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.

Space-Air-Ground Integrated Network (SAGIN) has recently emerged as a viable solution for reliable transmission, high data rates, and seamless connectivity with extensive coverage. However, the characteristics of the computation and communication devices located at various levels of SAGIN make application placement within such environments a challenging task. Real-time service expectations and resource requirements of applications further intensify this issue, and push the domain to operate beyond its capacity, resulting in uneven delays and significant overhead. Taking these constraints into account, SAGIN’s application placement problem can be expressed as a multiobjective optimisation problem. This paper aims to solve such a problem using a Dynamic Weight-configurable Harris Hawks Optimisation (DW-HHO) algorithm, considering diverse application contexts such as deadlines, resource usage and the number of application activities. It simultaneously minimises application total service time and host resource overhead with a robust global search. The performance of the proposed solution is compared with benchmark metaheuristic solutions such as PSO, NSGA-II, Greedy and Random. Experimental results demonstrate that DW-HHO outperforms other benchmark metaheuristic solutions in optimising resource utilisation and service delivery time of applications in SAGIN environments. The proposed DW-HHO demonstrates notable improvements over existing methods. Specifically, when evaluating the total service time for PSO, NSGA-II, Greedy, and Random, DW-HHO outperforms these methods by 7.28%, 9.07%, 13.01%, and 14.97%, respectively.

A major difficulty in deploying new protocols is understanding how they will perform with respect to throughput and fairness in the wider Internet. Running repeatable experiments over complex scenarios is difficult, often resulting in protocol evaluation via simulation. However simulation does not take into account actual implementation idiosyncrasies. We present Multi-path-enabled Software-defined TEACUP (msTEACUP), an open-source testbed infrastructure that supports automated experiments over complex network topologies. msTEACUP supports multi-router (traditional and SDN) topologies and programmable path characteristics. This allows automation of experiments over real networks to better evaluate performance.

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.

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.

Low Extra Delay Background Transport (LEDBAT) is defined in RFC6817 as a congestion control algorithm for lower than best effort transport service that reacts to both delay and loss congestion signals. LEDBAT allows bulk transfer applications (such peer-to-peer file transfer and software updates) to utilize available capacity in the background while limiting additional forward queuing delays at network bottlenecks to 100ms. New Active Queue Management(AQM) schemes similarly aim for low latencies by dropping or marking packets when the bottleneck queuing delay exceeds thresholds much lower than 100ms. Due to renewed interest in deploying modern AQMs on home broadband services, we experimentally evaluate and characterize the impact of placing PIE, FQ-CoDel and FQ-PIE variants of AQM in the path of flows generated by libutp (a widely deployed UDP-based LEDBAT implementation). We uncover, and propose solutions to, some differences between libutp and RFC6817 that lead to poor utilization and incorrect inter-flow capacity sharing over AQM bottlenecks.

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.

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.

There exist many tools to passively monitor a link for traffic flows. They are typically used near the edge of the network, but not necessarily at the termination point of data flows - usually within a few hops of end-points. Round trip time (RTT) values for individual flows is of interest for network management purposes and can be used to indicate user experienced network delay, and in network design decisions. Determining the RTT when not at an end-point of a data flow is complicated by the fact that packets may be seen out of order and that witnessed packets may not reach their destination. In this paper we present an algorithm to estimate running RTT and jitter characteristics of TCP streams monitored at the midpoint of a TCP flow