Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person’s unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial-off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.

Most Integrated Sensing and Communications (ISAC) systems require dividing airtime across their two modes. However, the specific impact of this decision on sensing performance remains unclear and underexplored. In this paper, we therefore investigate the impact on a gesture recognition system using a Millimeter-Wave (mmWave) ISAC system. With our dataset of power per beam pair gathered with two mmWave devices performing constant beam sweeps while test subjects performed distinct gestures, we train a gesture classifier using Convolutional Neural Networks. We then subsample these measurements, emulating reduced sensing airtime, showing that a sensing airtime of 25 % only reduces classification accuracy by 0.15 percentage points from full-time sensing. Alongside this high-quality sensing at low airtime, mmWave systems are known to provide extremely high data throughputs, making mmWave ISAC a prime enabler for applications such as truly wireless Extended Reality.

Current Virtual Reality (VR) systems rely on inside-out visual-inertial tracking, which enables accurate localization but provides only a partial representation of the user's body. This limitation restricts embodiment and interaction fidelity in interactive VR scenarios requiring full-body awareness and expressive gestures. To capture both global body motion and fine-grained interaction cues within a single sensing framework, we introduce MultiSenseVR, the first open multimodal dataset that jointly captures synchronized millimeter-wave (mmWave) Wi-Fi, Surface Electromyography (sEMG), inertial signals, and high-precision 3D motion capture for ground truth in an immersive VR setting. The dataset includes recordings from 24 participants interacting with a custom fast-food simulation designed to elicit natural full-body movement. In addition to objective sensing data, MultiSenseVR provides subjective measures of presence and cybersickness. Baseline evaluations show that mmWave Wi-Fi sensing supports 3D pose estimation with accuracy comparable to camera-based approaches, while sEMG enables accurate subject-specific grasp classification. The dataset and supporting code are publicly available at https://osf.io/f6r7d.

This paper investigates the impact of interactioninduced body movement on cybersickness in Virtual Reality (VR). Therefore we designed a controlled VR environment with three interaction conditions of increasing movement complexity. Data from 24 participants, combining subjective measures and motionderived features, show a consistent increase in cybersickness with higher movement demands, particularly in conditions involving vertical displacement and full-body interaction. Moreover, correlation analysis reveals a significant negative relationship between the coefficient of variation of head velocity and cybersickness. This suggests that more variable and adaptive motion may mitigate the discomfort. These findings highlight the importance of movement characteristics, beyond movement intensity alone, for designing full-body interactive VR experiences.

The latency gap between wired and wireless networks poses a challenge in the adoption of wireless technologies in latency-sensitive scenarios. The gap is especially notable in multi-hop communication typical for industrial sensor networks and robotic swarms. The main reason behind it is that commonly used wireless protocols rely on store-and-forward routing and costly overhead procedures to avoid interference. This article introduces RF-Zero-Wire, an RF-based symbol-synchronous communication protocol. Instead of relaying the whole frame per hop in a store-and-forward manner, nodes concurrently relay the frame symbol by symbol, without the need for tight time synchronization. Based on data collected in real-world experiments, we reveal that the inevitable carrier frequency offsets (CFOs) introduced by imperfect crystal oscillators cause a beating effect under concurrent symbol transmissions. This is characterized by periodic constructive and destructive interference, which significantly affects reliability. Subsequently, a thorough simulation study shows how the beating problem can be overcome with error correction codes. RF-Zero-Wire allows achieving an end-to-end latency of less than 1 ms for a small 4-byte frame transmitted across 5 hops. Moreover, latency is shown to increase only by 0.16% per extra hop for 16-byte frames, which is negligible compared to the over 100% per-hop latency increase observed in store-and-forward protocols. The trade-offs between network reliability and CFO range, communication distance, node density, and achievable data rate are studied in large-scale experiments based on simulation.

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network that features a miniature unmanned aerial vehicle (UAV) operating at terahertz (THz) frequencies and equipped with holographic surfaces to improve network performance. Unlike traditional reconfigurable intelligent surfaces (RIS), which serve as passive relays for signal reflection, this work introduces a novel concept: energy harvesting (EH) using reconfigurable holographic surfaces (RHS). These surfaces provide more powerful and focused energy delivery during wireless power transfer than RIS and are mounted on the miniature UAV. In this system, a source node enables the UAV to simultaneously receive both information and energy signals, with the harvested energy powering data transmission to a specific destination. The EE optimization problem involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV’s flight path while accounting for the unique characteristics of the THz channel. The problem is solved in two stages to maximize EE and meet a target transmission rate. The UAV trajectory is optimized using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. Simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements over baseline methods.

Hyperdimensional Computing (HDC) is an emerging AI algorithm, touted to be an efficient, neuro-inspired and reliable alternative to neural networks for Edge AI. HDC utilizes hypervectors with several thousand elements; the number of elements in these hypervectors denotes the HDC dimension. This dimension can be optimized for improving the efficiency and reliability of HDC inference against errors such as bit-flips, which can be caused by environmental radiation-induced soft errors. We hypothesize that, by reducing the runtime chip area and execution time utilized by HDC inference through lowering dimensionality, both efficiency and reliability against soft error-induced bit-flips can be simultaneously improved while trading off a negligible amount of accuracy and error threshold. We tested our hypothesis by executing an HDC inference algorithm with two different dimension values, 10000 (10k) and 1024, on a commercially available, low-power, bare-metal ARM platform with a Cortex-M4 processor. We conducted the efficiency analysis by measuring the CPU cycles and energy required for executing the algorithm, and the reliability analysis using real-world atmospheric-like neutron radiation from the ChipIr facility in Oxfordshire, UK. Analyses revealed that, by lowering the HDC dimension from 10k to 1024, the reliability of HDC inference against soft error-induced bit-flips was 3.5 times better and efficiency improved by more than 16 times. This innovative observation contrasts the prevailing understanding in the community that increasing the HDC dimension always improves robustness or reliability. To the best of our knowledge, our work is the first to study the reliability of HDC inference using real-world radiation.

This paper presents a novel stream allocation algorithm for semi-coherent Cell-Free Multi-User Multiple-Input Multiple-Output (CF-MU-MIMO) networks. In such networks, groups of phase-coherent Access Points (APs) are organized into clusters that operate coherently inside the clusters, while inter-cluster phase coherence is not maintained, reflecting practical limitations in achieving network-wide synchronization. The proposed algorithm is tailored for multi-cluster service, where multiple clusters can simultaneously serve each User Equipment (UE). To support this flexibility, different stream sequences are constructed for each UE, and each of the sequences is initialized and dynamically constructed based on the strongest links across all serving clusters and the UEs. During the allocation process, both inter-cluster and inter-stream interference are mitigated using projection-based techniques, thereby improving spatial multiplexing efficiency. Compared to the greedy stream allocation which is commonly adopted in the literature as a low-complexity benchmark due to its near-optimal performance in downlink MIMO systems, the proposed method achieves higher performance with only a modest increase in complexity. Furthermore, a significant enhancement in the achievable sum rate is demonstrated relative to single-user-single-cluster allocation strategies in distributed cell-free systems.

Over the years, advancements such as increased bandwidth, new modulation and coding schemes, frame aggregation, and the use of multiple antennas have been employed to enhance Wi-Fi performance. Nonetheless, as network density and the demand for low-latency applications increases, contention delays and retransmissions have become obstacles to efficient Wi-Fi deployment in modern scenarios. The introduction of Orthogonal Frequency-Division Multiple Access (OFDMA) in the IEEE 802.11 standard allows simultaneous transmissions to and from multiple users within the same transmission opportunity, thereby reducing the contention. However, the AP must efficiently manage the resource allocation, particularly in uplink scenarios where it lacks prior knowledge of users' buffer statuses, thus making polling a critical bottleneck in networks with a high number of users with sporadic traffic pattern. This paper addresses the polling problem and introduces the A2P algorithm, designed to enable scalable and efficient polling in high-density OFDMA-based time sensitive Wi-Fi networks. Simulation results show that A2P outperforms the alternative schemes by maintaining significantly lower delay and packet loss in dense time-sensitive teleconferencing scenario.

