Precise clock synchronization underpins deterministic operation in wireless systems spanning industrial automation, vehicular networks, distributed extended reality (XR), smart infrastructure, and wide-area precision agriculture. Wireless links introduce variable propagation delays, channel asymmetry, interference, clock drift, and scalability constraints that make sub-microsecond alignment difficult. This article provides a comprehensive survey and tutorial on wireless clock synchronization. We introduce a five-dimension taxonomy covering: system architecture, synchronization mechanism, correction strategy, delay and uncertainty modeling, and resource and deployment class, and apply it to eight canonical protocol families and to synchronization as realized across IEEE 802.15.4, ZigBee, Bluetooth low energy (BLE), LoRa, Wi-Fi, Ultrawide band (UWB), and 4G/5G/6G systems. We examine solutions across five application domains: industrial automation and Industrial Internet of Things (IIoT), vehicular V2X, distributed XR and metaverse, infrastructure monitoring, and wide-area Internet of Things (IoT) and precision agriculture; alongside tools, testbeds, and datasets supporting evaluation. Open challenges addressed include scalability and mobility, ultralow-jitter determinism, robust clock parameter estimation under non-Gaussian delay distributions, distributed and consensus-based synchronization for infrastructure-free networks, secure and resilient synchronization against wireless-specific threats, and cross-domain convergence encompassing time-sensitive networking (TSN)–5G/6G interoperability and joint communication, sensing, and timing as an emerging 6G design paradigm. Together, these contributions provide the first unified cross-technology framework connecting fundamentals, protocol families, application domains, and open research challenges in wireless clock synchronization.

In the context of large-scale data collection like in the Internet of Things, Data Confidence Fabrics are expected to play an essential role in verifying and authenticating sensor data. To this end, metadata is generated at each network node and stored on the blockchain. However, storing metadata on the blockchain introduces significant privacy risks, as it can be exploited to reveal sensitive information, such as network structures and communication paths. This paper addresses these challenges by proposing two novel schemes to protect network structures: Hostname Mapping and Hostname Encryption . Our work demonstrates that the Hostname Mapping approach effectively conceals network patterns but introduces inefficiencies due to additional table storage and computational overhead. In contrast, the Hostname Encryption method eliminates the need for additional table management, offering a more efficient and secure alternative. Despite these advancements, the timestamp field in metadata could still allow attackers to infer patterns using machine learning, highlighting the need for further research to fully secure metadata. By combining encryption with some enhancements to timestamp obfuscation, our approach lays the foundation for additional privacy protection against metadata exploitation.

Open Radio Access Networks (O-RAN) enhance RAN flexibility, interoperability, and intelligence by introducing open interfaces, disaggregation of RAN components, and data-driven control. This flexibility has the potential to significantly reduce capital and operating expenses (CAPEX/OPEX) by en-hancing efficiencies in network energy consumption. Advanced Sleep Modes (ASMs) are an advantageous energy-saving technique for 5G base stations (BS), with a set of sleep depths that progressively deactivate different RAN components. The deploy-ment and effectiveness of ASMs in O-RAN raises a number of challenges that have yet to be investigated. This paper surveys and critically reviews RAN ASMs, as well as other energy-enhancing techniques applied in O-RAN. It presents the first granular, per-component O-RAN Radio Unit (O-RU) power model, that will enable researchers to quantify the power savings gained from deploying ASMs. A calculation of the energy savings achieved at each ASM level per O-RU component for different 3GPP functional splits is provided. The power consumption of O-RU sub-components for functional splits 6, 7.2x, and 8 is shown. The default 3GPP functional split 7.2x as adopted by O-RAN shows that substantial power savings can be achieved by applying ASMs to the O-RU, achieving a minimum of 80% savings depending on the choice of SM as compared to power consumption at full load. Finally, a discussion of the open research challenges and opportunities in deploying ASMs in O-RAN is provided.

Time-Sensitive Networking (TSN) is increasingly adopted in industrial systems to meet strict latency, jitter, and reliability requirements. However, evaluating TSN’s fault tolerance under realistic failure conditions remains challenging. This paper presents IN2C, a modular OMNeT++/INET-based simulation framework that models two synchronized production cells connected to centralized infrastructure. IN2C integrates core TSN features—including time synchronization, traffic shaping, per-stream filtering, and Frame Replication and Elimination for Redundancy (FRER)—alongside XML-driven fault injection for link and node failures. Four fault scenarios are evaluated to compare TSN performance with and without redundancy. Results show that FRER eliminates packet loss and achieves sub-millisecond recovery, though with 2–3× higher link utilization. These findings offer practical guidance for deploying TSN in bandwidth-constrained industrial environments.

WiFi 7 IEEE (802.11be) introduced Multi-Link Operation (MLO) that enables its devices to communicate over multiple links to support evolving latency-sensitive and high-throughput applications. However, MLO requires advanced scheduling algorithms to optimize the operation. This paper models WiFi 7 scheduling as a constrained Markov Decision Process that optimizes throughput, delay, latency and fairness while capturing access constraints and traffic dynamics. We also develop an attention-enhanced Rainbow Deep Q-Network (DQN) scheduling framework that combines multi-head attention, distributional Q-learning, and prioritized experience replay. Simulation results show up to 2.3× throughput improvement, 6× delay reduction, and marked gains in packet drop rate and spectral efficiency over baseline Round Robin scheduling.

Private 5G networks are emerging as key enablers for smart factories, where a single device often handles multiple concurrent traffic flows with distinct Quality of Service (QoS) requirements. Existing simulation frameworks, however, lack the fidelity to model such multi-flow behavior at the QoS Flow Identifier (QFI) level. This paper addresses this gap by extending Simu5G to support per-QFI modeling and by introducing a novel QoS-aware Proportional Fairness (QoS-PF) scheduler. The scheduler dynamically balances delay, Guaranteed Bit Rate (GBR), and priority metrics to optimize resource allocation across heterogeneous flows. We evaluate the proposed approach in a realistic smart factory scenario featuring edge-hosted machine vision, real-time control loops, and bulk data transfer. Results show that QoS-PF improves deadline adherence and fairness without compromising throughput. All extensions are implemented in a modular and open-source manner to support future research. Our work provides both a methodological and architectural foundation for simulating and analyzing advanced QoS policies in industrial 5G deployments.

