Multi-access edge computing has become one of the key technologies involved in the development of the Internet of Vehicles (IoV) because of its low latency and high bandwidth. In some hot spots or emergency situations, unmanned aerial vehicle (UAV)-assisted vehicular edge computing can flexibly cope with the problem of insufficient resources in a fixed edge server network. Deep reinforcement learning (DRL) can effectively address the edge computing offloading optimisation problem in the above scenarios and improve the utilisation of network resources. In this paper, we transform the optimisation problem of a UAV-assisted edge computing offloading strategy in an IoV context into a multi-agent optimisation problem by establishing a system model. In addition, a graph attention network (GAT) is introduced to determine the interaction relationships between multiple agents, such as multiple UAVs and edge servers. The deep deterministic policy gradient (DDPG) algorithm combined with a GAT can effectively capture the collaboration pattern between multiple agents to obtain the computational offloading strategy that minimises the system delay. The simulation results show that the convergence speed and optimisation ability of the proposed algorithm based on the GAT-DDPG fusion network are superior to those of other baseline algorithms.

The ongoing evolution of future generations of mobile systems and fixed wireless networks is primarily motivated to enable high-bandwidth and low-latency demanding services in different vertical sectors. This endeavor is not fueled by smartphones alone, but technologies like industrial internet of things (IIoT), extended reality (XR), and human-to-machine (H2M) collaborations for fostering industrial and social revolutions like Industry 4.0/5.0 and Society 5.0. To ensure an ideal immersive experience and avoid cyber-sickness for the users in all the aforementioned usage scenarios, it is typically challenging to synchronize XR content from a remote machine to a human operator (HO) according to the head movements in real-time over communication networks. Thus, we propose a novel H2M collaboration scheme where the HO’s head movements are predicted ahead with very high accuracy to orient the machine’s camera in advance. As XR frame size varies in accordance with the HO’s head movements, we predict the corresponding bandwidth requirements from the machine’s camera to propose a human-machine coordinated dynamic bandwidth allocation (HMC-DBA) scheme. Through extensive simulations, we show that end-to-end latency and jitter requirements of XR frames are satisfied over enterprise networks like Fiber-To-The-Room-Business. Furthermore, we show that better efficiency in network resource utilization is achieved by employing our proposed HMC-DBA over state-of-the-art schemes.

The operational security of latency-sensitive networked applications is increasingly threatened by evolving malicious attacks that compromise operational integrity and network performance. Human-to-machine (H2M) applications, which rely on seamless bidirectional control signals and haptic feedback transmission, exemplify such latency-sensitive use cases. Existing learning-based malicious attack detection frameworks suffer from their reliance on pre-trained datasets, making machine learning models within them ineffective against previously unseen attack patterns. As attack profiles dynamically evolve, static models become obsolete, necessitating adaptive mechanisms to maintain detection accuracy. In this context, concept drift adaptation will serve as a critical tool for enabling models to continuously adjust to changing traffic distributions and emerging attack patterns. However, real-world H2M applications lack access to accurately labeled malicious traffic data, making real-time adaptation of defense mechanisms infeasible. To address these challenges, we propose a Concept Drift Adaptation-facilitated malicious attack Defense framework (CDAD). Firstly, CDAD employs Adaptive Random Forest as an incremental learning approach, integrating an error-rate-based concept drift detection mechanism to dynamically identify evolving attack patterns and trigger adaptive model updates. Secondly, a haptic behavior classifier is introduced to classify expected human operator interactions and compare them with real-time haptic feedback from remote machines. This enables automated traffic relabeling, allowing CDAD to adapt to previously unseen attacks without relying on pre-labeled datasets. The superior performance of CDAD over existing state-of-the-art methods is demonstrated across various malicious attack scenarios through extensive simulations. Results show that with CDAD, the attack success rate can be limited to 3%, while maintaining an inference time below 1ms, thereby ensuring effective and efficient malicious attack defense in latency-sensitive H2M applications.

