Mike Dixon

The constant increase in users’ bandwidth needs, through a large variety of multimedia applications, creates the need for highly effective network traffic control. This need is imperative in wireless networks, where the available bandwidth is limited, but is very important for wired networks as well. In this work we focus on the problem of policing video traffic from sources encoded with H.264 and H.265, given that these are the major state-of-the-art standards currently in the market. Building on work that has shown that classic traffic policing schemes can lead to unnecessarily strict policing for conforming video sources, we propose the use of Artificial Intelligence (AI) – based traffic policing schemes for video traffic. We conduct a performance evaluation of several AI – based schemes with the classic token bucket and we show that our proposed Frame Size Predictor and Policer scheme improve the performance of the classic token bucket by around 90% for conforming users, while providing only slightly worse policing results for non-conforming users.

As video has become the dominant type of traffic over wired and wireless networks, the efficient transmission of video streams is of paramount importance. Hence, especially for wireless networks, the optimum utilization of the available bandwidth while preserving the users’ Quality of Service and Quality of Experience requirements is crucial. Towards this goal, the accurate prediction of upcoming video frame sizes can play a significant role. This work focuses on achieving such an accurate prediction for videos encoded with H.264 and H.265, which are the major state-of-the-art standards based on their current market share. Unlike previous studies, we use single-step and multi-step approaches to capture the long-range dependence and short-range dependence properties of variable bit rate video traces through neural networks-based modeling. We evaluate the accuracy of Long Short Term Memory, Convolutional Neural Networks and Sequence-to-Sequence models and compare them with existing approaches. Our models show significantly higher accuracy for a variety of videos. We also provide a case study on how our model can be used for traffic policing purposes.

Global communications without reliance on an engineered communications network make the ionosphere an attractive medium for wireless sensors in remote deployments. However, ionospheric circuits’ temporary availability is a challenge in scheduling transmissions for a sensor with limited power, communications and computational capacity, particularly where cost and antenna constraints limit operation to a single frequency. We describe a technique for scheduling transmissions based on precomputed propagation models. The models predict the time-varying Signal to Noise Ratio (SNR) at the receiver. We describe methods to determine threshold SNR values, using the Weak Signal Propagation Reporter (WSPR) database to determine if a time slot is suitable for transmission.Two techniques are investigated to quantify the failed receptions: the Inverse Square Law method uses a statistical approach and a sampling measurement technique called Goldilocks. The two approaches yielded threshold SNR values of −21 dB and −19 dB, respectively, for a time slot with a 90% successful reception goal. Applying these thresholds to the modelled SNR, we generate a precomputed hourly transmission schedule. With the schedule determined monthly, a 12-month plan requires 36 bytes of wireless sensor storage. A six-day experiment, using a 1677 km path, found that the schedule resulted in an 83% reception rate when used with a power level of 200 mW.

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

Passwords are the most widely used method of authentication on the Internet, but users find compliance with password guidelines difficult, and we know little about the long-term effects of attempts to improve compliance. In this paper, we extend the work of fear appeals use in the IS security domain to investigate their longer-term effects. We conducted a longitudinal experimental study to examine fear appeals’ long- and short-term effects. Using a model based on protection motivation theory (Rogers, 1983), we found that fear of threat, perceived password effectiveness, and password self-efficacy predicted compliance. We also found that neither perceived vulnerability to a security attack nor perceived severity of an attack influenced compliance. Providing persuasive communication improved compliance with password guidelines and resulted in significantly stronger passwords, but the effects on compliance intentions were only short term. This study extends our understanding of the factors that influence compliance with password guidelines and how we can modify them to improve compliance. We raise interesting questions about the role of fear in different IS security contexts. We also highlight the need for more research on the long-term impact of persuasive communication.