Joint Communication and Sensing (JCAS) is expected to play a critical role in next-generation wireless networks such as 6G. For complex sensing tasks, such as 3D pose estimation for virtual reality (VR) applications, accurate channel impulse response (CIR) or I/Q samples as well as processing using a neural network is required. Due to the higher bandwidth and antenna array sizes of future wireless networks, it is expected that offloading this data to a remote server for processing would require data rates in the order of 100s of Megabits per second, which is an unreasonable amount of overhead. Therefore it is necessary to preprocess the sensing data locally, and reduce the raw data to useful intermediary features, to mimimize the sensing data transmission overhead, especially when using multiple sensing devices. This paper proposes a method leveraging split inference to distribute neural networks across multiple devices, which achieves high accuracy while addressing the sensing data transfer bottleneck. We evaluate the performance of the proposed method in a VR gaming scenario, where mmWave Wi-Fi signals are used for 3D pose estimation. We show that split inference allows for reducing the communication overhead by three orders of magnitude compared to the centralised approach, while only losing 10% of accuracy. These results pave the way for future work, exploring highly distributed multi-static JCAS as a practical and efficient method of sensing.

Achieving high-quality wireless interactive Extended Reality (XR) will require multi-gigabit throughput at extremely low latency. The Millimeter-Wave (mmWave) frequency bands, between 24 and 300 GHz, can achieve such extreme performance. However, maintaining a consistently high Quality of Experience with highly mobile users is challenging, as mmWave communications are inherently directional. In this work, we present and evaluate an end-to-end approach to such a mmWave-based mobile XR system. We perform a highly realistic simulation of the system, incorporating accurate XR data traffic, detailed mmWave propagation models and actual user motion. We evaluate the impact of the beamforming strategy and frequency on the overall performance. In addition, we provide the first system-level evaluation of the CoVRage algorithm, a proactive and spatially aware user-side beamforming approach designed specifically for highly mobile XR environments.

Real-time applications (RTA) tend to play a crucial role in people's everyday life. Such applications are among the key use cases for the next generations of wireless technologies. RTA applications are characterized by strict guaranteed delay requirements (in the order of a few milliseconds). One of the pillars of enabling RTA in next-generation Wi-Fi standards is Restricted Target Wake Time (R-TWT), which provides Wi-Fi stations exclusive channel access within negotiated service periods (SPs). If each RTA data flow uses dedicated SPs for data transmission, they are completely isolated from each other and do not experience any contention. To ensure the satisfaction of RTA QoS requirements while minimizing the channel airtime consumption, it is important to properly select the R-TWT pa-rameters, namely the duration of SPs and the period between SPs. In this paper, we develop a mathematical model that estimates the delay probability distribution and packet loss probability for a given set of network, traffic and R-TWT parameters. Using this model, the access point can select the optimal R-TWT parameters for the given QoS requirements. The high accuracy of the model is proven by means of simulation.

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

This paper considers the effective power in downlink a multi-antenna, multi-user single-cell network enabled with simultaneous wireless information and power transfer (SWIPT). The proposed power efficiency problem aims to maximize the harvested energy and minimize transmission power consumption simultaneously. Specifically, the beamforming and antenna selection procedures at the receivers are optimized under minimum data rate requirements. The underlying optimization problem is shown to be an intractable nonlinear programming problem. As a result, a joint beamforming design and antenna selection is performed based on the scheduling chosen for information decoding and energy harvesting. The main problem is decomposed into two subproblems: antenna selection and beamforming, which yields a locally optimal solution. The first subproblem is solved based on the maximum channel gain across all antennas. While the second subproblem is solved via a two-layer iterative structure based on the sum of ratio programming. Simulation results show that the proposed scheme not only improves power efficiency but also enhances energy efficiency. The results also unveil an interesting tradeoff between power and energy efficiency.

As the need for stand-alone energy harvesting devices increases, the alleviation of the ecological and economic impact of their production and maintenance is possible by increasing battery life while reducing needed battery capacity.However, the increasing energy requirements of far-edge Artificial Intelligence and long-range wireless transmissions in the Internet of Things threaten to demand ever-larger battery capacities for such remote devices.Dynamic adaptation of device operation based on harvestable energy -i.e., energy awarenessis a proposed solution and can be implemented using an energy management unit.Standardizing this unit as a separate, active electronic component with standardized drivers can simplify overall system development and benefit existing devices.Hence, we propose a novel interface that allows decoupling this unit from the rest of the system, independent of the power management unit in use.As a first step, we developed a prototype that uses the proposed interface to make existing, solar energy-based, third-party devices energy-aware with provisions to be cross-compatible with differing power management units.The prototype was evaluated using an air quality sensing device and improved the overall device's transmission rate.

This paper focuses on leveraging a mobile edge computing (MEC) server at an access point (AP) to address the delay and reliability sensitivity requirement of multi-user machine-type communication (MTC). By offloading tasks to the MEC server, latency for low-power MTC devices can be minimized. Meanwhile, intelligent reflecting surfaces (IRSs) are supported to facilitate robust offloading, enhance spectrum efficiency, and improve coverage by influencing incident radio-frequency wave propagation via modifying the phase shifts with passive reflecting components. Therefore, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-user IRS-assisted MEC network, wherein a multi-antenna AP receives information symbols from a set of Internet of Things (IoT) users with short packet transmission. In particular, we minimize the system’s power utilization subject to offloading MTC-enabled IoT users’ quality of service (QoS) requirements, transmit power feasibility, capacity limitation, and IRS phase shift. The non-convex nature of the formulated problem poses a challenge to solving it effectively. To address this, we propose an efficient iterative algorithm based on successive convex approximation (SCA) and a penalty-based approach for handling unit-modulus constraints in the presence of passive reflecting elements at the IRS. Simulation results demonstrate the superior performance of our algorithm compared to other baseline schemes.

A batteryless Internet of Things (IoT) offers a sustainable alternative to battery-powered IoT devices, which produce billions of dead batteries every year. Devices are instead powered by a small supercapacitor, which is recharged by a renewable energy source. However, since IoT devices are often characterized by intermittent periods of high energy consumption followed by periods of reduced activity, conventional average energy consumption models cannot be used to assess if IoT devices can be powered by energy harvesters. Therefore, this article presents an alternative feasibility evaluation approach that focuses on modeling the worst case periods with peak energy consumption and short idle times, which pose the highest constraints on the capacitor’s behavior. This approach simplifies the characterization of the wireless technology energy consumption as these worst case periods can be determined by a few parameters. The methodology is then applied to combinations of popular IoT technologies (LoRaWAN, BLE Mesh, and 6TiSCH) and energy sources (solar, kinetic, and radio frequency energy) for two common IoT use cases. We show that the proposed parameters can be successfully extracted with power measurements for different network configurations and that the Power Management Unit configuration has a nonnegligible impact on the communication requirements. Finally, we discuss how to apply the model to other technologies and other use cases.

Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). “Cutting the wire” in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna’s orientation. As such, beamforming must adapt, in real-time, to the user’s head rotations. An HMD’s built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using sub-arrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.

Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will “track” the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as a suitable candidate for near-term movement trajectory prediction for natural human mobility, and have also recently been shown as applicable in predicting VR users' mobility under the constraints of redirected walking. In this work, we extend these initial findings by showing that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature in comparison to the more traditional utilization of solely the historical physical movements of the VR users. Finally, we show that the prediction system trained on a static number of coexisting VR users be scaled to a multi-user system without significant accuracy degradation.