Machine learning (ML)-enhanced future communication networks are able to advance human-to-machine (H2M) applications by intelligent bandwidth prediction techniques to achieve bandwidth pre-allocation. Existing methods of H2M bandwidth prediction typically assume the stationary data stream over time. However, in the near future, communication networks are expected to support dynamic and heterogeneous applications. Since different H2M applications will exhibit different traffic distributions and loads across the day, an ML model learned on a specific H2M application at a particular network load will, therefore, be unable to adapt to changing applications and network loads. This will give rise to the phenomenon known as concept drift. This paper addresses concept drift in dynamic and heterogeneous networks supporting H2M applications by proposing a novel framework, the concept drift detection and adaptation (CDDA) framework, to respond and adapt to the concept drift rapidly. CDDA learns the traffic characteristics of H2M applications and combines offline and online learning processes to enhance H2M traffic prediction and improve band-width prediction performance. Results from our investigation using experimental traffic from H2M applications over a 10Gb/s passive optical network simulator show that CDDA can more rapidly respond to concept drift and better predict the bandwidth of changing H2M applications and network load.

We propose a first Asynchronous Federated Split Learning (AFSL), to add the flexibility of asynchronous computing to the combination of federated and split learning. This amounts to designing a harmonious combination of different paradigms in order to benefit from the advantages of each of them and to reduce the impacts of their shortcomings.This way, AFSL answers to the increasingly rising interest for distributed algorithms with the advent of edge computing in order to support new market segments, such as cloud gaming, immersive eXtended Reality (XR), indoor positioning, and mission critical IoT networks, with stringent requirements on latency and reliability.Computational experiments are conducted on IID and non-IID datasets to investigate the added value of the asynchronous feature. Results indicate that AFSL can accelerate model learning by up to 86% without sacrificing the model’s convergence and accuracy. Indeed, not only average training times are reduced, but clients use fewer resources, a critical characteristic for devices with limited computing capabilities, e.g., in edge devices. Performance degradation can be mitigated by a careful selection of the aggregation principle. Other advantages are with AFSL training in dynamic scenarios as it provides robustness with a short recovery time by leveraging asynchronous client training.

Reinforcement learning (RL) has recently emerged as a promising solution for intelligence bandwidth decisions that reduce latency in optical access networks. Even though RL drives model-free self-adaptive bandwidth decisions, the learning time cost and the widely-known exploration-exploitation dilemma of when to apply the best decision learnt are challenging to address in the bandwidth decision context. This paper for the first time exploreshow to rapidly learn an optimal bandwidth decision with a known confidence level of the decisionfor minimizing optical access network latency. We investigate critical aspects, including reward acquisition and strategies to explore decisions, in an RL-based bandwidth allocation scheme. Applying renewal theory, we address the timing for the central office (CO) to acquire rewards from optical network units for accurate decision value evaluation. Further, we derive the relationship between the decision practice times and the confidence of the optimal decision in closed-form. A reward variance-oriented (RVO) exploration strategy is proposed, in which the CO selects bandwidth decisions with probabilities proportional to the reward variances. We prove that the RVO is the most time efficient in learning an optimal decision with a confidence guarantee. With numerical and extensive simulations, we validate the theory and compare several common strategies with the RVO.

Passive optical networks are envisioned to become increasingly complex as they support more and more diverse and immersive services that have different capacity, latency, and reliability needs. In the near term, they are expected to support the delivery of a diverse and immersive set of services including mixed reality, holographic communication, human-to-machine/robot communications, Tactile Internet, and digital sensing. However, in supporting these diverse and immersive services, traffic on the network will become increasingly dynamic across a range of different time scales. The upstream bandwidth in a passive optical network is typically shared by a group of end users, meaning that the uplink latency performance as experienced by each end user is thus highly dependent on the amount and when bandwidth to that end user is allocated. Machine learning enhanced bandwidth allocation algorithms have been proposed but are typically stationary, primarily-designed or pre-trained based on certain network configurations. In dynamic network conditions where traffic can evolve over time, concept drift, a phenomenon whereby the underlying distribution of the training data will no longer be representative of that in deployment, may occur. In view of future dynamic network conditions, we present a novel online reinforcement learning based bandwidth allocation scheme to address concept drift in machine learning enhanced passive optical network. The scheme facilitates self-adaptive decisions in real-time to accommodate dynamic network environments with varying traffic types and network loads. Results from comprehensive performance evaluation of the scheme show that rapid and self-adaptive bandwidth decisions can be achieved, yielding ~ 60% latency improvement in dynamic traffic environments.

