AI-driven data analysis methods have garnered attention in enhancing the performance of wireless networks. One such application is the prediction of downlink throughput in mobile cellular networks. Accurate throughput predictions have demonstrated significant application benefits, such as improving the quality of experience in adaptive video streaming. However, the high degree of variability in cellular link behaviour, coupled with device mobility and diverse traffic demands, presents a complex problem. Numerous published studies have explored the application of machine learning to address this problem, displaying potential when trained and evaluated with traffic traces collected from operational networks. The focus of this paper is an empirical investigation of machine learning-based throughput prediction that runs in real-time on a smartphone, and its evaluation with video streaming in a range of real-world cellular network settings. We report on a number of key challenges that arise when performing prediction “in the wild”, dealing with practical issues one encounters with online data (not traces) and the limitations of real smartphones. These include data sampling, distribution shift, and data labelling. We describe our current solutions to these issues and quantify their efficacy, drawing lessons that we believe will be valuable to network practitioners planning to use such methodologies in operational cellular networks.

Accurate prediction of cellular link performance represents a corner stone for many adaptive applications, such as video streaming. State-of-the-art solutions focus on distributed device-based methods relying on historic throughput and PHY metrics obtained through device APIs. In this paper, we study the impact of centralised solutions that integrate information collected from other network nodes. Specifically, we develop and compare machine learning inference engines for both distributed and centralised approaches to predict the LTE physical resource blocks using ns3-simulation. Our results illustrate that network load represents the most important feature in the centralised approaches resulting in halving the RB prediction error to 14% in comparison to 28 % for the distributed case.

To facilitate the adoption of 5G in industrial networks, 3GPP introduced a new 5G feature called 5G LAN-type service in Release 16. The 5G LAN-type service aims to support similar functionalities of Local Area Networks, but on top of the 5G network. As a result, 5G LAN-type service would be expected to offer UE-to-UE communication with ultra-low latency, which is attractive for localised industrial control settings. The requirements of this new service have been specified but its design and implementation are still being studied. In this demo paper, we present a workable design to implement the 5G LAN-type service and demonstrate its benefit in terms of end-to-end (E2E) latency. Our evaluation shows that E2E latency in a 5G LAN-type service is smaller than those in Multi-access Edge Computing scenarios. The testbed provides a platform for exploring challenging aspects of 5G LAN-service design and implementation.

As consumers switch to video-on-demand services, over the best effort Internet, the importance of service-level agreement enforcement schemes cannot be over emphasised. For these agreements to be effective, content providers must be able to enforce business policies in a simple and scalable manner, typically without access to the functionality within the core of the content delivery infrastructure. The option of relying on Media Presentation Description (MPD) attributes for video rate restriction is neither flexible nor effective. Hence, in this paper, we present a bio-inspired solution that exploits the inherent features of an HTTP-based adaptive streaming service to enable content providers guarantee service-level agreements. We utilise concepts from mathematical ecology that model species competing for a limited resource. In the proposed solution, distributed clients are assisted with global information using SDN. To enhance the scalability of the system, a business policy is enforced through parameter optimisation. To demonstrate the applicability of the proposed service, we built a test-bed and implemented a number of business policies. Evaluation results show that business policies are enforced in a fair and stable manner.

Software-Defined Networking (SDN) is used widely in Data Center Networks (DCNs) to facilitate the automated configuration of network devices required to provide cloud services and a multi-tenant environment. The resulting rate of change presents a challenge to a DCN operator who needs to be able to answer questions about the past state of the network. We describe our work in addressing this need, and how an ontological approach was taken to build a topological and temporal model of a DCN, which could then be populated using control-plane data captured in a message log. Sophisticated queries applied against the populated model allow the DCN operator to gain insight into the effects of historical automated configuration changes. We have tested our model for accuracy against a network from which a message log was captured, and we have demonstrated how queries have been formulated to retrieve useful information for the DCN operator.

As the next generation of cellular networks (5G) nears deployment, autonomous smart nodes, such as Internet of Things (IoT) and vehicular devices, are being developed to take advantage of increased throughput and improved resiliency offered by the new link layer protocols of 5G. Evaluating the impact of real-time 5G air-interface scheduling algorithms on the stringent real-time delivery needs of these devices and their new applications is no small feat. In this short paper, we expand upon the mmwave (5G) module for NS-3 and present DI5GUISE, a configurable and highly dynamic real-time simulated testbed framework, through which the impact of these smart nodes can be investigated. We utilise real-time video delivery as an example of applications with high throughput requirements. The experimental results illustrate that even on low cost commodity hardware, such as Raspberry Pis, over 80Mbps per client of real-time streaming of 4K content can be achieved.