The$6^{th}$generation of wireless communication (6G) is envisioned to give rise to various technologies for improving the end-to-end communication performance, where the commu-nication is envisioned to utilize wireless signals in the millimeter wave (mmWave) frequencies and above. Among others, these technologies comprise Intelligent Reflective Surfaces (IRSs) and Mobile Relays (MRs), whose envisaged roles include mitigating the negative effects of Non-Line-of-Sight (NLoS) connectivity, in particular at mm Wave and higher frequencies. The core idea behind these technologies is to use cooperative networking where the source sends a signal to a repeater, in this case the IRS or the MR, which is upon reception forwarded to the destination. When comparing the two technologies, it is important to realize that the IRSs are primarily envisioned to be static entities attached to various objects in the environment such as walls and furniture. In contrast, the MRs will feature a higher degree of freedom, as they will be able to position themselves seamlessly in the environment. Based on the above assumptions, we derive an approach for determining the optimal position of the IRS and MR in indoor environments, i.e., the one that maximizes the end-to-end link quality between the source and the destination. We follow by capturing the communication quality indicators for both IRS- and MR-supported NLoS avoidance in indoor mm Wave communication in a number of scenarios. Our results show that, from the end-to-end link quality perspective, the MRs generally outperform the IRSs, suggesting their utilization potential for throughput-optimized NLoS avoidance scenarios.

Tiny batteryless Internet of Things (IoT) devices that depend on the harvested energy from their environment provide a promising alternative for a sustainable IoT vision. These devices use small capacitors as energy storage, which together with the unpredictable and dynamic harvesting environment results in intermittent on–off behavior of the device. The crucial issue to effectively use batteryless IoT devices is to find a way of enabling the successful execution of application tasks in face of this intermittency. As the conventional computing models cannot handle this behavior, in this article, we present an energy-aware task scheduler for batteryless IoT devices based on dependencies and priorities, which can intelligently schedule the application tasks avoiding power failures and maintaining forward progress. With the properly defined voltage thresholds for each application task, using our energy-aware task scheduler a safer execution can be ensured. We evaluate our approach based on emulated and real experiments and validate it using two types of power management units (PMUs) (environment emulator and intelligent PMU based on the AEM10941 chip). Our results show that the energy-aware task scheduler is able to react and adapt the execution to environmental changes, avoiding power failures. Comparing to the state-of-the-art scheduling approaches, which are mostly not aware of the energy, we show that our energy-aware task scheduler can keep the device on during the full time of the experiment, executing more tasks when a relatively small capacitor of 10 mF or less is used at harvesting currents as low as$40 ~\mu \text{A}$.

Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism.

Full-immersive multiuser Virtual Reality (VR) setups envision supporting seamless mobility of the VR users in the virtual worlds, while simultaneously constraining them inside shared physical spaces through redirected walking. For enabling high data rate and low latency delivery of video content in such setups, the supporting wireless networks will have to utilize highly directional communication links, where these links will ideally have to “track” the mobile VR users for maintaining the Line-of-Sight (LoS) connectivity. The design decisions about the mobility patterns of the VR users in the virtual worlds will thus have a substantial effect on the mobility of these users in the physical environments, and therefore also on performance of the underlying networks. Hence, there is a need for a tool that can provide a mapping between design decisions about the users' mobility in the virtual words, and their effects on the mobility in constrained physical setups. To address this issue, we have developed and in this paper present a simulator for enabling this functionality. Given a set of VR users with their virtual movement trajectories, the outline of the physical deployment environment, and a redirected walking algorithm for avoiding physical collisions, the simulator is able to derive the physical movements of the users. Based on the derived physical movements, the simulator can capture a set of performance metrics characterizing the number of perceivable resets and the distances between such resets for each user. The simulator is also able to indicate the predictability of the physical movement trajectories, which can serve as an indication of the complexity of supporting a given virtual movement pattern by the underlying networks.

Many IoT scenarios, such as smart cities, wild life monitoring, or smart agriculture, involve thousands of battery-powered devices. The disposal and replacement of such batteries represent an important economical and environmental cost. To realize Green IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest power from renewable sources, such as solar or wind energy and store it in much more sustainable capacitors. The limited and inconstant energy supply and the limited energy storage capacity of such devices, however, require special care in the design of communication and computational processes, which have a major impact on the energy consumption of the devices. In this work, we explore multiple elements that could affect the device energy and communication capabilities of LoRaWAN devices. We propose and compare different energy-aware packet transmission algorithms, and test them in a scenario where values for the harvested power are collected from real testbeds. We show that the number of successfully transmitted packets can be doubled by using an energy-aware design approach.

Tiny energy harvesting battery-less devices present a promising alternative to battery-powered devices for a sustainable Internet of Things (IoT) vision. The use of small capacitors as energy storage, along with a dynamic and unpredictable harvesting environment, leads these devices to exhibit intermittent on-off behavior. As the traditional computing models cannot handle this behavior, in this demo we present and demonstrate an energy-aware task scheduler for battery-less IoT devices based on task dependencies and priorities, which can intelligently schedule the application tasks avoiding power failures.

Bluetooth Low Energy (BLE) is emerging as an Internet of Things (IoT) technology that effectively connects small devices and sensors. It can enable many smart building use cases such as automation and control, environmental condition monitoring, and indoor location services. The BLE mesh standard provides a friendship feature to support Low Power Nodes (LPNs). We demonstrate how these BLE LPNs can support communication (uplink, downlink, or bidirectional) when powered by ambient indoor light using a mini solar panel and a small capacitor for energy storage. Being batteryless, it can exhibit intermittent behaviour with periodic ON and OFF states. However, with the knowledge of the capacitor voltage, an energy-aware LPN can try to avoid the OFF state. It can delay the execution of upcoming tasks by switching to an SLEEP state (consuming minimum energy) and provide some time to recharge the capacitor. We consider an example use case of monitoring temperature and room occupancy. The mesh nodes in the network can send instructions (such as turn-on an LED or a buzzer) to the batteryless LPN that should be executed by it. We study the use-case with real experiments on the communication feasibility of an energy-aware BLE LPN in a network and characterize the capacitance behaviour by placing a 6 W light bulb at 120 cm from the solar panel.

Flow-guided electromagnetic nanonetworks will enable innovative medical applications for monitoring, information gathering, and data transmission inside the human body. These nanonetworks will have to operate under extreme computational and powering-related constraints, and in very hostile environments inside human vascular systems. Under these circumstances, successful transmissions between in-body nanonodes and an on-body nanorouter rarely occur, thus requiring new approaches to improve the network throughput in this scenario. Along this view, in classical flow-guided nanonetworks the nanonodes are envisioned to transmit packets if they have enough energy for the transmission, regardless of their vicinity to the nanorouter. In this paper, we propose a nanorouter awareness model that can provide significant throughput gains compared to the baseline based on blind transmissions, facilitating the roll-out of nanocommunication-supported medical applications.

The numerous applications of industrial automation have always posed many challenges for wireless connectivity. In the last decade, IEEE 802.15.4e time-slotted channel hopping (TSCH) networks have provided high reliability and low-power operation in such challenging industrial environments. Typically, TSCH networks employ one modulation at the physical layer and are thus limited by the characteristics of the chosen modulation in terms of, among others, data rate, reliability and energy efficiency. To tackle these limitations and to improve network performance and flexibility in those challenging industrial environments, this work explores the simultaneous use of multiple modulations in a TSCH network. Traditionally, TSCH relies on fixed-duration slots, large enough to send a packet of any size given the fixed data rate. In order to avoid wasting airtime when simultaneously using modulations with different data rates, we propose the concept of slot bonding. This allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen modulation. To analyze the proposed slot bonding technique, we formally describe the TSCH slot bonding problem in terms of optimizing the packet delivery ratio while minimizing radio on time, with the inclusion of parent selection and interference avoidance. Afterward, we propose a genetic algorithm that allows us to implement the problem and find solutions heuristically. Finally, we provide insights into preferred parent selection and modulation configurations by using this heuristic approach during extensive simulation experimentation in which the scalability advantage of slot bonding over longer fixed-duration slots is also shown.