Optical wireless communication (OWC) is emerging as a potential solution for the deployment of 5G and beyond! 6G heterogeneous networks as it can reach physical layer data rates exceeding 10 Gbps. However, the upper layer protocols of OWC are not yet fully developed to support multi user communication and emerging applications. Hence, the development of proper upper layer protocols and network architectures to support such communication under the multi-gigabit capabilities becomes a matter of the utmost importance. In this paper, we propose a contention-free medium access control (MAC) protocol within the Full-duplex Split-plane Optical Wireless Network (FLOWN) architecture to offer users a guaranteed channel access. The proposed protocol is equipped with split-plane operation, full-size IP packet support with high aggregation, and adaptive beacon interval adjustment. Through simulations, we demonstrate that the proposed MAC protocol can simultaneously serve upcoming high demanding multi-gigabit and delay-stringent applications by satisfying their quality of experience requirements.

Human-controlled and haptic feedback data in emerging Tactile Internet human-to-machine (H2M) applications require stringent low-latency transmission. Understanding the traffic features of the new applications is vital in innovating network control and resource allocation strategies to meet their latency demand. In this article, we present our experimental study on human control and haptic feedback traffic in H2M applications and investigate novel bandwidth allocation schemes in supporting converged H2M application delivery over access networks. We introduce our haptic experiment system, the developed H2M applications, and analyze the control and feedback traffic traces collected. Then, exploiting the correlation between real-time control and feedback reported in our analysis, we propose an artificial intelligence-facilitated low-latency bandwidth allocation (ALL) scheme for emerging H2M applications. ALL provisions priority-differentiated bandwidth allocation for aggregated H2M and conventional content-centric applications over future access networks. By using ALL, the central office preallocates bandwidth for control and its corresponding feedback traffic interactively and prioritizes their transmission over content traffic. This expedites H2M application delivery by eliminating the report-then-grant process in the existing bandwidth allocation schemes. Via extensive simulations injected with experimental traffic traces, we comprehensively investigate the latency performance of ALL and existing schemes. Our results validate the superior capability of ALL in reducing and constraining latency for H2M applications.

This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.

This paper presents the exploitation of an artificial neural network (ANN) to facilitate insights into existing bandwidth allocation schemes in optical access networks and supervise bandwidth allocation decisions that reduce the latency. Specifically, based on the classic and predictive dynamic bandwidth allocation (DBA) schemes, we train a multi-layered ANN at the central office (CO) to learn the uplink latency corresponding to varying bandwidth allocation decisions. Multiple network feature knowledge, such as network load, traffic/packet statistics, fiber link distances and the number of optical network units (ONUs), is for the first time considered and utilized in the training process. Then, with the dependency between bandwidth allocation and the resultant latency learned by the ANN, we numerically analyze the latency performance of existing DBA schemes and show the optimal bandwidth decisions supervised by the ANN in achieving low latency. With extensive simulations, we show that exploiting the ANN to supervise bandwidth allocation at the CO, termed as ANN-DBA scheme, effective improvement in latency performance is realized.

In anticipation of future ubiquitously-connected healthcare services with stringent delay requirements, we outline the main characteristics, design challenges and existing open issues in the medium access control (MAC) layer design of wireless body area networks (WBANs) and highlight the need to define hybrid MAC frame that enables flexible access according to traffic and services. For the first time, a thorough investigation of two WBAN standards, namely the IEEE 802.15.6 WBAN and the recently-proposed ETSI SmartBAN, in terms of delay and energy, is presented. We provide insights into the impact of MAC frame timing structure on delay and energy performances of these existing protocols. We compare the selections of access durations for the SmartBAN hybrid MAC frame and IEEE 802.15.6 superframe. Then, we present our simulation comparisons of the uplink delay and energy consumption in a SmartBAN and a IEEE 802.15.16 WBAN for healthcare, taking into account periodic monitoring and health-critical emergency traffic patterns. Our results emphasize that compared to IEEE 802.15.6-defined WBANs, SmartBANs are advantageous in energy-saving. Moreover, with a time-optimized MAC, SmartBANs reduce the delay for both periodic monitoring and emergency report.

