This paper presents a resilient framework for real-time, low-bitrate voice communication across heterogeneous wireless networks in emergency Search and Rescue (SAR) operations. The system supports IP-based voice transmission from high-bitrate Wi-Fi networks to constrained Long Range (LoRa™) and Direct Sequence Spread Spectrum (DSSS) links. A key innovation is an adaptive flow control mechanism that manages link asymmetry, achieving over 98% successful packet delivery. The design extends existing emergency satellite communication channels by enabling voice support without disrupting ongoing services, enhancing utilization of underused spectrum. Furthermore, the system is low-cost and rapidly deployable, offering a practical alternative to traditional solutions. Structured over-the-air and emulated testing demonstrate reliable sub-1 kbps voice transmission with consistent latency, jitter, and inter-arrival time across all links. These results highlight a viable upgrade path for legacy SAR systems operating in remote or infrastructure-degraded environments, as well as in areas where mainstream networks are rendered inoperative by disasters. • Novel IP protocol enables voice transmission over links below 1 kbps. • Codec 2 compresses voice to 700 bps while preserving intelligibility. • VoLB protocol supports voice across low-bitrate heterogeneous links. • Evaluated latency and jitter over Wi-Fi, CSS, and DSSS networks. • Achieves sub-0.8 s latency and jitter consistently below 0.05 s.

Wireless sensor networks (WSNs) have emerged as an increasingly important technology with wide application in fields such as industrial automation, transportation, smart cities, and similar areas. LoRa based LPWAN provides long range, robust, economical, and reliable communication with high link budget compared to other LPWAN technologies. However, LoRa based networks are usually deployed using a star topology (LoRaWAN). This type of network topology is not suitable for long range wireless communication and is also subject to single point of failure at the LoRaWAN gateway. In this study we evaluate the potential of LoRa as links in linear networks. We have addressed the potential applications, design considerations and current challenges in adaptation of LoRa technology for linear networks. This survey presents a detailed analysis of LoRa technology as a linear network and provides recommendations to researchers and practitioners to explore LoRa technology as a linear network technology for various IoT applications.

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted standard for delivering high Quality of Experience (QoE) for consumer video streaming applications. The progressive deployment of Active Queue Management (AQM) schemes - such as PIE and FQ-CoDel - at ISP bottlenecks or home gateways means that consumers' video streams are increasingly impacted by such AQM schemes. However, many existing approaches do not consider adjusting streaming strategies based on the bottleneck queue types. We have previously demonstrated the benefits of AQM schemes for DASH video streams, and proposed adaptive chunklets for an improved streaming performance. In this paper, we demonstrate the problems of queue-agnostic streaming and propose a queue-detection technique during DASH-like streaming. This entirely client-side and application-level technique is capable of detecting likely FIFO, PIE and FQ-CoDel AQM schemes at network bottlenecks.

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.

Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).

With Netflix and YouTube accounting for more than 50% of North American, fixed network peak download traffic in 2015, video streaming is a significant source of Internet traffic. Dynamic Adaptive Streaming over HTTP (DASH) is a recent standard for live and on-demand video streaming services, where clients adapt their behaviour on-the-fly to match regularly updated estimates of network capacity. Consumer DASH streams are likely to be bottlenecked by last-mile ISP links, and impacted by emerging active queue management (AQM) schemes being deployed to counter bufferbloat. We experimentally characterise and evaluate the impact of bottlenecks utilising PIE, FQ-PIE, CoDel and FQ-CoDel AQM schemes on DASH streams. We show that PIE's higher burst tolerance provides better streaming quality for single DASH stream over moderate to high RTT paths and when coupled with a FlowQueue scheduler's flow isolation capabilities, FQ-PIE protects DASH streams in the presence of cross-traffic.

The Border Gateway Protocol (BGP) is the default Internet routing protocol that manages connectivity among Autonomous Systems (ASes). Although BGP disruptions are rare, when they occur the consequences can be very damaging. Consequently there has been considerable effort aimed at understanding what is normal and abnormal BGP traffic and, in so doing, enable potentially disruptive anomalous traffic to be identified quickly. In this paper, we make two contributions. We show that over time BGP messages from BGP speakers have deterministic, recurrence and non-linear properties, then build on this insight to introduce the idea of using Recurrence Quantification Analysis (RQA) to detect BGP instability. RQA can be used to provide rapid identification of traffic anomalies that can lead to BGP instability. Furthermore, RQA is able to detect abnormal behaviours that may pass without observation.