Billions of Internet-of-Things (IoT) devices are deployed worldwide and batteries are their main power source. However, these batteries are bulky, short lived, and full of hazardous chemicals that damage our environment. Relying on batteries is not a sustainable solution for future IoT. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in an intermittent on-off behavior. LoRaWAN is a popular low-power wide-area network technology used in many IoT devices and can be used in these new scenarios. In this work, we present a Markov model to characterize the performance of batteryless LoRaWAN devices for uplink and downlink (UL/DL) transmissions and we evaluate their performance in terms of parameters that define the model (i.e., device configuration, application behavior, and environmental conditions). Results show that LoRaWAN batteryless communications are feasible if choosing the proper configuration (i.e., capacitor size and turn-on voltage threshold) for different application behavior [i.e., transmission interval, UL/DL packet sizes (PSs)], and environmental conditions (i.e., energy harvesting rate). Since DL in the second reception window highly affects the performance, only small DL PSs should be considered for these devices. Besides, a 47-mF capacitor can support 1 B SF7 transmissions every 60 s at an energy harvesting rate of 1 mW. However, if no DL is expected, a 4.7-mF capacitor could support 1 B SF7 transmissions every 9 s.

Narrowband Internet of Things (NB-IoT) is becoming one of the most promising low-power wide area (LPWA) networking technologies. It can support more than 50 000 devices within a cell using licensed spectrum. NB-IoT provides low energy consumption, reliable connectivity and deep indoor coverage for the device, making it a good candidate for IoT use cases. NB-IoT introduces two novel energy-saving techniques, namely, extended discontinuous reception (eDRX) and power saving mode (PSM). This article presents a Markov chain model to evaluate the power consumption and latency of NB-IoT devices using PSM and eDRX. By exploiting the characteristics of the steady-state distribution of the Markov chain, the probabilities in steady-state can be obtained explicitly. Based on these probabilities, we calculate the system downlink (DL) latency as a function of different timers of these power-saving features. We also compare the model to simulation results obtained from the ns-3 event-based network simulator, to determine its accuracy. The results show that its performance in terms of energy and latency is comparable. Our model is accurate with consideration of the protocol details and the new radio resource control (RRC) Idle features of NB-IoT. The results show that the analytical model achieves an average accuracy of more than 91% for power consumption and DL latency. Lastly, we use the model to automatically determine the optimal parameter set in terms of latency and power consumption for various IoT use cases with different traffic requirements, based on multiobjective analysis of the Pareto front.

Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.

Location information is among the main enablers of context-aware applications and wireless networks. Practical localization services are able to generate location estimates that are generally erroneous. To maximize its usability and benefits, each location estimate should be leveraged jointly with the corresponding estimate of its localization error. Hence, we propose an Artificial Neural Network (ANN)-based method for the estimation of individual localization errors. We do that for fingerprinting, one of the most prominent localization solutions for GPS-constrained environments. First, we provide insights on how to optimally hyperparameterize the proposed method. We do that by exploring its hyperparameter' space in order to find its close-to-optimal hyperparameterization for different environments and fingerprinting technologies. We believe the provided insights can serve to reduce the overhead of deploying the method in new environments. Second, we demonstrate that the method, when hyperparameterized according to the provided insights, substantially outperforms the current state-of-the-art. The improvement is more than 25% in the best case scenario.

IEEE 802.11ah is a novel sub-GHz license-free WiFi technology that is of great interest for Smart Cities, primarily due to its long range, low energy consumption, reliability, and ubiquitousness. To guarantee a long lifetime of battery-powered IEEE 802.11ah Mobile Terminals (MTs), network discovery based on beacon listening or active probing should ideally be avoided. Alternative discovery approaches heavily rely on crowd-sourcing, which in turn relies on propagation modeling. Due to the novelty of IEEE 802.11ah, the accuracy of the available propagation models is currently all but clear, and this paper makes one of the first steps in bridging this gap. Specifically, for a number of Smart City-relevant environments we experimentally evaluate the accuracy of an exhaustive set of propagation models by comparing their outputs against real measurements obtained using IEEE 802.11ah-compatible hardware. Our results are encouraging, showing that the existing models are indeed suitable for IEEE 802.11ah. However, they also indicate that diverse models perform best in distinct types of deployment environments, suggesting the need for an environment-tailored design of IEEE 802.11ah-based systems that utilize propagation modeling.

The need for cables with high-fidelity Virtual Reality (VR) headsets remains a stumbling block on the path towards interactive multi-user VR. Due to strict latency constraints, designing fully wireless headsets is challenging, with the few commercially available solutions being expensive. These solutions use proprietary millimeter wave (mmWave) communications technologies, as extremely high frequencies are needed to meet the throughput and latency requirements of VR applications. In this work, we investigate whether such a system could be built using specification-compliant IEEE 802.11ad hardware, which would significantly reduce the cost of wireless mmWave VR solutions. We present a theoretical framework to calculate attainable live VR video bitrates for different IEEE 802.11ad channel access methods, using 1 or more head-mounted displays connected to a single Access Point (AP). Using the ns-3 simulator, we validate our theoretical framework, and demonstrate that a properly configured IEEE 802.11ad AP can support at least 8 headsets receiving a 4K video stream for each eye, with transmission latency under 1 millisecond.

Dynamic Spectrum Access allows using the spectrum opportunistically by identifying wireless technologies sharing the same medium. However, detecting a given technology is, most of the time, not enough to increase spectrum efficiency and mitigate coexistence problems due to radio interference. As a solution, recognizing traffic patterns may lead to select the best time to access the shared spectrum optimally. To this extent, we present a traffic recognition approach that, to the best of our knowledge, is the first non-intrusive method to detect traffic patterns directly from the radio spectrum, contrary to traditional packet-based analysis methods. In particular, we designed a Deep Learning (DL) architecture that differentiates between Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) traffic, burst traffic with different duty cycles, and traffic with varying rates of transmission. As input to these models, we explore the use of images representing the spectrum in time and time-frequency. Furthermore, we present a novel data randomization approach to generate realistic synthetic data that combines two state-of-the-art simulators. Finally, we show that after training and testing our models in the generated dataset, we achieve an accuracy of ≥ 96 % and outperform state-of-the-art methods based on IP-packets with DL.

Low-power wireless mesh networks provide connectivity for a wide range of applications in industrial scenarios. For many years, IEEE 802. 15.4e Time-Slotted Channel Hopping (TSCH) networks have proven their efficiency in such environments, providing high reliability and low-power operation. TSCH networks run on top of one physical (PHY) layer and are thus limited by the characteristics of the chosen PHY layer in terms of, among others, data rate, reliability and energy efficiency. To tackle these limitations and to improve network performance and flexibility in those challenging industrial environments, this work explores the simultaneous use of multiple PHYs, and more specifically multiple modulation and coding schemes (MCSs), in a TSCH network. Traditionally, TSCH relies on fixed-duration slots, large enough to send a packet of any size given the fixed data rate. In order to avoid wasting airtime when simultaneously using multiple PHYs or MCSs with different data rates, we first introduce the concept of slot bonding. This allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, we formally describe TSCH slot bonding using a Mixed Integer Linear Program (MILP) model. Finally, we use this model to determine the optimal MCS configuration with a short slot frame length that causes network saturation and show the scalability advantage of slot bonding in terms of packet delivery ratio.