The Long Term Evolution (LTE) technology is ubiquitously implemented today and it achieves noticeable success in fulfilling the data transmission demand instigated by the unprecedented growth of mobile services. However, the LTE might fail to provide qualified services for Machine Type Communications (MTC) and the emerging Tactile Internet, as both require data being transmitted with extremely low latencies (0.5~5 ms). In this paper, we propose a novel predictive Semi-Persistent Scheduling (SPS) scheme with the corresponding SPS scheduler that takes advantage of transmission history, to effectively reduce the uplink latency of LTE systems. Moreover, the proposed SPS design is compatible with new radio (NR) technology developed by Third Generation Partnership Project (3GPP), since both systems share a similar medium access control (MAC) layer skeleton. Results from extensive simulations show that the proposed SPS scheme outperforms the conventional dynamic scheduling scheme and existing SPS schemes in terms of latency and scheduling accuracy. In particular, with a short SPS periodicity, the proposed SPS scheme achieves a sub-4 ms uplink latency under various traffic loads.

Optical wireless communication has emerged as a promising candidate for future high data rate indoor applications such as virtual reality. Even though physical layer of optical wireless networks has rapidly developed during last decade, upper layer architecture that harness the physical layer capabilities has not yet been developed in the same pace. To this end, the authors develop a novel contention‐based medium access control (MAC) protocol that accompanies a service differentiation mechanism and a dynamic contention window tuning algorithm. The proposed service differentiation mechanism can identify the diverse traffic types and facilitate their throughput and delay requirements. To add more robustness to the contention‐based MAC protocol which depends on contention windows to avoid collisions, the authors also propose an algorithm that dynamically changes the contention window sizes to suit the congestion level. They analyse the performance of the proposed MAC protocol under diverse network configurations and they show that it is far more effective to use end‐user network metrics such as throughput in dynamic adaptation algorithms in addition to collision rate due to the wide range of traffic types present in the network. The proposed results demonstrate that the proposed MAC protocol can handle next‐generation traffic types and their stringent latency requirements in an effective manner.

Human-to-machine (H2M) communications in emerging tactile-haptic applications are characterized by stringent low-latency transmission. To achieve low-latency transmissions over existing optical and wireless access networks, this paper proposes a machine learning-based predictive dynamic bandwidth allocation (DBA) algorithm, termed MLP-DBA, to address the uplink bandwidth contention and latency bottleneck of such networks. The proposed algorithm utilizes an artificial neural network (ANN) at the central office (CO) to predict H2M packet bursts arriving at each optical network unit wireless access point (ONU-AP), thereby enabling the uplink bandwidth demand of each ONU-AP to be estimated. As such, arriving packet bursts at the ONU-APs can be allocated bandwidth for transmission by the CO without having to wait to transmit in the following transmission cycles. Extensive simulations show that the ANN-based prediction of H2M packet bursts achieves >90% accuracy, significantly improving bandwidth demand estimation over existing prediction algorithms. MLP-DBA also makes adaptive bandwidth allocation decisions by classifying each ONU-AP according to its estimated bandwidth, with results showing reduced uplink latency and packet drop ratio as compared to conventional predictive DBA algorithms.

Existing optical and wireless networks designed to support today's services may not be suitable for the emerging services, such as machine-to-machine applications of the Internet-of-Things paradigm and tactile-haptic applications of the Tactile Internet paradigm. Some of these applications must adhere to stringent quality of service requirements, especially an ultra-low end-to-end latency constraint of approximately 1 ms. In this paper, we investigate the feasibility of the wireless local area network (WLAN) infrastructure, specifically the IEEE 802.11 HCF Controlled Channel Access (HCCA) media access control (MAC) protocol, in supporting low-latency applications. The scenario considered is tactile-haptic data transmission between body-worn data collection devices and a wireless access point. In particular, we focus on uplink direction rather than downlink direction, because uplink data transmission forms a latency bottleneck of this particular network. In this paper, we first formulate an analytical model of the HCCA using a queuing theory to evaluate the average uplink latency. We then carry out global sensitivity analyses to understand the implications of various timing and network parameters on the uplink latency. Using these insights, we propose a novel strategic parameter selection (SPS) algorithm that effectively reduces uplink latency of the IEEE 802.11 WLAN. This paper provides insights into WLAN design such that latency-sensitive applications can be supported in the near future.