Evolved Multimedia Broadcast Multicast Service (eMBMS) is a 3GPP standard that improves the utilization of scarce wireless resources and the quality of the received content. eMBMS uses a Single Frequency Network (SFN) to transmit real-time videos over synchronized resources across neighboring base stations (eNBs) and allows users to share wireless spectrum across multiple cell sites. However the user with the worst channel condition and the eNB with the least available resources limit the throughput of a session. To overcome such limitations, the SFN can be divided into non-overlapping clusters of eNBs and in each cluster users can be split into groups. We formulate an optimization problem that maximizes an operator-defined utility for multiple eMBMS sessions served at multiple bitrates by choosing the optimal set of SFN clusters and user groups for each session. We propose an algorithm, RTOP, that finds the optimal or a near-optimal solution in real-time regardless of the number of eMBMS users. Our extensive simulations indicate that, in comparison to state-of-the-art schemes, RTOP improves the system utility and average user bitrate by up to 14% and 90% respectively. Additionally, we show that the utility of RTOP always stays within a 1% gap from the optimal solution.

To find missing people in a remote area, we propose an autonomous unmanned aerial vehicle (UAV) approach which attempts to locate the target by detecting and localising the radio signals produced by a GSM cell phone. By using a low- weight software defined radio and companion computer, the UAV can act as a GSM base station and induce the missing person's device to attempt to make contact. Through the signal strength values and known UAV location, a series of these contact attempts can be used to quickly and accurately localise their position. As the area in which the missing person might be located may be quite large, and the interaction of radio signals with terrain is potentially complex, an efficient search strategy for exploring the area is required in order to reduce time taken to make contact. We make use of a constraint-based graph-based path planning approach to produce a route for the UAV to traverse in the air passing through expected signals from a large number of possible source locations, and demonstrate through experiments the timely identification and localisation of the cell phone.

Millimeter wave (mmWave) bands are considered highly for localization and object detection. In this paper we assess the potential of commercial IEEE 802.11ad mmWave equipment to offer accurate object detection, ultimately providing models of the physical environment. Unlike solutions using bespoke mmWave equipment for detection, the use of IEEE 802.11ad ensures a low-cost system, and one in which detection can be integrated with communication, creating potential for innovative applications. Our approach is to build a laboratory testbed in which we capture reflected mmWave signals that are generated and transmitted by a commercial off-the-shelf (COTS) IEEE 802.11ad mmWave device. From the measured channel impulse response, we measured the distance from the mmWave transceiver to the objects in the environment, by some simple signal processing techniques. By knowing the angle of mmWave departure/arrival and this measured distance, we can develop a 2D model of the physical environment. We report on the achieved accuracy, which is 2cm in most experiments, and discuss technology limitations and research opportunities.

Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users' demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P. 1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms.

In multi-radio multi-channel wireless mesh networks, energy saving mechanisms try to save energy by putting radios into sleep mode. The decision to switch energy states of radios is taken based on parameters like remaining energy or traffic requests at nodes. In IEEE 802.11 power saving mode (PSM), nodes turn off the radios whenever there is no traffic to receive, send or forward. Nodes wake up radios periodically to check if there is any new traffic demand. Due to waking up radios redundantly and a requirement of tight synchronization PSM misses opportunities to save energy in the multi radio scenario. We propose an advanced energy saving method (AESM), where each node makes an independent decision on switching radios states while satisfying QoS requirements for different types of traffic flows. Experimental evaluation shows that AESM reduces energy consumption by 20% over PSM, while also reducing delay and packet loss to maintain QoS for network performance.

In this paper we introduce a client emulator for experimenting with DASH video. dashc is a standalone, compact, easy-to-build and easy-to-use command line software tool. The design and implementation of dashc were motivated by the pressing need to conduct network experiments with large numbers of video clients. The highly scalable dashc has low CPU and memory usage. dashc collects necessary statistics about video delivery performance in a convenient format, facilitating thorough post hoc analysis. The code of dashc is modular and new video adaptation algorithm can easily be added. We compare dashc to a state-of-the art client and demonstrate its efficacy for large-scale experiments using the Mininet virtual network.

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error. We find that by applying accurate prediction to three algorithms, user QoE can improve up to 55% depending on the algorithm in use. Furthermore having longer horizon positively affects QoE metrics. Accurate predictions have the most significant impact on stall performance by completely eliminating them. Prediction also improves switching behaviour significantly and longer prediction horizons enable a client to promptly reduce quality and avoid stalls when the throughput drops for a relatively long time that can deplete the buffer. For all algorithms, a 3-chunk horizon strikes the best balance between different QoE metrics and, as a result, achieving highest user QoE. While error-induced predictions significantly lower user QoE in certain situations, on average, they provide 15% improvement over DASH algorithms without any prediction.

In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a wellknown non-rooted Android network monitoring application, GNetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks. To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.