Wireless networks are ubiquitous in today’s world and consist of an ever-expanding number of heterogeneous consumer devices and communication technologies. As modern devices support multiple communication technologies, efforts have been made to efficiently manage this plethora of technologies by supporting functionalities such as simultaneous usage or handovers.However, existing solutions are missing both the fine-grained control and intelligence to offer seamless inter-technology management and network optimizations. As a result, technologies operate in an isolated manner, network management is inefficient, and the requirements of users and modern applications are not being met. In contrast, we present intelligent and dynamic multi-technology network management that breaks this isolation, abstracting the connectivity decisions from the user and application level. Different contributions are made: first of all, we introduce a framework for inter-technology management that enables, among others, seamless handovers and packet-based load balancing. Next, we propose different algorithms that can be deployed on top of the novel, or existing, management solutions to increase the network-wide throughput by providing more intelligent network configurations, taking into account mobility and real-time requirements. Finally, as management approaches rely on an accurate overview of the network state, we also consider the monitoring aspect and investigate the detection of traffic patterns in the radio spectrum. Our contributions are evaluated through practical implementations in real-life prototypes.

The performance of wireless networking approaches degrades under user's mobility. To objectively and reliably establish their performance under mobility, one has to guarantee highly repeatable experimentation with minimized external influences, which is currently a burdensome manual process for 3-dimensional (3D) environments. To address this issue, we propose a drone-based testbed for automated, autonomous, and repeatable experimentation with mobile wireless infrastructures in heterogeneous 3D environments. The developed testbed can be easily deployed in various environments, allows for simple integration of a new System Under Test (SUT), and guarantees the absence of interference with the SUT.

Multi-Radio Access Technology (RAT) IoT devices are able to combine the high coverage of Low-Power Wide-Area (LPWA) technologies with the higher data-rates of shorter range technologies such as IEEE 802.11ah. In such scenarios, a discovery procedure has to be used for detecting the availability of a IEEE 802.11ah network. Currently, these procedures consume substantial energy, as the discovery has to be periodically performed, even if the IEEE 802.11ah technology is not available, which is undesirable for low-power Internet of Things (IoT) devices. We propose using the device's location information for making more optimized discovery and handover decisions. We demonstrate the feasibility of this approach in performing energy efficient handovers between various LPWA technologies and IEEE 802.11ah based on estimated location. We carry out our evaluation in terms of the energy consumption of the procedure and the duration of the device's association to IEEE 802.11ah. We show that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons.

The Internet of Things (IoT) already connects billions of devices and keeps growing exponentially. These devices are designed to be integrated with industrial machines, home appliances, and infrastructures. One such use case is to build a smart grid management system that relies on demand-response techniques to control the appliances automatically so that the power can be distributed optimally. The mission-critical smart grid communications require secure, reliable, two-way communicable, and latency bounded connections between the management system and the electrical appliances. To realize these, cellular technology is arguably the most feasible solution. 3GPP has already released the narrowband-IoT (NB-IoT) standards as the low-power dense-area coverage IoT cellular solution. In this article, we present an NB-IoT system to monitor and control the connected electrical appliances in a smart grid network. The platform is also capable of configuring the network dynamically. We assess the latency performance for our solution using the commercially available Orange network in Belgium. It is observed that NB-IoT enabled devices can be controlled and monitored with a maximum latency less than 8 s in the deep-indoor environment and within 2 s for the outdoor environment.

From the outset, batteries have been the main power source for the Internet of Things (IoT). However, replacing and disposing of billions of dead batteries per year is costly in terms of maintenance and ecologically irresponsible. Since batteries are one of the greatest threats to a sustainable IoT, battery-less devices are the solution to this problem. These devices run on long-lived capacitors charged using various forms of energy harvesting, which results in intermittent on-off device behaviour. In this work, we model this intermittent battery-less behaviour for LoRaWAN devices. This model allows us to characterize the performance with the aim to determine under which conditions a LoRaWAN device can work without batteries, and how its parameters should be configured. Results show that the reliability directly depends on device configurations (i.e., capacitor size, turn-on voltage threshold), application behaviour (i.e., transmission interval, packet size) and environmental conditions (i.e., energy harvesting rate).

A variety of nanoscale applications require downlink and broadcast-based transmission of control packets from a powered transmitter to energy-harvesting nanonodes with constrained storage capacity. The nanonode's communication system is anticipated to be a bottleneck for such nanonetworks, hence an accurate modeling of its energy consumption is needed. Currently, TS-OOK is a prevailing scheme for nanocommunication in the terahertz (THz) frequencies, with short pulses representing logical "1"s and silences logical "0"s. In the energy modeling of this scheme, certain energy consumptions are attributed to the transmission and reception of the pulses. However, traditional communication systems teach us that the idling energy consumption should not be neglected. Hence, we provide an energy consumption model for TS-OOK-based nanocommunication systems that accounts for the energy consumed in idling, in addition to the transmission and reception-originated consumptions. We demonstrate that, for a meaningful performance of the considered nanonetwork, the nanonode's idling energy consumption has to be at least nine orders of magnitude lower than the corresponding energy consumption in reception. To increase the tolerable idling energy, we propose a new energy lifecycle for the receiving nanonodes. Assuming frequent packet repetitions on the transmit side, the proposed lifecycle utilizes periodic short wake-ups of the receiving nanonodes. We show that, when the proposed lifecycle is utilized, up to three orders of magnitude higher idling energy consumption can be tolerated compared to the baseline.

Location information is a valuable source of context that can be utilized by end-user applications and wireless networks to optimize their performance and usability. When used, location information should ideally be considered jointly with the estimate of its accuracy. Most of the current approaches for estimating the accuracy rely on performing a static performance benchmark of a localization solution in a deployment environment, which fails to capture the dynamic nature of the environment. We address this problem for fingerprinting-based localization by grounding the estimation of localization errors on the low-level features, i.e. the RSS values from APs used in fingerprinting. We use these low-level features measured at different locations in an environment, as well as their respective localization errors, to train different regression models, allowing us to predict the localization errors at new locations, given new observed values of the low-level features at these locations. Our evaluation results show substantially better performance of the proposed regression-based estimation of localization errors compared to static performance benchmarks.

Narrowband Internet of Things (NB-IoT) is gaining prominence as a key Low Power Wide Area Network (LPWAN) technology for IoT applications. Since it operates on licensed frequency spectrum it can provide guarantees to applications demanding Quality of Service (QoS). NB-IoT has emerged as a competitive rival for other LPWAN technologies such as LoRa and Sigfox, which work in the unlicensed frequency spectrum and are vulnerable to interference. Therefore, NB-IoT is the trivial fit for industries and other business companies that demand guaranteed services. In this paper the different features of the NB-IoT technology have been studied on the commercial Orange network in Belgium using the ublox SARA-N210 module [1] as the user equipment (UE). We focused on the device and network performance in terms of setup times, signal quality, throughput, latency, and reliability and studied the network dynamicity on signal strength. These observations are then compared with the theoretical defined limits of NB-IoT.

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

Internet of Things (IoT)-based solutions for sport analytics aim to improve performance, coaching, and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g., LTE and Wi-Fi). In this article, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed, and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multihop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g., a moving car following the cyclists). In this article, we present a detailed technical description of this solution, describing its requirements, options, and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.

The new medium access method of IEEE 802.11ah, called restricted access window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this article, we present a surrogate model that can accurately predict RAW performance under a given RAW configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10(17) possible data points. This is too big to achieve feasible training convergence. In this article, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10(5) data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.