Data Confidence Fabrics (DCFs) are emerging as a mechanism to obtain measurable trust in decentralized smart computing environments, while remote attestation (RA) is being established as a key mechanism for verifying security in distributed systems. However, both of these approaches remain underutilized in container orchestration platforms, resulting in inadequate trust guarantees, increased attack surface areas, and insufficient mechanisms for verifying the integrity of devices and applications. In this paper, we propose leveraging DCFs to integrate RA with software security practices, and utilizing this integration to securely onboard devices and containerized applications by tracing their provenance and analyzing vulner-abilities. This dual-level protection bridges device attestation measurements with measurements of application security to provide measurable and transparent confidence scores for both. The proposed approach brings trustworthiness into a distributed environment where devices are continuously monitored for malicious tampering, and applications are assessed before being run. We verified this approach on a setup representing real environments. Additionally, a machine learning model was put under test and trained on data weighted with confidence scores produced by our proposed approach. The model saw improved performance and accuracy, showing that this approach can increase the reliability of systems.

Time-Sensitive Networking (TSN) standards have enabled deterministic Ethernet solutions, providing low-latency, reliable communication critical for Industry 4.0 applications. With industrial environments increasingly incorporating wireless solutions, the integration of TSN over 5G and Wi-Fi has emerged as a key research focus. This paper provides a comparative analysis of the integration of 5 G and Wi-Fi with TSN, evaluating latency, reliability, scalability, synchronization, mobility support, and implementation complexity. The simulation results identify the conditions under which 5G+TSN or Wi-Fi+TSN offers superior performance, offering clear recommendations for the selection of technology adapted to industrial use cases.

The need to trust data has become a key requirement in modern distributed systems. To facilitate measurable trust and confidence in data and applications spanning heterogeneous systems, the emerging concept of a Data Confidence Fabric (DCF) offers a compelling solution. Data producers and processors provide metadata, known as annotations, recording trust insertion along the data delivery chain. Thus, it is possible to asses the trustworthiness of data before processing it. While a DCF, such as the Alvarium framework, enables this management of trust, there is a cost in terms of the overheads associated with security annotations themselves. To improve efficiency of a DCF, we therefore propose a set of techniques for making annotations more compact and to reduce the number of DCF transactions. Our work shows that transaction efficiency gains of up to 93% in our considered use cases can be achieved.

The growing heterogeneity and decentralization in the modern computing paradigm of edge-cloud continuum introduces new constraints on storage systems, such as storage type, associated processors, privacy, scarce resources, compliance, GDPR and geographical restrictions. While existing distributed data and object stores can ensure data availability and fault-tolerance, they are not flexible or dynamic enough to address these diverse set of constraints. In this paper, we introduce a modular policy-driven data placement framework, CATER, designed to seamlessly integrate with existing storage systems and overcome the aforementioned limitations. CATER formulates the data placement problem as an optimization model, incorporating data collocation and hardware constraints. We integrated a pro-totype of CATER with Apache Ozone and conducted experiments and simulations. Results show a 23% improvement in data placement while respecting 100 % of the constraints.

In distributed environments, data for Machine Learning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit from using large training sets, not all data can be equally trusted. In this work, we examine data trust as a factor in creating ML models, and explore an approach using annotated trust metadata to contribute to data weighting in generating ML models. We assess the feasibility of this approach using well-known datasets for both linear regression and classification problems, demonstrating the benefit of including trust as a factor when using heterogeneous datasets. We discuss the potential benefits of this approach, and the opportunity it presents for improved data utilisation and processing.

Cable networks are embracing Distributed Access Architectures (DAA) that push traditionally centralized network functions to the network edge. While this shift offers higher data rates, it complicates the management and configuration of the network by having functions distributed in remote nodes. Separately, SDN has evolved for enabling programmable networks of distributed switches, managed in a logically centralized manner. This paper presents an evolutionary path for SDN-based cable DAA that can overcome the aforementioned challenges and support new network services. We present an SDN-DAA architecture, implemented in a real DAA remote device, and evaluated using Mininet to demonstrate the operational benefits.

Evolved Multimedia Broadcast Multicast Service (eMBMS) is used in cellular networks to improve the utilization of scarce wireless resources in high user density service areas. However, eMBMS configuration involves interwoven decisions including which base stations (eNB) to synchronize to form Single Frequency Networks (SFN), which video qualities to be serviced, and how to distribute resources among different videos. These decisions should accommodate disparate channel conditions for eMBMS users, and the impact of eNB's unicast-load in the service area. In this paper, we formulate eMBMS configuration as an optimization problem that maximizes the video QoE for users. Additionally, we present NIMBLE as an eMBMS configuration heuristic, guided by our optimization framework, to solve the problem in realtime. Furthermore, NIMBLE's design integrates elements to accommodate the dynamic nature of cellular networks resulting from changes in both user, and network state over time. We developed a simulation testbed, and performed extensive experiments to show that, in comparison to state-of-the-art schemes, NIMBLE can increase the average user throughput by 150%, and reduce the bitrate switches by 75%.

In this paper, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns (video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the base station (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bit-rate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their users' streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that SMASH guarantees a high QoE with consistent performance across a variety of streaming contexts.

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.

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.

The emerging popularity of live streaming services poses a great challenge for the rigid and static traditional Internet architecture. The rise in adaptation of Software Defined Networking (SDN) by Internet Service Providers (ISP) and Content Delivery Networks (CDN) presents an opportunity to dynamically adapt and respond in real-time to high definition (HD) mega events or dynamic short-lived broadcast events. In this paper, we present an SDN-based system design that utilizes a communication framework between ISPs and CDNs to interact and thus enable a reliable and resource efficient live streaming service. We build and deploy an optimization model that can maximize the video quality for users while minimizing the resource utilization for both ISPs and CDNs. The model considers device capabilities, network constraints and the subscription level of users with the ISP/CDN. Our system is a network-assisted, cross-layer, approach that implements multicast at the network layer and can dynamically adapt the video bitrates that are served to each client at the application layer. We build a prototype of our proposed design and evaluate real-world scenarios with up to 500 users streaming multiple videos at different bitrates. Results show that our approach can increase average user goodput by up to 70% while almost eliminating frame drops by handling network congestion.

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.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

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.

In this paper we present a Multi-Profile Ultra High Definition (UHD) DASH dataset composed of both AVC (H.264) and HEVC (H.265) video content, generated from three well known open-source 4K video clips. The representation rates and resolutions of our dataset range from 40Mbps in 4K down to 235kbps in 320x240, and are comparable to rates utilised by on demand services such as Netflix, Youtube and Amazon Prime. We provide our dataset for both realtime testbed evaluation and trace-based simulation. The real-time testbed content provides a means of evaluating DASH adaptation techniques on physical hardware, while our trace-based content offers simulation over frameworks such as ns-2 and ns-3. We also provide the original pre-DASH MP4 files and our associated DASH generation scripts, so as to provide researchers with a mechanism to create their own DASH profile content locally. Which improves the reproducibility of results and remove re-buffering issues caused by delay/jitter/losses in the Internet.