RPL is a standard routing framework for low-power and lossy networks (LLNs). LLNs usually operate in challenged conditions, therefore RPL can be adapted to satisfy requirements of a particular LLN. RPL facilitates this through objective functions (OFs). An OF is used to discover and maintain data forwarding paths based on the requirements of LLNs. In RPL, different OFs can use different routing metrics in different ways. In this paper, we design different OFs and analyse their impact on RPL performance in multi-gateway ad-hoc LLNs. In conjunction with the shortest hop-count, our designed OFs also use the following tie-breaking metrics: available bandwidth, delay, buffer occupancy, and ETX. Our OFs use the tie-breaking metrics on a greedy or an end-to-end basis. In our experimental analysis, we consider the impact of duty-cycling, number of gateways, and data traffic load on the OFs’ performance. Our results demonstrate that, generally speaking, the performance improves with an increase in the number of gateways. In the absence of duty-cycling, the greedy approach is better compared to the end-to-end approach, and using delay, buffer occupancy, and ETX metrics as the tie-breaking metrics in conjunction with the shortest hop-count metric yield the best performance. In a relatively high data traffic load, all OFs perform similarly. In duty-cycling mode, frequent changes in the parent node incur extra synchronization time between a sender and receiver. OFs that use the tie-breaking metrics on an end-to-end basis do not frequently change parent nodes, hence they demonstrate better performance. Furthermore, in duty-cycling mode, the shortest hop-count metric demonstrates the best performance compared to the other metrics.

In dense wireless deployments, such as Enterprise WLANs (EWLANs) and home WLANs, interference may occur because of neighbouring WLANs sharing the same unlicensed spectrum. Mechanisms to centrally manage WLAN deployments cannot effectively mitigate the interference caused by hidden terminals (HTs) in WLANs that belong to different organisations. Furthermore, the impact of interference is amplified if it is combined with long-lived TCP traffic flows, which are becoming increasingly commonplace. In this paper, we focus on mitigating the impact of HTs on long-lived TCP flows in home WLANs. In particular, we study the effect of five key factors on long-lived TCP flows under the impact of HTs: packet bursting, backoff mechanisms, maximum number of RTS attempts, capture affect and the number of associated clients with the same Access Point (AP). Extensive simulation results show that a combination between RTS/CTS messages and bursting increases the throughput up to 8× in the presence of HTs. Therefore, we develop a mechanism called joint RTS/CTS with Bursting (RCBurst) that leverages RTS/CTS messages and packet bursting to mitigate the impact of HTs. The simulation results show that RCBurst achieves an improvement of up to 0.3 in Jain's fairness index over the conventional CSMA/CA, without reducing the overall throughput.

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.

Typically, in machine-type communications (MTC) devices communicate with servers over the Internet. In a large-scale machine-to-machine area (M2M) network, the devices may not connect directly to the Internet due to radio transmission and energy limitations. Therefore, the devices collaborate wirelessly to relay their data to a gateway. A large-scale M2M area network may have multiple gateways, selecting a proper gateway for the devices can have immense impact on the network's performance. We present the channel capacity and contention-based joint routing and gateway selection methods for MTC. Based on channel capacity and contention, our methods select the best gateway on per-packet, per-flow, and per-node basis. We compare the methods' performance with existing methods using simulation and test-bed experiments. We analyse the impact of the number of gateways, physical distribution of transmitters, control overhead, and duty-cycling on the performance of the gateway selection methods. Our results demonstrate that, in duty-cycled operations, the methods' performance depends on control overhead and making a good trade-off between load imbalance to different gateways and a forwarding path's length. Otherwise only the latter impacts the methods' performance. In general, our node-based best gateway selection method makes a better trade-off and exhibits lower control overhead, hence it demonstrates better performance. Moreover, our methods demonstrate better performance as compared to an existing state-of-the-art joint routing and gateway selection method.

Wireless mesh networks (WMNs) connect to the Internet via access gateways. This paper studies multi-source video multicast in Internet-connected WMNs. The focus is on the design of a shareable integrated multicast that allows the multicasts of video sources to employ common Internet shortcuts or WMN paths to avoid potentially high WMN overheads and excessive Internet usage. Several algorithms are described that together form a video multicast framework running a controlled number of shareable multicasts under the constraint of Internet availability. These algorithms are the resource-efficient source group algorithm, the efficient integrated architecture algorithm, and the interference-controlled multicasting tree algorithm. These algorithms represent different approaches to overcoming various costs arising from multi-source video multicast, enabling multiple video sources to distribute delay, and throughput-guaranteed videos to receivers across large-scale areas. Simulation results are presented that quantify the performance gains that can be achieved.

In this article, we describe a neighbour disjoint multipath (NDM) scheme that is shown to be more resilient amidst node or link failures compared to the two well-known node disjoint and edge disjoint multipath techniques. A centralised NDM was first conceptualised in our initial published work utilising the spatial diversity among multiple paths to ensure robustness against localised poor channel quality or node failures. Here, we further introduce a distributed version of our NDM algorithm adapting to the low-power and lossy network (LLN) characteristics. We implement our distributed NDM algorithm in Contiki OS on top of LOADng—a lightweight On-demand Ad hoc Distance Vector Routing protocol. We compare this implementation's performance with a standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), and also with basic LOADng, running in the Cooja simulator. Standard performance metrics such as packet delivery ratio, end-to-end latency, overhead and average routing table size are identified for the comparison. The results and observations are provided considering a few different application traffic patterns, which serve to quantify the improvements in robustness arising from NDM. The results are confirmed by experiments using a public sensor network testbed with over 100 nodes.