ACARS and how it can be applied to road safety, improve network control and power management. Result shows that ACARS is able to minimize the total transmit power in the presence of propagation processes and mobility of vehicles, by adapting to the fast varying channels conditions with the Path loss exponent values that was used for that environment which is shown in the network simulation parameter. Our results have shown that ACARS is a very robust algorithm which performs very well with the effect of propagation processes that is prone to every transmitted signal in mobile networks. © 2013 IEEE.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.

Impact of denial of service solutions on network quality of service

The Internet has become a universal communication network tool. It has evolved from a platform that supports best-effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end-to-end QoS with recently proposed DoS/DDoS solutions. © 2010 John Wiley & Sons, Ltd.

Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.

Robust H.264/AVC video transmission using data partitioning and unequal loss protection

In this work, we present an adaptive unequal loss protection (ULP) scheme for H264/AVC video transmission over lossy networks. This scheme combines erasure coding, H.264/AVC error resilience techniques and importance measures in video coding. The unequal importance of the video packets is identified in the group of pictures (GOP) and the H.264/AVC data partitioning levels. The presented method can adaptively assign unequal amount of forward error correction (FEC) parity across the video packets according to the network conditions, such as the available network bandwidth, packet loss rate and average packet burst loss length. A near optimal algorithm is developed to deal with the FEC assignment for optimization. The simulation results show that our scheme can effectively utilize network resources such as bandwidth, while improving the quality of the video transmission. In addition, the proposed ULP strategy ensures graceful degradation of the received video quality as the packet loss rate increases. © 2010 IEEE.