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 well-known non-rooted Android network monitoring application, G-NetTrack 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.

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.

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.

Dynamic adaptive streaming over HTTP (DASH) is widely adopted for video transport by major content providers. However, the inherent high variability in both encoded video and network rates represents a key challenge for designing efficient adaptation algorithms. Accommodating such variability in the adaptation logic design is essential for achieving a high user quality of Experience (QoE). In this paper, we present ARBITER+ as a novel adaptation algorithm for DASH. ARBITER+ integrates different components that are designed to ensure a high video QoE while accommodating inherent system variabilities. These components include a tunable adaptive target rate estimator, hybrid throughput sampling, controlled switching, and short-term actual video rate tracking. We extensively evaluate the streaming performance using real video and cellular network traces. We show that ARBITER+ components work in harmony to balance temporal and visual QoE aspects. Additionally, we show that ARBITER+ enjoys a noticeable QoE margin in comparison to state-of-the-art adaptation approaches in various operating conditions. Furthermore, we show that ARBITER+ also achieves the best application-level fairness when a group of mobile video clients shares a cellular base station.

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.

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 rise of Software Defined Networking (SDN) presents an opportunity to overcome the limitations of rigid and static traditional Internet architecture and provide services like network layer multicast for live video streaming. In this paper we propose mCast, an SDN-based architecture for live streaming, to reduce the utilization of network and system resources for both Internet Service Providers (ISP) and Content Delivery Networks (CDN) by using multicast over the Internet. We propose a communication framework between ISPs and CDNs to enable mCast while retaining user and data privacy. mCast is transparent to the clients and maintains the control of CDNs on user sessions. We developed a testbed and performed large scale evaluation and comparison. Results showed that mCast can improve the video quality received by clients and, for CDNs and ISPs in comparison to IP unicast, mCast can decrease link utilization by more than 50% and network losses to 0%.

The dramatic growth of cellular 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 system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a 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. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance 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 comparison. 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.

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.

This paper exposes several problems in managing hidden terminals for uplink traffic in rate-controlled environments, and presents solutions to mitigate them. In particular, we focus on scenarios, in which, clients are associated with an access point (AP). The main challenge stems from the negative interactions between rate-control protocols and hidden terminals. To expose the problems, we use a recent channel estimation approach (CEA) to differentiate the reason for packet losses into three categories, noise, congestion and hidden terminals. Our testbed and simulation-based experiments show that the accuracy of hidden terminal estimations using the CEA degrades as MAC-layer ACK frames are sent with relatively high transmission rates. To improve the accuracy of the CEA, the results demonstrate the necessity and cost of making the AP send ACKs based on the minimum ACK rate of all clients. We propose an adaptive scheme that combines both the CEA and RTS/CTS messages. The proposed scheme increases the overall throughput of Minstrel rate-control algorithm by 60% in case of light congested environments. We also proposed a threshold-based adaptive RTS/CTS scheme based on the prior scheme to handle the highly congested environments. The threshold-based adaptive RTS/CTS scheme improves the overall throughput of the adaptive RTS/CTS scheme and Minstrel algorithm between 20-35%. Finally, we propose and evaluate an opportunistic burst scheme, which enforce fairness among clients. Simulation results show that opportunistic bursting outperforms the prior schemes and Minstrel algorithm in Jain's fairness metric (between 0.11 and 0.38) for a realistic given scenario. It also keeps a relatively high overall throughput.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

In this demonstration we present a platform that encompasses all of the components required to realistically evaluate the performance of Dynamic Adaptive Streaming over HTTP (DASH) over a real-time NS-3 simulated network. Our platform consists of a network-attached storage server with DASH video clips and a simulated LTE network which utilises the NS-3 LTE module provided by the LENA project. We stream to clients running an open-source player with a choice of adaptation algorithms. By providing a user interface that offers user parametrisation to modify both client and LTE settings, we can view the evaluated results of real-time interactions between the network and the clients. Of special interest is that our platform streams actual video clips to real video clients in real-time over a simulated LTE network, allowing reproducible experiments and easy modification of LTE and client parameters. The demonstration showcases how changes in LTE network settings (fading model, scheduler, client distance from eNB, etc.), as well as video-related decisions at the clients (streaming algorithm, quality selection, clip selection, etc.), can impact on the delivery and achievable quality.

Dynamic adaptive video over HTTP (DASH) is fast becoming the protocol of choice for content providers for their online video streaming delivery. Concurrently, dependence on cellular Long Term Evolution (LTE) networks is growing to serve user demands for bandwidth-hungry applications, especially video. Each LTE base station's (eNodeB) scheduler assigns wireless resources to individual clients. Several alternative schedulers have been proposed, especially to meet the user's desired quality of experience (QoE) with video. In this paper, we investigate the impact of the scheduler on DASH performance, motivated by the fact that video performance and the underlying traffic models are different from other HTTP/TCP applications. We use our laboratory testbed employing real video content and streaming clients, over a simulated ns-3 LTE network. We quantify the impact of the scheduler and show that it has a significant impact on key video streaming performance metrics such as stalls and QoE, for different client adaptation algorithms. Additionally, we show the impact of user mobility within a cell, which has the side-effect of improving performance by mitigating long-term fading effects. Our detailed assessment of four LTE schedulers in ns-3 shows that the proportional fair scheduler achieves the best overall user experience, although somewhat disadvantaging static cell-edge users.

In this paper we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). Our trace-based simulation dataset provides a means of evaluation in frameworks such as NS-2 and NS-3, while our testbed evaluation dataset offers a means of analysing the delivery of content over a physical network and associated adaptation mechanisms at the client. Our datasets are available in both H.264 and H.265 with encoding rates comparative to the representations and resolutions of content distribution providers such as Netflix, Hulu and YouTube.

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.

Network utility theory has been extensively employed for resource management purposes. However, traditional utility functions cannot support attributes that affect the resource allocation, such as mobility or more advanced traffic models. Especially in the context of a heterogeneous wireless network, transient parameters can have varying effects on each access network type. This work proposes a new utility function that can support multiple design requirements for mobile networks including advanced traffic models, user classes, handover and session priorities. We integrate the new utility function with the Super Base Station framework and devise a novel trigger-based network and resource assignment framework that efficiently copes with the complexity of a heterogeneous wireless network. Our simulation results show that the proposed sub-optimal trigger-based framework performs equally well as the complex optimal scheme.