Efficient management of supercomputing facilities requires estimates of future workload based on past user behaviour. For supercomputers with large numbers of users, aggregate user behaviour is commonly assumed to be best in prediction of future workloads, however for systems with smaller numbers of users the question arises as to whether it is still suitable or if benefits can be derived from monitoring individual user behaviour to predict future workload. We compare using individual user behaviour, aggregate user behaviour and a hybrid approach where we track heavy users individually and cluster aggregate light users into a small number of clusters. We find that the hybrid approach produces the best results in both mean absolute error and mean squared error. However, treating all users separately provides slightly worse predictions. We also introduce a new approach to prediction based on the hazard function which is a significant improvement on previously used schemes based on autoregressive models. The schemes are investigated numerically using a two-year workload trace from a supercomputer with a population of 136 users.

The Next Generation Mobile Network (NGMN) is expected to include integration of wireless technologies. In NGMN multimedia services are provided through the IP Multimedia Subsystem (IMS). Recently IMS has been enhanced to ensure session continuity when roaming among different wireless technologies. However achieving seamless mobility which is one of the goals of NGMN is still a challenge. In this paper we propose the PRIME mechanism which reduces handover delay and makes seamless mobility achievable. We evaluate the performance of the proposed method by simulating a WiMAX to LTE handover. The results show that in a typical handover the PRIME mechanism reduces the connection preparation delay by 70%.

In this paper we describe the construction and performance of classifiers able to identify Variable Rate VoIP traffic flows rapidly, reliably and independently of the application version that generated it. We show that features calculated on short sequences of packets extracted from the flow (sub-flows) are sufficient to identify VoIP flows with Recall of 99% and Precision of 90%. The features we used are based on mean packet length, autocorrelation and the ratio of data transmitted in either direction of a bi-directional flow. Even though the codecs we use to generate VoIP traffic are quite different, we show that by using selected features that capture the nature of variable bit rate voice traffic, a classifier trained on traffic generated by one version of VoIP can reliably recognize traffic generated by another version.

BitTorrent is one of the dominant traffic generating applications in the Internet. The ability to identify BitTorrent traffic in real-time could allow network operators to manage network traffic more effectively. In this paper we demonstrate that erroneous output of a Machine Learning based classifier is randomly distributed within a flow, allowing the application of temporal feedback to improve the overall classifier performance. We propose and evaluate a number of feedback algorithms. Our results show that we are able to improve classification outcomes (Recall by 2.4% and Precision by 0.1%) whilst both improving classification timeliness from three to two minutes, and improving robustness against future changes to the BitTorrent protocol.

Machine Learning (ML) for classifying IP traffic has relied on the analysis of statistics of full flows or their first few packets only. However, automated QoS management for interactive traffic flows requires quick and timely classification well before the flows finish. Also, interactive flows are often long-lived and should be continuously monitored during their lifetime. We propose to achieve this by using statistics derived from sub-flows—a small number of most recent packets taken at any point in a flow's lifetime. Then, the ML classifier must be trained on a set of sub-flows, and we investigate different sub-flow selection strategies. We also propose to augment training datasets so that classification accuracy is maintained even when a classifier mixes up client-to-server and server-to-client directions for applications exhibiting asymmetric traffic characteristics. We demonstrate the effectiveness of our approach with the Naive Bayes and C4.5 Decision Tree ML algorithms, for the identification of first-person-shooter online game and VoIP traffic. Our results show that we can classify both applications with up to 99% Precision and 95% Recall within less than 1 s. Stable results are achieved regardless of where within a flow the classifier captures the packets and the traffic direction.

Research on Voice over IP (VoIP) in infrastructure wireless local area networks (WLANs) has shown that, because of the access mechanism, the access point severely limits the number of voice calls a WLAN can support. To address this problem we propose a new scheme based on the IEEE 802.11e quality of service (QoS) mechanism where a tradeoff between codec quality and priority is exploited to improve the number of calls that can be supported. In particular, we propose certain priority settings at the access point (AP) to encourage users to switch to a lower quality codec during periods of high contention and thus enable them to maintain the call. We develop a detailed analytical model to show the benefit of this scheme. Our analytical results are validated by extensive simulation and show that the voice capacity in the network can be significantly improved. Furthermore, by using the ITU-T E-model to assess the voice quality, we show that users with a lower quality codec can still maintain an acceptable level of quality using the proposed scheme.