Today's electronic devices have multiple communication technologies available at any time. Currently, the application layer or the user needs to manually switch between them, depending on the networks in range. No holistic and adaptive approach exists that can manage all technologies and devices at once. To this extent, we previously proposed the inter-technology management framework ORCHESTRA. The framework is the first of its kind in providing fine-grained packet-level control across different network technologies in a network-wide manner. In this paper, we present a real-life implementation of this framework and show that it can cope well with mobility requirements of users by offering seamless inter-technology handovers.

The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

Modern connected devices are equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Current network management solutions fail to provide a fine-grained, coordinated, and transparent answer to this heterogeneity, while the lower layers of the OSI stack simply ignore it by providing full separation of layers. To address this, we propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless networks and introduce capabilities such as packet-level dynamic and intelligent handovers (both interand intra-technology), load balancing, replication, and scheduling. The framework is the first of its kind in providing a fine-grained packet-level control across different technologies by introducing a fully transparent virtual medium access control layer and an software-defined networking-like controller with global intelligence. Furthermore, we present a novel optimization problem formulation that can be solved to optimally configure the network. We provide a thorough evaluation through simulations and a prototype implementation. We show that our framework enables, in a real-life setting, transparent and realtime inter-technology handovers and that coordinated load balancing can double the network-wide throughput across different scenarios.

Today's local area networks consist of an ever-expanding number of heterogeneous consumer devices and communication technologies. Despite supporting multiple technologies, those devices tend to connect to the Internet using a single technology, based on predefined priorities. This static behavior does not allow the network to unlock its full potential, which becomes increasingly more important as the quality of service (QoS) requirements of services grow. Moreover, existing approaches make use of theoretical models that assume, unrealistically, full knowledge of the network. To this extent, we present a multi-technology flow-management load balancing framework that dynamically re-routes traffic through heterogeneous networks, in order to maximize the global throughput, based on changing network conditions and QoS demands. Along a problem formulation, we focus on the estimation of wireless and dynamic network characteristics and provide a thorough evaluation through simulations and a prototype implementation. We show that our framework is indeed capable of responding to dynamic network events in real-time and offers an increased overall throughput by optimally using the network's capacity. This results in a throughput increase of around 20 % on average.

Current mobile consumer devices are equipped with the ability to connect to the Internet using a variety of heterogeneous wireless network technologies (e.g., Wi-Fi and LTE). These devices generally opt to statically connect using a single technology, based on predefined priorities. This static behavior does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of for example throughput and reliability, grow. Multipath TCP (MPTCP) is a solution that allows the simultaneous use of multiple network interfaces. However, it does this uncoordinated for a single connection between two endpoints. Therefore, this paper proposes a Software-Defined Networking (SDN) architecture to enable coordinated multi-path routing across the several networks for mobile devices. Moreover, we propose a novel weighted MPTCP scheduler that allows the transmission of certain controllable percentages of data per network interface. The proposed idea is evaluated through a real-life prototype implementation with a smartphone.

Local area networks (LANs) are employed by a plethora of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Existing solutions and the lower layers of the OSI stack are unfit to cope with this heterogeneity. For instance, dynamical inter-technology switching is user-of application-based. We propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless local area networks (WLANs) and introduce capabilities such as packet-level dynamic and intelligent handovers (both inter- and intratechnology), load balancing, replication, and scheduling. The framework consists of a controller that is capable of communicating with both existing Software-Defined Networking (SDN) and Network Function Virtualization (NFV) controllers and with devices containing a newly introduced virtual Medium Access Control (MAC) layer. We show that the virtual MAC enables transparent and real-time inter-technology handovers and that our solution scales up to two thousands of clients.

IEEE 802.11ah is the new sub-1 GHz Wi-Fi standard, targeting large-scale and dense deployments of low-power stations. One of its major improvements compared to previous 802.11 standards, is its ability to scale to thousands of stations per access point. Cost-effective evaluation at such a scale is only possible using simulation, which requires realistic path loss models and hardware parameters. In this paper, we evaluate seven path loss models, based on a large scale sub-urban measurement campaign, including macro line-of- sight (LoS), pico LoS, and pico non-LoS with different as well as equal antenna height deployments. For each of the four resulting scenarios, the most accurate model is determined and used in combination with radio transceiver parameters obtained from actual 802.11ah station hardware to determine MAC-layer throughput and packet loss as a function of distance. The standard promises a range of up to 1 km at 150 kbps. Our results paint a less optimistic picture. When using realistic hardware parameters ranges up to 450 and 130 m can be achieved for a near LoS macro and pico deployment scenario respectively. For the non-LoS pico scenario ranges of 80 and 150 m can be achieved for transmitter at height 12 m and transmitter at heights 1.5 m respectively. With an ideal hardware configuration that operates at the maximum allowed transmission power, this could ideally be increased to 1700, 490, 300 and 550 m respectively.

The current local area networks (LANs) are occupied by a large variety of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different network technologies (e.g., Ethernet, 2.4 and 5GHz Wi-Fi). Nevertheless, devices generally opt to statically connect using a single technology, based on predefined priorities. This static behaviour does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of latency and throughput, grow. In this paper we present a real-life SDN-based implementation of our previously proposed algorithm that addresses this problem.

Today's local area networks (LANs) consist of a plethora of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different network technologies (e.g., Ethernet, Power-Line, 2.4 and 5GHz Wi-Fi). Nevertheless, devices generally opt to statically connect using a single technology, based on predefined priorities. This static behaviour does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of latency and throughput, grow. To address this issue, we present a load balancing algorithm that dynamically selects a suitable interface and path through the network for each flow, based on service requirements, bandwidth availability and current link quality. The goal of the algorithm is to find an optimal path configuration for all the flows across the network that maximizes the global throughput. It dynamically adapts to changing network conditions, the arrival and departure of flows and link failures. The problem is formulated as a Mixed Integer Linear Program (MILP) and its solution, as well as that of a faster heuristic algorithm, is extensively tested in a series of ns-3 simulations with different network topologies and flow configurations. We differentiate from existing work by estimating flow rates and dynamic network conditions using real-time monitoring information, taking into account the shared medium of wireless networks and the specific fairness behaviour of TCP. Results show an increase in throughput up to 70% in heterogeneous LANs under dynamic network conditions.

IEEE 802.11ah is a new Wi-Fi standard operating on unlicensed sub-GHz frequencies. It aims to provide long-range connectivity to Internet of Things (IoT) devices. The IEEE 802.11ah restricted access window (RAW) mechanism promises to increase throughput and energy efficiency in dense deployments by dividing stations into different RAW groups and allowing only one group to access the channel at a time. In this demo, we demonstrate the ability of the RAW mechanism to support a large number of densely deployed IoT stations with heterogeneous traffic requirements. Differentiated Quality of Service (QoS) is offered to a small set of high-throughput wireless cameras that coexist with thousands of best-effort sensor monitoring stations. The results are visualized in near real-time using our own developed IEEE 802.11ah visualizer running on top of the ns-3 event-based network simulator.

Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment and performance comparable to the full reference counterpart, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video set) obtaining a correlation between 78% and 91% to the FR benchmark (the video quality metric). Due to its unsupervised learning essence, our method is flexible and dynamically adaptable to new content and scalable with the number of videos.