Information technology has been identified as a vital means for supporting construction project processes, yet the level of adoption in the construction industry has been low relative to other sectors. Mobile Information and communications technology (mICT) allows people to access information from wherever they are, and as work in the construction industry is mainly fieldwork, with workers being highly mobile, mICT holds promise for the sector, particularly in developing countries. The aim of the study reported in this paper was to investigate factors that could impact stakeholders’ adoption of mICT in the Libyan construction industry. A model of mICT adoption was developed, and tested using data collected from a survey of 202 construction industry stakeholders from 15 companies in Libya. The analysis was undertaken using structural equation modelling. It was found that perceived usefulness and ease of use are important in determining intention to adopt mICT, and that they are influenced by self - efficacy and facilitating conditions. The cost of technology was not found to be a barrier to adoption. Recommendations are made to the construction industry in Libya and relevant government authorities, in order to help improve awareness of the potential of mICT and to help improve potential users’ self-efficacy.

This paper presents the Weighted Random Early Detection (WTRED) strategy for congestion handling in TCP networks. WTRED provides an adjustable weight parameter to increase the sensitivity of the average queue size in RED gateways to the changes in the actual queue size. This modification, over the original RED proposal, helps gateways minimize the mismatch between average and actual queue sizes in router buffers. WTRED is compared with RED and FRED strategies using the NS-2 simulator. The results suggest that WTRED outperforms RED and FRED. Network performance has been measured using throughput, link utilization, packet loss and delay.

Smart Space (SS) communications has rapidly emerged as an exciting new paradigm that includes ubiquitous, grid, and pervasive computing to provide intelligence, insight, and vision for the emerging world of intelligent environments, products, services and human interaction. Dependable networking of a smart space environment can be ensured through reliable routing, efficient selection of error free links, rapid recovery from broken links and the avoidance of congested gateways. Since link failure and packet loss are inevitable in smart space wireless sensor networks, we have developed an efficient scheme to achieve a reliable data collection for smart spaces composed of low capacity wireless sensor nodes. Wireless Sensor Networks (WSNs) must tolerate a certain lack of reliability without a significant effect on packet delivery performance, data aggregation accuracy or energy consumption. In this paper we present an effective hybrid scheme that adaptively reduces control traffic with a metric that measures the reception success ratio of representative data packets. Based on this approach, our proposed routing scheme can achieve reduced energy consumption while ensuring minimal packet loss in environments featuring high link failure rates. The performance of our proposed routing scheme is experimentally investigated using both simulations and a test bed of TelosB motes. It is shown to be more robust and energy efficient than the network layer provided by TinyOS2.x. Our results show that the scheme is able to maintain better than 95% connectivity in an interference-prone medium while achieving a 35% energy saving.

This work presents the Risk Threshold RED (RTRED) congestion control strategy for TCP networks. In addition to the maximum and minimum thresholds in existing RED-based strategies, we add a third dropping level. This new dropping level is the risk threshold which works with the actual and average queue sizes to detect the immediate congestion in gateways. Congestion reaction by RTRED is on time. The reaction to congestion is neither too early, to avoid unfair packet losses, nor too late to avoid packet dropping from time-outs. We compared our novel strategy with RED and ARED strategies for TCP congestion handling using a NS-2 simulation script. We found that the RTRED strategy outperformed RED and ARED.

The performance of the current and the next generation server applications such as ECommerce, Storage and Web server that employ TCP/IP and UDP/IP as the communication protocol of choice depends upon the efficiency of the Protocol Stack Processing within this node. As the speed of networks exceeds one GBPS, the design and implementation of high-performance Network Interfaces (NI) for servers become very challenging. It is observed that using programmable NI with a general purpose processing core to offload some of the TCP/IP or UDP/IP protocol functions can deliver some important features which include scalability, short development times and reduced costs. In this paper, we proposes a new NI-programmable based model that support the Large Segment Offload (LSO) for sending side and a novel technique called Receiving Side Amalgamating (RSA) for receiving side and which is used for incoming packets. The core engine assigned to handle these functions is single specialized embedded processors utilizing RISC cores in each side. As a result, a 240 MHz RISC core can be used in Ethernet Network Interface ENI card for wide range of transmission line speed up to 100 Gbps. These results are based on the use of a specialized RISC core that we developed and simulated. Also, the author has discussed some of the design issues that are related to RISC core based NI and the data movement type.