XG-PON requires an effective dynamic bandwidth allocation (DBA) mechanism for upstream traffic to support quality of service for different classes of traffic. We propose X-GIANT, which extends GPON based GigaPON Access Network (GIANT) DBA, with validated optimisations to the originally proposed key parameters - service timers and assured vs non-assured ratio of medium priority traffic. We implement X-GIANT in a standard-compliant XG-PON module designed for the state-of-the-art ns-3 simulator, tune the above key parameters and show that mean-delay and throughput for different classes of traffic obey the XG-PON requirements and respect priorities at both light and heavy upstream loads. We also show that X-GIANT shows better mean-delay performance than Efficient Bandwidth Utilisation (EBU), a recently proposed, GIANT-derived, priority-based DBA mechanism for XG-PON, for all three classes of traffic simulated.

HTTP streaming currently dominates Internet traffic. It is increasingly common that video players employ adaptive bitrate (ABR) streaming strategies to maximise the user experience by selecting the highest video representation while targeting stall-free playback. Our interest lies in the common situation where a set of video flows are competing for access to a shared bottleneck link, such as in a cellular radio access network. We observe that ISPs (e.g. cellular operators) are considering innetwork techniques for resource allocation and sharing among different users. Buoyed by the ability of software defined networks (SDN) to offer flow-specific control and traffic shaping, we focus on traffic shaping techniques, and experimentally analyse the effect on ABR video flows when sharing a bottleneck link. We conduct experiments using the GPAC video player operating over a Mininet virtual network. We conclude that traffic shaping can allow a balance of fairness, efficiency and quality. Traffic shaping ABR videos reduce the number of stalls and quality switches, while also reducing the peaks for the aggregate network traffic.

This paper describes our demonstration of a network repair problem where a robot bridges a gap between two disconnected wireless nodes by searching for a good position and moving there to forward data between the two nodes. It serves to show the potential for our published solutions for automated network repair. A simple Adhoc network consists of two Intel Galileo Gen 2 nodes exchanging messages and an NXT Mindstorm robot with another Galileo on board healing the network connection between the two Galileos in the case the two get disconnected. The demo showcases a solution that employs mobile agents to serve as relays to bridge the connectivity gaps in the wireless network.

Recent studies demonstrate that the performance of a wireless sensor network (WSN) can be improved by deploying multiple sinks in the network. Therefore, in this paper we present different routing protocols for multi-sink WSNs based on the routing protocol for low-power and lossy networks (RPL). Our protocols use different routing metrics and objective functions (OFs). We use the available bandwidth, delay, MAC layer queue occupancy, and expected transmission count (ETX) as the tie-breaking metrics in conjunction with the shortest hop-count metric. Our OFs use the tie-breaking metrics on a greedy or end-to-end basis. Our simulation results demonstrate that the protocols based on the delay, buffer occupancy, and ETX metrics demonstrate best performance, increasing the packet delivery ratio by up to 25% and decreasing the number of retransmissions by up to 65%, compared to a version of the RPL protocol that only uses the hop-count metric. Another key insight is that, using the tie-breaking metrics on a greedy basis demonstrates a slight performance improvement compared to using the metrics on an end-to-end basis. Finally, our results also demonstrate that multiple sinks inside a WSN improve the RPL-based protocol performance.

In many emerging wireless sensor network scenarios, the use of a fixed infrastructure of base stations for data collection is either infeasible, or prohibitive in terms of deployment and maintenance costs. Instead, we consider the use of mobile devices (i.e. smartphones) carried by people in their daily life to collect data from sensor nodes opportunistically. As the movement of these mobile nodes is, by definition, not controlled for the purpose of data collection, synchronization through contact probing becomes a challenging task, particularly for sensor nodes, which need to be aggressively duty-cycled to conserve energy and achieve long lifetimes. This paper formulates this important problem, providing an analytical solution framework and systematically investigating the effective use of contact probing for opportunistic data collection. We present two new solutions, Sensor Node-Initiated Probing (SNIP) and SNIP-Rush Hours, the latter taking advantage of the temporal locality of human mobility. These schemes are evaluated using numerical analysis and COOJA network simulations, and the results are validated on a small sensor testbed and with the real-world human mobility traces from Nokia MDC Dataset. Our experimental results quantify the relative performance of alternative solutions on sensor node energy consumption and the efficacy of contact probing for data collection, allowing us to offer insights on this important emerging problem.

In this paper, we use graph analysis to evaluate the network architecture of a large scale delay tolerant network (DTN) of transoceanic aircraft. At LCN (Local Computer Networks) 2014 we analyzed information propagation inside a pure opportunistic version of this network, a scenario constructed from more than 2,500 traces of transatlantic flights in which communications relied only on the sporadic contacts between airplanes. As only a small percentage of the nodes were capable of performing efficient air-to-ground communications we concluded the need to devise a more suitable network architecture by combining opportunistic and satellite communication systems. We propose a generic methodology based on graph analysis (both static and dynamic temporal) to evaluate the different ways to create this new architecture. We show the architectural combination that most improves the network delivery performance while minimizing its deployment costs.

In this paper, we propose a "Neighbour Disjoint Multipath (NDM)" scheme that increases resilience against node or link failures in a wireless sensor network (WSN). Our algorithm chooses the shortest path between a sensor and the sink as the primary path, thus ensuring the algorithm is energy efficient under normal circumstances. In selecting the backup paths, we utilise the disjoint property to ensure that i) when there are k paths between source and sink, no set of k node failures can result in total communication break between them, and ii) by having (k-1) spatially separated backup paths w.r.t. the primary path, the probability of simultaneous failure of the primary and backup paths is reduced in case of localised poor channel quality or node failures. Our algorithm not only ensures the node disjointedness characteristics of the constructed paths, but also tries to minimise the impact of co-located node or link failures where a localised portion of the network may be unusable. We analyse the motivation behind our idea clearly, and discuss the algorithm in detail. We also compare the NDM scheme with other common multipath techniques such as node-disjoint and edge-disjoint approaches, and point out its effectiveness through simulation.

XG-PON is the next-generation standard for passive optical networks operating at 10Gb/s and TCP is the dominant transport protocol of the Internet. In this paper, we present the first performance evaluation of TCP over XG-PON, considering efficiency, fairness, responsiveness, and convergence. The impact of XG-PON's large delay-bandwidth product and asymmetric bandwidth provision are assessed, together with the dynamic bandwidth allocation mechanism. Our state-of-the-art NS3 simulation uses real implementations of three TCP variants (Reno, CUBIC and H-TCP) from the Network Simulation Cradle. Our results highlight several issues that arise for TCP over XG-PON, and emphasise the need for improved awareness of medium access control and scheduling in the context of specific TCP congestion control behaviour.