Network function virtualization (NFV) and software defined networking (SDN) have been proposed to increase the cost-efficiency, flexibility, and innovation in network service provisioning. This is achieved by leveraging IT virtualization techniques and combining them with programmable networks. By doing so, NFV and SDN are able to decouple the network functionality from the physical devices on which they are deployed. Service function chains (SFCs) composed out of virtual network functions (VNFs) can now be deployed on top of the virtualized infrastructure to create new value-added services. Current NFV approaches are limited to mapping the different VNF to the physical substrate subject to resource capacity constraints. They do not provide the possibility to define location requirements with a certain granularity and constraints on the colocation of VNF and virtual edges. Nevertheless, many scenarios can be envisioned in which a service provider (SP) would like to attach placement constraints for efficiency, resilience, legislative, privacy, and economic reasons. Therefore, we propose a set of affinity and anti-affinity constraints, which can be used by SP to define such placement restrictions. Furthermore, a semantic SFC validation framework is proposed that allows the virtual network function infrastructure provider (VNFInP) to check the validity of a set of constraints and provide feedback to the SPs. This allows the VNFInP to filter out any non-valid SFC requests before sending them to the mapping algorithm, significantly reducing the mapping time.

Optical Wireless (OW) technologies deploying narrow multiwavelength light beams offer a promising alternative to traditional wireless indoor communications as they provide higher bandwidths and overcome the radio spectrum congestion typical of the 2.4 and 5GHz frequency bands. However, unlocking their full potential requires exploring novel control and management techniques. Specifically, there is a need for efficient and intelligent resource management and localization techniques that allot wavelengths and capacity to devices. In this paper we present a resource allocation model for one such indoor optical wireless approach, a Beam-steered Reconfigurable Optical-Wireless System for Energy-efficient communication (BROWSE). BROWSE aims to supply each user within a room with its own downstream infrared light beam with at least 10Gbps throughput, while providing a 60GHz radio channel upstream. Using Integer Linear Programming (ILP) techniques, we have designed and implemented a resource allocation model for the BROWSE OW downstream connection. The designed model optimises the trade-off between energy-consumption and throughput, while providing TDM capabilities to effectively serve densely deployed devices with a limited number of simultaneous available wavelengths. Through several test-scenarios we have assessed the model's performance, as well as its applicability to future ultra-high bandwidth video streaming applications.

IEEE 802.11ah is a new Wi-Fi draft for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT): connectivity among a large number of power-constrained stations deployed over a wide area. The new Restricted Access Window (RAW) mechanism promises to increase throughput and energy efficiency by dividing stations into different RAW groups. Only the stations in the same group can access the channel simultaneously, which reduces collision probability in dense scenarios. However, the draft does not specify any RAW grouping algorithms, while the grouping strategy is expected to severely impact RAW performance. To study the impact of parameters such as traffic load, number of stations and RAW group duration on optimal number of RAW groups, we implemented a sub-1Ghz PHY model and the 802.11ah MAC protocol in ns-3 to evaluate its transmission range, throughput, latency and energy efficiency in dense IoT network scenarios. The simulation shows that, with appropriate grouping, the RAW mechanism substantially improves throughput, latency and energy efficiency. Furthermore, the results suggest that the optimal grouping strategy depends on many parameters, and intelligent RAW group adaptation is necessary to maximize performance under dynamic conditions. This paper provides a major leap towards such a strategy.

In the media production process a substrate network can be shared by many users simultaneously when different media actors are geographically distributed. This allows sophisticated media productions involving numerous producers to be concurrently created and transferred. Due to the predictable nature of media transfers, the collaboration among different actors could be significantly improved by deploying an efficient advance reservation system. In this paper, we propose a model for the advance bandwidth reservation problem, which takes the specific characteristics of media production networks into account. Flexible and time variable bandwidth reservations, meeting delivery deadlines, supporting splittable flows and interdependent transfers and all types of advance reservation requests imposed by the media production transfers are incorporated into this model. In addition to the optimal scheduling algorithms , which are presented based on this model, near optimal alternatives are also proposed. The experimental results show that the proposed algorithms are scalable in terms of physical topology and granularity of time intervals and obtain a satisfactory performance, executing significantly faster than an optimal algorithm and within 8.78% of the optimal results.

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance.

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today’s heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients’ Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time.

In recent years a lot of new consumer devices have been introduced to the home network. Modern home networks usually consists of multiple heterogeneous communication technologies such as Ethernet, Wi-Fi and power-line communications. Today, the user has to manually decide which transmission technology to use as there is no automated optimization across technologies. Load balancing algorithms can improve overall throughput while redundant links also provide the opportunity to switch flows in case of link failures. Current standards either lack real implementation in consumer devices or do not have the flexibility to support all necessary functionality towards creating a convergent hybrid home network. Therefore, we propose an alternative way by using Software-Defined Networking techniques to manage a heterogeneous home network. In this paper we specifically evaluate the ability of OpenFlow-enabled switches to perform link switching both under normal conditions and in case of link failures. Our results show that SDN-based management can be used to improve heterogeneous home networks by utilising redundant links for flow rerouting. However, they also show that improvements are still needed to reduce downtime during link failure or rerouting in case of TCP traffic.

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results.

The popularity of multimedia services offered over the Internet have increased tremendously during the last decade. The technologies that are used to deliver these services are evolving at a rapidly increasing pace. However, new technologies often demand updating the dedicated hardware (e.g., transcoders) that is required to deliver the services. Currently, these updates require installing the physical building blocks at different locations across the network. These manual interventions are time-consuming and extend the Time to Market of new and improved services, reducing their monetary benefits. To alleviate the aforementioned issues, Network Function Virtualization (NFV) was introduced by decoupling the network functions from the physical hardware and by leveraging IT virtualization technology to allow running Virtual Network Functions (VNFs) on commodity hardware at datacenters across the network. In this paper, we investigate how existing service chains can be mapped onto NFV-based Service Function Chains (SFCs). Furthermore, the different alternative SFCs are explored and their impact on network and datacenter resources (e.g., bandwidth, storage) are quantified. We propose to use these findings to cost-optimally distribute datacenters across an Internet Service Provider (ISP) network.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the client's bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming.

Over the past decades, the importance of multimedia services such as video streaming has increased considerably. Since streaming protocols such as Real Time Streaming Protocol (RTSP) and Real Time Transport Protocol (RTP) require server-side bit-rate adaptation schemes, they are not ideally suited to deal with highly heterogeneous and dynamically changing network conditions. Therefore, research shifted towards client-side adaptation schemes, requiring significantly less investments in server-side infrastructure. HTTP Adaptive Streaming (HAS) is now becoming omnipresent in video streaming services due to many advantages offered by HTTP-based streaming: reliable transmission over TCP, reuse of existing caching infrastructure and compatibility with NATs and firewalls. In HAS, the video content is split temporally into segments which are encoded at different quality rates. The client side heuristic decides at which quality rate each segment should be downloaded, based on measured network statistics, buffer filling level and device characteristics. Traditionally, Advanced Video Coding (AVC) is used to encode the different segments, introducing a significant amount of redundancy across quality representations. Scalable Video Coding (SVC) can cope with these issues of content redundancy by creating dependencies between the base and enhancement layers. Adopting SVC in HAS significantly improves caching and bandwidth efficiency at the server side. Another advantage of SVC, is the ability to gradually upgrade the quality of the video by downloading additional video layers.

HTTP Adaptive Streaming (HAS) has several advantages compared to traditional streaming protocols, such as easy traversal of firewalls and reuse of widely deployed HTTP infrastructure. HAS content is temporally segmented, and encoded at different quality representations, allowing the video player to autonomously adapt to network conditions by adapting play-out quality between subsequent segment downloads. However, to guarantee continuous playback, current-generation HAS protocols require a large play-out buffer. This makes them ill-suited for live television, as it significantly increases the live signal delay. This paper proposes a novel HAS solution for live streaming services. A HAS video player was designed that can cope with buffers as small as 2 seconds. This obviously requires the player to more rapidly react to bandwidth changes, which was achieved by using the Scalable Video Coding (SVC) extension of the H.264 Advanced Video Coding (AVC) video codec. Moreover, an intelligent network proxy was developed that guarantees the delivery of the SVC base quality layer using Differentiated Services (DiffServ). Furthermore, a more dynamic deadline-based approach is proposed which allows the client itself to decide which segments should be prioritized based on the risk of running into a buffer starvation. This enables more efficient use of the prioritized channel, leading to less freezes and increased quality and stability. The combination of these technologies allows the video player to align its quality adaptation decisions to the available bandwidth more efficiently and completely avoid buffer starvations. The small buffer size also reduces the total live signal delay from multiple dozens to only a few seconds.