Geolocation of IP addresses is used for determining authenticity of webpages, delivering specific country or location related content and advertisements, or to add security for online transactions. Although IP geolocation databases exist, it is sometimes useful to validate their entries or create new, independent databases using independent sources of information. We propose and demonstrate a method whereby collecting and analyzing online game server discovery traffic over short periods of time can allow us to detect in which timezone a certain prefix or AS is located. Our method provides very good estimates of various AS timezones which we verify using publicly available IP geolocation databases.

BitTorrent is one of the dominant traffic generating applications in the Internet today. The ability to identify BitTorrent traffic in real-time could allow network operators to better manage network traffic and provide a better service to their customers. In this paper we analyse the statistical properties of BitTorrent traffic and select four features that can be used for real-time classification using Machine Learning techniques. We then train and test a classifier using the C4.5 algorithm. Our results show that based on statistics calculated on 150-packet sub-flows, we can classify BitTorrent traffic with Recall of 98.2% and Precision of 96.5%. We then show that 98.1% of sub-flows from other client-server bulk transfer applications are correctly classified as non-BitTorrent.

A key part of First Person Shooter network gaming is the game server discovery phase. Whilst probing for suitable servers from a wireless network, a large burst of network traffic is generated, potentially leading to detrimental effects on network capacity available to other wireless users. This can be minimised using an optimised algorithm to order the discovery probes and subsequently terminate the discovery process early. In this paper we explore further modifications to a previously proposed algorithm and examine its efficacy in further reducing the probe time/traffic during the server discovery phase. We show that it is possible to further reduce the overall discovery process duration by up to 13% while still presenting all suitable servers to the user for selection.

Seamless handoff between different wireless technologies is a vital requirement for the next generation mobile networks (NGMN). The soft handoff (SHO) method provides seamless roaming, but makes heavy use of battery and radio resources. In this paper, we propose a conservative soft handoff (cSHO) which maintains the benefits of SHO and reduces the consumption of resources. Instead of using both radio interfaces during the handoff period, the mobile station (MS) activates and deactivates its radio interfaces and switches between them to maintain the best possible radio connection. Since multimedia packets are already duplicated in the network and buffered in the base stations, fast switching between interfaces is possible. We investigate the performance of our proposed scheme by simulation and compare the performance of the cSHO scheme with the SHO and hard handoff (HHO) mechanisms.

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics - for example, in terms of ldquoaveragerdquo values - can be highly misleading. Many metrics, it turns out, are distributed like wealth - with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.

Modelling traffic generated by Internet-based multiplayer computer games has attracted much attention in the past few years. This has been driven by a need to simulate correctly the network impact of highly interactive online game genres such as the first person shooter (FPS). Packet size distributions and autocovariance models are important elements in the creation of realistic traffic generators for network simulators. In this paper we present techniques for creating representative models for N-player FPS games based on empirically measured traffic of 2-player games. The models capture the packet size distribution as well as the time series behaviour of game traffic. We illustrate the likely generality of our approach using data from seven FPS games that have been popular over the past nine years: Half-Life, Half-Life Counterstrike, Half-Life 2, Half-Life 2 Counterstrike, Quake 3 Arena, Quake 4 and Wolfenstein Enemy Territory.

We propose and evaluate a novel improvement to a previously published, unreliable covert channel based on the network traffic of multiplayer, first person shooter online games (FPSCC). Covert channels typically embed themselves within pre-existing (overt) data transmissions in order to carry hidden messages. FPSCC encodes covert bits as slight, yet continuous, variations of a player's character's movements. These variations are visually imperceptible to human players, yet occur frequently enough to create a low bit-rate covert channel. The nature of first person shooter network protocols means the original FPSCC channel is noisy (not reliable), experiencing a significant number of bit errors (including synchronisation errors). We have now augmented FPSCC to ensure bits are transmitted reliably. Evaluation of our technique with a prototype demonstrates throughput of up to 13 bits/second without any bit errors.