This paper investigates the converged architecture of an LTE mobile network and a PON access network. We identify that the default handover behaviour for LTE is highly inefficient when performed on a backhaul PON tree-topology. We propose a scheme of intelligent opportunistic caching, using the existing resources at the eNBs, to help mitigate this inefficiency. We show that we can achieve a significant reduction in the amount of redundant traffic sent over the PON and that in circumstances when the PON upstream is heavily congested, we can greatly improve the mobile terminal's sustained data rate by up to 50% during the handover process.

Opportunistic data collection in wireless sensor networks uses passing smartphones to collect data from sensor nodes, thus avoiding the cost of multiple static sink nodes. Based on the observed mobility patterns of smartphone users, sensor data should be preforwarded to the nodes that are visited more frequently with the aim of improving network throughput. In this article, we construct a formal network model and an associated theoretical optimization problem to maximize the throughput subject to energy constraints of sensor nodes. Since a centralized controller is not available in opportunistic data collection, data pre-forwarding (DPF) must operate as a distributed mechanism in which each node decides when and where to forward data based on local information. Hence, we develop a simple distributed DPF mechanism with two heuristic algorithms, implement this proposal in Contiki-OS, and evaluate it thoroughly. We demonstrate empirically, in simulations, that our approach is close to the optimal solution obtained by a centralized algorithm. We also demonstrate that this approach performs well in scenarios based on real mobility traces of smartphone users. Finally, we evaluate our proposal on a small laboratory testbed, demonstrating that the distributed DPF mechanism with heuristic algorithms performs as predicted by simulations, and thus that it is a viable technique for opportunistic data collection through smartphones.

Wireless Sensor Networks (WSNs) increasingly enable the interaction of the physical world with services, which may be located across the Internet from the sensing network. Cloud services and big data approaches may be used to store and analyse this data to improve scalability and availability, which will be required for the billions of devices envisaged in the Internet of Things (IoT). This potential of WSNs is limited by the relatively low number deployed and the difficulties imposed by their heterogeneous nature and limited (or proprietary) development environments and interfaces. This paper proposes a set of requirements for achieving a pervasive, integrated information system of WSNs and associated services. It also presents an architecture which provides a set of abstractions for the different types of sensors and services, enabling them to take advantage of Big Data and cloud technologies and which is termed holistic as it caters for the data flow from sensors through to services. The architecture has been designed for implementation on a resource constrained node and to be extensible to server environments, shown in this paper where we present a 'C' implementation of the core architecture, including services on Linux and Contiki (using the Constrained Application Protocol (CoAP)) and a Linux service to integrate with the Hadoop HBase data store.

A Wireless Sensor Network (WSN) is composed of many sensor nodes which transmit their data wirelessly over a multi-hop network to data sinks. Since WSNs are subject to node failures, the network topology should be robust, so that when a failure does occur, data delivery can continue from all surviving nodes. A WSN is k-robust if an alternate length-constrained route to a sink is available for each surviving node after the failure of up to k-1 nodes. Determining whether a network is k-robust is an NP-complete problem. We develop a Constraint Programming (CP) approach for solving this problem which outperforms a Mixed-Integer Programming (MIP) model on larger problems. A network can be made robust by deploying extra relay nodes, and we extend our CP approach to an optimisation problem by using QuickXplain to search for a minimal set of relays, and compare it to a state-of-the-art local search approach.

Resource management in heterogeneous wireless networks has been approached from various angles by the research community. The complexity of the network and the heterogeneity of clients make the conclusive comparison of the various resource allocations challenging. This work introduces superBS, an approach defining a theoretical optimal resource allocation that adheres to the required resource management policies and can be used as a reference for the performance of considered algorithms, mitigating the heterogeneity of the system. Two applications that leverage superBS are developed, an implementation of a heuristic resource management algorithm, and the enhancement of a popular fairness metric with support for clients of different classes and traffic demands. Simulations demonstrate the performance of superBS and the proposed algorithms.

Wireless Sensor Networks in volatile environments may suffer damage, and connectivity must be restored. The repairing agent must discover surviving nodes and damage to the physical and radio environment as it moves around the sensor field to execute the repair. We compare two approaches, one which re-generates a full plan whenever it discovers new knowledge, and a second which attempts to minimise the required number of new radio nodes. We apply each approach with two different heuristics, one which attempts to minimise the cost of new radio nodes, and one which aims to minimise the travel distance. We conduct extensive simulation-based experiments, varying key parameters, including the level of damage suffered, and comparing directly with the published state-of-the-art. We quantify the relative performance of the different algorithms in achieving their objectives, and also measure the execution times to assess the impact on being able to make autonomous decisions in reasonable time.

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality.

Contiki's Cooja is a very popular Wireless Sensor Network (WSN) simulator, but it lacks support for modelling sensing coverage. We introduce WSN-Maintain, a Cooja-based tool for maintaining coverage requirements in an in-building WSN. To analyse the coverage of a building, WSN-Maintain takes as input the floorplan of the building, the coverage requirement of each region and the locations of sensor nodes. We take account of the heterogeneity of device specifications in terms of communication capability and sensing coverage. WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator. We show that WSN-Maintain is able to automatically turn on redundant nodes to maintain the coverage requirement when active nodes fail and report failures that require physical maintenance. This tool allows us to evaluate different approaches to maintain coverage, including deferring physical maintenance to reduce operational costs.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Simulation of building evacuations can be a powerful tool for predicting evacuation outcomes, but for this prediction to be useful it must be produced in a timely manner. The building evacuation outcomes are dependent on the movement decisions of the occupants, but simulating all possible combinations of occupant decisions is infeasible. Our contribution is a novel technique using building structure knowledge in the form of a Network Flow Graph to determine where and when occupants might interact with one another. We decompose the problem into non-interacting groups, to be simulated separately, which leads to a significant simulation workload reduction.

This paper studies video multicasting in large-scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low-capacity multihop wireless paths. A set of heuristic-based algorithms is described that together aim to maximize reliable network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are presented, showing that our multicasting algorithms can achieve up to 40 percent more throughput than other related published approaches.

We develop a set of solution techniques for real-time evacuation guidance of pedestrians during emergency, focusing on evacuation from buildings during a fire. We model the problem as an extension of a dynamic network flow by allowing for nodes and edges to expire over time. This captures evacuation situations where the spreading hazard renders parts of the network unavailable. We formally state the problem, analyze its complexity, develop a set of heuristic approaches and compare their performance against a number of most relevant alternative approaches. We experimentally demonstrate that our heuristics outperform the alternatives and are suitable for real-time use even for large networks.