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for Over-The-Top video streaming. In HAS, each video is temporally segmented and stored in different quality levels. Quality selection heuristics, deployed at the video player, allow dynamically requesting the most appropriate quality level based on the current network conditions. Today's heuristics are deterministic and static, and thus not able to perform well under highly dynamic network conditions. Moreover, in a multi-client scenario, issues concerning fairness among clients arise, meaning that different clients negatively influence each other as they compete for the same bandwidth. In this article, we propose a Reinforcement Learning-based quality selection algorithm able to achieve fairness in a multi-client setting. A key element of this approach is a coordination proxy in charge of facilitating the coordination among clients. The strength of this approach is three-fold. First, the algorithm is able to learn and adapt its policy depending on network conditions, unlike current HAS heuristics. Second, fairness is achieved without explicit communication among agents and thus no significant overhead is introduced into the network. Third, no modifications to the standard HAS architecture are required. By evaluating this novel approach through simulations, under mutable network conditions and in several multi-client scenarios, we are able to show how the proposed approach can improve system fairness up to 60% compared to current HAS heuristics.

In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11–18% in terms of mean opinion score in a wide range of network configurations.

HTTP adaptive streaming (HAS) services allow the quality of streaming video to be automatically adapted by the client application in face of network and device dynamics. Due to their advantages compared to traditional techniques, HAS-based protocols are widely used for over-the-top (OTT) video streaming. However, they are yet to be adopted in managed environments, such as ISP networks. A major obstacle is the purely client-driven design of current HAS approaches, which leads to excessive quality oscillations, suboptimal behavior, and the inability to enforce management policies. Moreover, the provider has no control over the quality that is provided, which is essential when offering a managed service. This article tackles these challenges and facilitates the adoption of HAS in managed networks. Specifically, several centralized and distributed algorithms and heuristics are proposed that allow nodes inside the network to steer the HAS client's quality selection process. The algorithms are able to enforce management policies by limiting the set of available qualities for specific clients. Additionally, simulation results show that by coordinating the quality selection process across multiple clients, the proposed algorithms significantly reduce quality oscillations by a factor of five and increase the average delivered video quality by at least 14%.

The Internet is rapidly evolving towards a multimedia service delivery platform. However, existing Internet-based content delivery approaches have several disadvantages, such as the lack of Quality of Service (QoS) guarantees. Future Internet research has presented several promising ideas to solve the issues related to the current Internet, such as federations across network domains and end-to-end QoS reservations. This paper presents an architecture for the delivery of multimedia content across the Internet, based on these novel principles. It facilitates the collaboration between the stakeholders involved in the content delivery process, allowing them to set up loosely-coupled federations. More specifically, the Federated Resource Reservation (FedRR) algorithm is proposed. It identifies suitable federation partners, selects end-to-end paths between content providers and their customers, and optimally configures intermediary network and infrastructure resources in order to satisfy the requested QoS requirements and minimize delivery costs.

Recently, the Internet has become a popular platform for the delivery of multimedia content. However, its best effort delivery approach is ill-suited to guarantee the stringent Quality of Service (QoS) requirements of many existing multimedia services, which results in a significant reduction of the Quality of Experience. This paper presents a solution to these problems, in the form of a framework for dynamically setting up federations between the stakeholders involved in the content delivery chain. More specifically, the framework provides an automated mechanism to set up end-to-end delivery paths from the content provider to the access Internet Service Providers (ISPs), which act as its direct customers and represent a group of end-users. Driven by Service Level Agreements (SLAs), QoS contracts are automatically negotiated between the content provider, the access ISPs, and the intermediary network domains along the delivery paths. These contracts capture the delivered QoS and resource reservation costs, which are subsequently used in the price negotiations between content provider and access ISPs. Additionally, it supports the inclusion of cloud providers within the federations, supporting on-the-fly allocation of computational and storage resources. This allows the automatic deployment and configuration of proxy caches along the delivery paths, which potentially reduce delivery costs and increase delivered quality.

In recent years, the networking community has put a significant research effort in identifying new ways to distribute content to multiple users in a better-than-unicast manner. Scalable delivery is more important now video is the dominant traffic type and further growth is expected. To make content distribution scalable, in-network optimization functions are needed such as caches. The established transport layer protocols are end-to-end and do not allow optimizing transport below the application layer, hence the popularity of overlay application layer solutions located in the network. In this paper, we introduce a novel transport protocol, the Shared Content Addressing Protocol (SCAP) that allows in-network intermediate elements to participate in optimizing the delivery process, using only the transport layer. SCAP runs on top of standard IP networks, and SCAP optimization functions can be plugged-in the network transparently as needed. As such, only transport protocol based intermediate functions need to be deployed in the network, and the applications can stay at the topological end points. We define and evaluate a prototype version of the SCAP protocol using both simulation and a prototype implementation of a transparent SCAP-only intermediate optimization function.

In recent years, telecom operators have been moving away from traditional, broadcast-driven, television towards IP-based, interactive and on-demand services. Consequently, multicast is no longer a viable solution to limit the amount of traffic in the IP-TV network. In order to counter an explosion in generated traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Classical caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this paper, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The use of popularity prediction is compared to classical strategies using trace files from an actual deployed Video on Demand service. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated.

As the requirements and scale of cloud environments increase, scalable management of the cloud is needed. Centralized solutions lack scalability and fully distributed management systems only have a limited overview of the system. One of the often-studied problems in cloud environments is the application placement problem, used to decide where application instances are instantiated and how many resources to allocate to the instances. In this paper a general approach is introduced for using centralized cloud resource management algorithms in a hierarchical context, increasing the scalability of the management system while maintaining a high placement quality. The management system itself is executed on the cloud, further increasing scalability and robustness. The proposed method uses aggregation techniques to generate input values for a centralized application placement algorithm which is run in all management nodes. Decoupling ensures management nodes can function independently. Subsequently, we compare the performance of hierarchical application placement method with that of a fully centralized algorithm. The results show that a solution, within 5% of the optimum placement when using the centralized algorithm, can be achieved hierarchically in less than 25% of the time needed for execution of the centralized algorithm.

The popularity of streaming multimedia services has greatly increased in recent years. Telco- and cable-providers have started offering a plethora of multimedia services in the access and aggregation network, including video on demand, interactive digital television, and time-shifted TV. However, these services introduce additional challenges, such as stringent time constraints, and high bandwidth requirements. To overcome these problems, we explore the advantages of delivering such multimedia content using deadline-aware scheduling and caching algorithms. These algorithms decide when to send and store which content. This enables the network to optimize bandwidth consumption and satisfy deadline constraints. The designed algorithm was evaluated and compared to classical deadline-unaware delivery protocols. This allows us to study the efficiency of the new algorithm, and identify the scenarios in which deadline-aware scheduling improves delivery of multimedia content.

A generic and self-managing service hosting infrastructure, provides a means to offer a large variety of services to users across the Internet. Such an infrastructure provides mechanisms to automatically allocate resources to services, discover the location of these services, and route client requests to a suitable service instance. In this paper we propose a dynamic and latency-aware algorithm for assigning resources to services. Additionally, the proposed service hosting architecture and its protocols to support the service placement algorithm, are described in detail. Extensive simulations were performed to compare the solution of our latency-aware algorithm to the latency-unaware variant, in terms of system efficiency and scalability.