In this paper we present results of experimental work using machine learning techniques to rapidly identify Skype traffic. We show that Skype traffic can be identified by observing 5 seconds of a Skype traffic flow, with recall and precision better than 98%. We found the most effective features for classification were characteristic packet lengths less than 80 bytes, statistics of packet lengths greater than 80 bytes and inter-packet arrival times. Our classifiers do not rely on observing any particular part of a flow. We also report on the performance of classifiers built using combinations of two of these features and of each feature in isolation.

Modeling traffic generated by Internet based multiplayer computer games has attracted a great deal of attention in the past few years. In part this has been driven by a need to simulate the network impact of highly interactive online games such as the first person shooter (FPS). Packet size distributions and autocorrelation models are important elements in the creation of realistic traffic generators for network simulators. In this paper we present a simple technique for constructing Markov chains that model autocorrelated packet length for N-player FPS games based on traffic traces of of 2- player games. This enables us to synthesize the time sequence of the length of server to client traffic as well as its probability distribution. We illustrate the likely generality of our approach using data from seven FPS games that have been popular over the past nine years: half-life, half-life counterstrike, half-life 2, half-life 2 counterstrike, quake III arena, quake 4 and Wolfenstein enemy territory.

Covert channels aim to hide the existence of communication between two or more parties. Such channels typically utilise pre-existing (overt) data transmissions to carry hidden messages. Internet-based covert channels often encode new information into unused (or loosely specified) IP packet header fields, or the time intervals between IP packet arrivals. We propose a novel covert channel embedded within the traffic of multiplayer, first person shooter online games. We encode covert bits as slight, yet continuous, variations of a playerpsilas characterpsilas movements. Movement information is propagated to all clients attached to a given game server, yet the channel remains covert so long as the variations are visually imperceptible to the human players. A modified version of Quake III Arena is used to demonstrate our concept. We empirically analyse the covert channelpsilas bit rate, and compare the statistical characteristics of unmodified game traffic with those of game traffic carrying covert information.

Modeling traffic generated by Internet-based multiplayer computer games has attracted a great deal of attention in the past few years. In part this has been driven by a need to simulate correctly the network impact of highly interactive online game genres such as the first person shooter (FPS). Packet size distributions and autocovariance models are important elements in the creation of realistic traffic generators for network simulators. In this paper we present simple techniques for creating representative models for N-player FPS games based on empirically measured traffic of 2- and 3-player games. The models capture the packet size distribution as well as the time series behaviour of game traffic. We illustrate the likely generality of our approach using data from seven FPS games that have been popular over the past nine years: Half-Life, Half-Life Counterstrike, Half-Life 2, Half-Life 2 Counterstrike, Quake III Arena, Quake 4 and Wolfenstein Enemy Territory.

User mobility models are used in simulations of mobile communications systems to study characteristics of network performance. One of the models which is in common use is the Random Waypoint Model (RWP). The RWP is a simple mobility model based on random destinations, speeds and pause times. The RWP is often criticised as not representing how humans actually move. Paradoxically, validation against real mobility data is seen as being difficult due to the impracticalities of obtaining real mobility data.We give details of a real world user movement trace from which we obtained data about one individual's destinations, travel routes, average speed and rest times whilst moving throughout a city-wide area. We present results from this real life data and use it to validate some of the key characteristics of the RWP. In this paper we consider the RWP as a model of user mobility in networks that cater for a large geographical area - such as a city.

We report on the statistics of user behaviour obtained during a semester of student usage of our video-on-demand based multimedia system. The statistics show that interactive behaviour is adequately modelled by exponential distributions, but that a better match is obtained from lognormal distributions. From the statistics we develop a model of interactive behaviour that consists of a Markov chain of video interactions overlaid by a Poisson process generating other interactions. We show that the model performs quite well, and provided caution is exercised, Markov processes are adequate models of interactive behaviour.

Video-on-demand in education is used in a very different way to entertainment video. Usage is much more interactive. Viewers will frequently pause, fast-forward, skip and view frame-by-frame. Designing a network and server able to support interactivity and make efficient use of bandwidth is difficult. In this paper we suggest that in designing a system neither the network nor the server can be considered in isolation, but must be designed so as to take advantage of each others features. We present some preliminary data on interactive behaviour obtained during our video-on-demand trials and use it to illustrate the theoretical treatment of two different system designs.