We introduce ER-MAC, a hybrid MAC protocol for emergency response wireless sensor networks. ER-MAC is designed as a hybrid of the TDMA and CSMA approaches, giving it the flexibility to adapt to traffic and topology changes. It adopts a TDMA approach to schedule collision-free slots. Nodes wake up for their scheduled slots, but otherwise sleep to conserve energy. When an emergency occurs, nodes that participate in the emergency monitoring change their MAC behaviour by allowing contention in TDMA slots. Simulations in ns-2 show that ER-MAC outperforms Z-MAC with higher delivery ratio, lower latency, and lower energy consumption.

The convergence of heterogeneous wireless access technologies is an intrinsic part of the long term evolution of wireless networks. This convergence creates network overlays in which achieving seamless and efficient roaming between different technologies, commonly known as vertical handoff (VHO), introduces several challenging design issues. The media independent handover standard (IEEE 802.21) enables information exchange across different layers to improve VHO performance. In this work, we present a framework for integrating an adaptive lifetime-based vertical handoff (ALIVE-HO) algorithm with IEEE 802.21. The proposed framework is implemented and the optimal design value of the application parameter of ALIVE-HO is experimentally determined for video streaming applications under different operating scenarios.

The goal of the GINSENG project is a performance-controlled sensor network suitable for applications with specific network performance requirements, such as plant automation and health monitoring. Any such sensor network must include a deterministic MAC protocol that can meet the application's targets in areas like delivery delays and reliability. We present a description and preliminary evaluation results of GinMAC, designed to be such a MAC protocol. Also included are details of a deployment to the GALP oil refinery in Sines, Portugal.

The integration of different wireless access technologies combined with the huge characteristic diversity of supported services in next-generation wireless systems creates a real heterogeneous network. In this paper, we propose a generic practical framework that optimizes media streaming in heterogeneous systems by taking advantage of cost and resource characteristic diversity of the integrated access technologies and the buffering capability of streaming applications. The proposed optimization framework represents a means to compromise the tradeoff between different performance metrics including streaming monetary cost, signaling load, and session quality. Additionally, it accommodates different design challenges including mobility randomness, limited processing capacity, and handoff delay requirements. The simulation results provide important insights on the design of pricing profiles in integrated systems. Additionally, the results show that significant cost savings can be realized using the newly proposed streaming management algorithms and optimization framework.

This work introduces a dynamic model for the fire emergency evacuation problem. The model extends the concept safety introduced by Barnes et.al. for the situation when the navigation graph is dynamic. The two possible scenarios are described for using the dynamic model with a Wireless Sensor Network for fire emergency evacuation.

In our previous work, we have analyzed the worst performance for tree-based and mesh-based multicast along the link stress, the number of overlay hops, and the number of shortest paths. In this paper, we extend our research through studying the average performance and the difference between the worst and the average performance for these metrics. We present a set of theoretical results that evaluate the average performance and the performance difference for tree-based multicast and mesh-based multicast in quantity. And also, we program NICE tree and CAN-based multicast in NS2 to evaluate our theoretical prediction and compare tree-based and mesh-based multicast. Simulation results prove our theoretical analysis. We find that tree-based multicast suits to not only real-time but also interactive streaming media applications, and mesh-based multicast holds the promise for the bottleneck-avoidance and reliable transmission in multi-source non-real-time applications.

This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy multi-hop wireless paths with low capacity. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the alternative channel assignment algorithm. These algorithms use different approaches to arrange multicast group members into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are used to determine the performance of the different approaches.

To implement duty cycles with packet based transceivers, a sender transmits a trail of identical packets (which we call framelets) of which the receiver is able to catch one in its active listening phase. This communication concept is used in the standard low power listening (LPL) protocol shipped with TinyOS 2.x. This existing solution has many shortcomings which result in a very limited network performance. In this paper, we firstly present an alternative framelet based low power listening implementation called Framelet Communications (FrameComm) that eliminates these shortcomings. Secondly, we present a novel additional improvement to FrameComm - Interception and Aggregation of Framelet Communications (i-FrameComm) - that further improves network performance by opportunistically aggregating packets over the radio channel. A prototype implementation of the proposed FrameComm mechanism in TinyOS 2.02 on TelosB nodes is used for evaluation and comparison. The experiments show that the interception and aggregation method increases network throughput and lifetime as communication resources are used more efficiently.

FrameComm is a contention based, duty cycled, MAC protocol that ensures a message will be transmitted during the receiverpsilas listen phase by sending a packet, followed by a short gap, repeatedly for a precalculated number of times or until an acknowledgment is received. While introducing duty cycled communications can yield large power savings it does so at the cost of increased delay and decreased throughput. Many WSNs may incorporate several distinct message types of varying priority. A node with a high priority message to send may find the channel to be busy with a lesser priority message from another node and must therefore dasiaback-offpsila leading to further delays. In a multi-hop environment, these delays are compounded and may become unacceptably large. This paper proposes adding a high priority interrupt message to FrameComm that allows a node with important data to send to interrupt another nodepsilas lesser priority transmission giving immediate access to the channel. The priority interrupt mechanism is evaluated using an implementation in TinyOS 2 on a small laboratory testbed.

Wireless mesh networks hold great promise in the wireless transmission of video flows, particularly if the problem of providing sufficient network capacity can be addressed. For this reason, schemes which help to address this difficulty in capacity-limited wireless networks are of great interest. This paper presents a novel and simple algorithm, adaptive split transmission algorithm, for achieving real-time, and quality-guaranteed video transmission in wireless mesh networks. The algorithm utilizes the unused capacities of multiple channels rather than trying to transmit the flow over just one overloaded channel. The flow is efficiently split into several sub-flows in a capacity-aware manner, each sub-flow then being transmitted through different channels in parallel. The adaptive split transmission algorithm controls flows dynamically in response to changes in the states of the available channels, thereby avoiding the overloading of any one channel. We evaluate the algorithm through simulations. The results show that the adaptive split transmission algorithm achieves synchronized, quality-guaranteed, and real-time wireless video transmission. The proposed algorithm can be used for interactive real-time wireless video applications without changing current wireless hardware, MAC protocols and upper-layer protocols.

The integration of different wireless access technologies combined with the huge characteristic diversity of supported services in next-generation systems creates a real heterogeneous system. This heterogeneity opens new avenues for improving the system utility of both operators and users. In this paper, we propose a Pseudo-optimal greedy media streaming (PGMS) algorithm to reduce the cost of streaming sessions using an optimization framework that considers the real-time requirements of the vertical handoff decision. The proposed algorithm results in noticeable reductions in session cost, signaling load, and blocking probability in comparison to previously proposed heuristics. More importantly, the results show the adaptability of PGMS to different operating scenarios including different mobility patterns and service cost profiles.

Data aggregation is a method used in sensor networks to reduce the amount of messages transported. By aggregating, the data contained in several messages is fused into one single message. If such a message, containing the equivalent of many individual messages, is lost due to transmission errors then this has a detrimental effect on the application quality experienced. In many sensor network applications a constant supply of data is needed and therefore application quality is severely effected by excessive data loss. This paper proposes and evaluates the use of an in-network control mechanism to offset this disadvantageous effect. The control mechanism analytically calculates the correct reliability that an aggregate of given size must be forwarded at in order to meet application specific goals.

Using data aggregation to reduce power consumption is a well studied area. In this paper we show a new result: data aggregation can also be used to increase reliability. In scenarios where status is being collected from every node (e.g. network management), one measure of reliability is the completeness (nodes included). Using some of the power savings from aggregation to increase the retransmission limit for aggregated data gives significant improvements in completeness. The contribution of this paper is in providing a detailed comparison of the completeness and energy efficiency for aggregated and non-aggregated data. Results, derived by numerical analysis and simulation, show that with data aggregation and increased retransmission limits, reliability can be substantially improved, while still saving power. These results are applicable to designing network management protocols for Wireless Sensor Networks, as well as for other scenarios where high completeness is required for network-wide data collection.

Abstract—Wireless sensor networks are collections of autonomous devices with computational, sensing and wireless communication capabilities. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. This paper reports on a joint project between The Tyndall National Institute and the Computer Science Department at University College Cork, Ireland in developing a novel miniaturised modular platform for wireless sensor networks. The system architecture, hardware and software will be discussed as well as details of the deployment scenario chosen for the project – a car park management system. Results and problems encountered during deployment will be presented Keywords-component Wireless Sensor Networks, deployment, car park monitoring

Data collected in a sensor network is transported hop-by-hop to a sink for further analysis. The quality of the analysis depends on the amount of data reaching the sink. Hence, data transport reliability influences the quality of the analysis. Data aggregation is a common method used in sensor networks to reduce the amount of messages transported. By aggregating, the data contained in several messages is fused into one single message. Therefore, data aggregation significantly influences the overall data transport reliability observed at the sink. This influence is analyzed and described analytically and by experiment within this paper. Furthermore it is shown how the influence of data aggregation on data transport reliability can be controlled for a particular class of data gathering application

The 19 papers in this special issue focus on cross-layer optimized wireless multimedia communications. The papers are organized into four sections: quality of service support for wireless networks; system architecture for multimedia over wireless networks; resource allocation in wireless multimedia communications, and multimedia coding and scheduling issues in wireless networks.

This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by (sigma, rho) regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel (sigma, rho, lambda) regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold rho* such that for a given set of K groups, when the average rate of traffic entering the group end hosts rho macr > rho*, the ratio of the worst-case multicast delay bound of the proposed (sigma, rho, lambda) regulator over the traditional (sigma, rho) regulator is O(1/Kn) for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations.

A complete wireless sensor network solution for carpark management is presented in this paper. The system architecture and design are first detailed, followed by a description of the current working implementation, which is based on our DSYS25z sensing nodes. Results of a series of real experimental tests regarding connectivity, sensing and network performance are then discussed. The analysis of link characteristics in the car-park scenario shows unexpected reliability patterns which have a strong influence on MAC and routing protocol design. Two unexpected link reliability patterns are identified and documented. First, the presence of the objects (cars) being sensed can cause significant interference and degradation in communication performance. Second, link quality has a high temporal correlation but a low spatial correlation. From these observations we conclude that a) the construction and maintenance of a fixed topology is not useful and b) spatial rather than temporal message replicates can improve transport reliability

Today's intra-domain protocols are limited in their scalability. We examine these limitations and propose an alternative in the form of an IGP based on path vectors. Taking advantage of the recent research interest in BGP's performance, we are able to develop a protocol that converges quickly, produces a relatively low level of control-plane traffic and promises to scale to very large networks, while still producing shortest path trees based on minimising latency or maximising bandwidth. We show that such a protocol converges and present results of its simulation

This paper investigates the problem of how to improve TCP performance in multi-rate ad-hoc networks with congested links. To improve network performance, different rate adaptation algorithms, such as automatic rate fallback (ARF) and receiver-based autorate (RBAR), have been proposed to adapt the data rate according to the current channel quality. Opportunistic auto rate (OAR) protocol is an optimisation for any existing rate adaptation algorithm which leads to a significant performance gain by providing temporal fairness. We analyze the reasons for the high performance gain obtained using OAR, and show that the OAR protocol does not work well for TCP communications in ad-hoc networks where nodes use different data rates to communicate with each other (heterogeneous). Based on these analysis, we propose a congestion reactive opportunistic auto rate (CROAR) protocol, which is a new rate adaption enhancement tailored to improve TCP performance in heterogeneous multi-hop ad-hoc networks. Extensive simulations show that CROAR, compared to OAR and RBAR, produces significant throughput and end-to-end transmission latency improvements while only marginally relaxing temporal fairness

The availability of simulators and emulators that are tailored for wireless sensor networks (WSNs) is a necessary step in allowing accurate evaluation, but simply having the right tools is not sufficient to ensure that the results of experiments actually correspond to what might be expected in realistic deployments. The critical issue is what models are available in these tools, and if these are used in an appropriate manner. In this paper we review the current approaches to WSN experimentation and identify a serious shortcoming in common simulation methodology, specifically in regard to the choice of network topologies and traffic models. We hypothesize that this mismatch has an important impact on the sensitivity of published sensor network simulations. We support our hypothesis using an analysis of the directed diffusion protocol

Wireless sensor networks are collections of autonomous devices with computational, sensing and wireless communication capabilities. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. In this paper, the development of a highly modular and miniaturized wireless platform for sensor networks is described. The system incorporates a radio transceiver (in the 2.4 GHz ISM Band) with embedded protocol software to minimize power consumption and maximize data throughput. Additional input capability for sensor and actuator integration can be incorporated seamlessly due to the modular nature of the system. The total system is packaged in a modular 25 mm cubed form factor. A smaller, (10 mm cubed), prototype is currently under development. Ongoing development of highly miniaturized nodes is discussed.

A basic problem introduced by the use of radio duty cycles as an energy saving technique is needed to establish rendezvous between transmitter and receiver. Since communication can only take place when the receiver's radio is active, the transmission of frames needs to somehow overlap with this active period. This paper investigates the use of framelets - small, fixed sized frames - to achieve transmitter-receiver rendezvous and contrasts this technique with the use of long frames. The benefits of applying framelets is assessed analytically and an implementation of the concept for the DSYS25 sensor platform is presented and evaluated. The results show that substantial energy savings can be achieved with framelets as well as an increase in communication throughput

Personal Video Recorders (PVRs) such as TiVo have become very popular in recent years due to their ability to intelligently record TV content and make it accessible in an on-demand fashion to its users. Our research looks at providing a globally accessible storage architecture where all content broadcast over a period of time is available for streaming. Our architecture consists of idle ISP servers, that can be rented and released dynamically as the load requires. In this paper we focus on managing the resources required to provide TV on Demand (TVoD) system, and develop a cost function which examines the resources required to serve a client and identifies the optimal proxy to serve the client.

In this demonstration, a new sensor platform named DSYS25 is presented. The platform has a unique hardware design and runs a customized version of the TinyOS operating system. Transceiver hardware and packaging distinguish the D-Systems platform from other available designs.

The convergence of advances in storage, encoding, and networking technologies has brought us to an environment where huge amounts of continuous media content is routinely stored and exchanged between network enabled devices. Keeping track of (or managing) such content remains challenging due to the sheer volume of data. Storing "live" continuous media (such as TV or radio content) adds to the complexity in that this content has no well defined start or end and is therefore cumbersome to deal with. Networked storage allows content that is logically viewed as part of the same collection to in fact be distributed across a network, making the task of content management all but impossible to deal with without a content management system. In this paper we present the design and implementation of the Spectrum content management system, which deals with rich media content effectively in this environment.Spectrum has a modular architecture that allows its application to both stand- alone and various networked scenarios. A unique aspect of Spectrum is that it requires one (or more) retention policies to apply to every piece of content that is stored in the system. This means that there are no eviction policies. Content that no longer has a retention policy applied to it is simply removed from the system. Different retention policies can easily be applied to the same content thus naturally facilitating sharing without duplication. This approach also allows Spectrum to easily apply time based policies which are basic building blocks required to deal with the storage of live continuous media, to content. We not only describe the details of the Spectrum architecture but also give typical use cases.

Layered multicast is a promising technique for broadcasting adaptive-quality TV video to heterogeneous receivers. While several-layered multicast approaches have been proposed, prior work has identified several problems including significant and persistent instability in video quality, arbitrary unfairness with other sessions, low access link utilization due to conservative bandwidth allocation, and problems with receiver synchronization. In this paper we propose a new layered multicast scheme, where we exploit a simple, coarse-grained, two-tier loss differentiation architecture to achieve stable and fair bandwidth allocation for viewers. Despite the simplicity of our loss differentiation model, we show that it achieves most of the benefits of complex and costly priority dropping schemes. In addition, our protocol is receiver-driven and thus retains the incentives to limit bandwidth usage that are not present in existing priority dropping schemes.

Receiver synchronization of continuous media streams is required to deal with delay differences and variations resulting from delivery over packet networks such as the Internet. This function is commonly provided using per-stream playout buffers which introduce additional delay in order to produce a playout schedule which meets the synchronization requirements. Packets which arrive after their scheduled playout time are considered late and are discarded. In this paper, we present the Concord algorithm, which provides a delay-sensitive solution for playout buffering. It records historical information and uses it to make short-term predictions about network delay with the aim of not reacting too quickly to short-lived delay variations. This allows an application-controlled tradeoff of packet lateness against buffering delay, suitable for applications which demand low delay but can tolerate or conceal a small amount of late packets. We present a selection of results from an extensive evaluation of Concord using Internet traffic traces. We explore the use of aging techniques to improve the effectiveness of the historical information and hence, the delay predictions. The results show that Concord can produce significant reductions in buffering delay and delay variations at the expense of packet lateness values of less than 1%.

Receiver playout buffers are required to smooth network delay variations for multimedia streams. Playout buffer algorithms such as those commonly used in the Internet, autoregressively measure the network delay and variation and adjust the buffer delay accordingly, to avoid packets arriving too late. In this work, we attempt to adjust the buffer delay based on a prediction of the network delay and a similar measure of variation. The philosophy here is that the use of an accurate prediction will adjust the buffer delay more effectively by tracking rapid fluctuations more accurately. Proper buffer delay can lead to either (or both) a lower total end-to-end delay for a fixed packet lateness percentage or fewer late packets for a fixed total end-to-end delay which are both important metrics for applications such as IP telephony. We present a playout algorithm based on a simple normalized least-mean-square (NLMS) adaptive predictor and demonstrate using Internet packet traces that it can yield reductions in average total end-to-end delays.

Packet telephony is of increasing interest in both the telecommunications and Internet communities. The emergence of packet telephony will create new services, and presents an opportunity to rethink how conventional telephony services are implemented. In this paper, we present an architecture for telephony over packet networks (TOPS). TOPS allows users to move between terminals or to use mobile terminals while being reachable by the same name. TOPS users can have multiple terminals and control how calls are routed to them. TOPS allows for terminals with a range of capabilities such as support for video, whiteboard, and other media with a variety of coding formats. TOPS retains the necessary information on terminal capabilities to determine the appropriate type of communication to be established with the remote terminal. The architecture assumes that the underlying network supports the establishment of end-to-end connectivity between terminals, with an appropriate quality of service. The components of TOPS are a directory service, an application layer signaling protocol, and a logical channel abstraction for communication between end-systems. The directory service maps a user's name to a set of terminals where the user may be reached. A user can control the translation operation by specifying profiles that customize how his name is mapped to a set of terminals where he can be reached. Terminal capabilities are also stored in the directory service. The application layer signaling protocol establishes and maintains call state between communicating terminals. The logical channel abstraction provides a shared end-to-end context for a call's constituent media and control streams, while isolating the applications from the details of the network transport mechanisms. In addition to supporting simple point-to-point calls, the architecture supports both centralized and decentralized conferencing. We also introduce a simple encapsulation format for voice.

The development of ATM networking makes it attractive to use computer communication networks for the transport of digital audio and motion video. When coupled with advances in workstation technology, this creates the opportunity to integrate these continuous information media within a distributed computing system. Continuous media have an inherent temporal dimension, resulting in a set of synchronization requirements which have real-time properties. The paper identifies the role and position of synchronization, in terms of the support which is necessary in an integrated distributed system.>