Cloud-based computing technology is one of the most significant technical advents of the last decade and extension of this facility towards access networks by aggregation of cloudlets is a step further. To fulfill the ravenous demand for computational resources entangled with the stringent latency requirements of computationally-heavy applications related to augmented reality, cognitive assistance and context-aware computation, installation of cloudlets near the access segment is a very promising solution because of its support for wide geographical network distribution, low latency, mobility and heterogeneity. In this paper, we propose a novel framework, Cloudlet Cost OptiMization over PASSIve Optical Network (CCOMPASSION), and formulate a nonlinear mixed-integer program to identify optimal cloudlet placement locations such that installation cost is minimized whilst meeting the capacity and latency constraints. Considering urban, suburban and rural scenarios as commonly-used network deployment models, we investigate the feasibility of the proposed model over them and provide guidance on the overall cloudlet facility installation over optical access network. We also study the percentage of incremental energy budget in the presence of cloudlets of the existing network. The final results from our proposed model can be considered as fundamental cornerstones for network planning with hybrid cloudlet network architectures.

The smart body area network (SmartBAN) is a recently proposed system for realizing low complexity and ultralow power body area network. In this paper, we present the first study on SmartBAN downlink delay and energy performances considering the emerging control/actuation applications in future e-health. A novel downlink transmission framework arising from our performance study is presented, which reduces the delay and energy consumption for downlink-dominated SmartBANs. In this paper, we first investigate the delay of the supplementary downlink mode (SDM) specified in the SmartBAN medium access control (MAC) protocol and propose an improved SDM (ISDM), showing that reordering the access periods in the SmartBAN MAC frame can effectively reduce the delay. To address the delay bottleneck in SDM and ISDM, we further propose limited-exhaustive (LEDM) and fully exhaustive (FEDM) downlink mechanisms. Then, based on SDM, ISDM, LEDM, and FEDM, energy-saving mechanisms in the SmartBAN downlink are discussed. Moreover, to critically evaluate the delay and energy-savings of LEDM and FEDM, we develop two embedded Markov chains that suit the SmartBAN beacon-enabled MAC. Finally, based on the above performances study, a novel downlink transmission framework that selects suitable transmission mechanism and access durations for delay-constraint SmartBAN applications is proposed. Extensive simulations show the effectiveness of our proposed mechanisms and transmission framework.

The smart body area network (SmartBAN) is a recently proposed system for wireless body area networks (WBANs). Compared to conventional WBANs, it is designed to support lower system complexity and ultralow power consumption. In a SmartBAN, the sensors' access is scheduled upon receiving a beacon on the data channel at the beginning of each working cycle, termed as interbeacon interval (IBI). As network performance, including delay and energy consumption, is highly dependent on the length of IBI, we present, in this paper, an optimal IBI frame for SmartBAN. Our focus is on the delivery of uplink periodically-generated sensor data with low delay and high energy efficiency. As periodic traffic is a common traffic pattern widely generated in e-health applications, for which most previously proposed Markov models for WBANs are inapplicable, a closed-form analytical delay model for periodic SmartBAN transmission is derived. Exploiting this model, a time-optimized framework that minimizes average uplink delay is formulated. An adaptive IBI algorithm is then proposed to determine the optimal IBI during the network connection between hub and sensors. Finally, sleep mode in sensors and doze mode in the hub are introduced to reduce energy consumption under the proposed framework. With optimal IBI, the percentage of energy savings and channel efficiency is evaluated. Simulation and theoretical results show that by using the proposed time-optimized framework, both delay and energy consumption of periodic traffic are significantly reduced. Comparisons with the IEEE 802.15.6 WBAN show performance improvements in periodic uplink delay and energy consumption with our proposed time-optimized framework for SmartBANs.

The Internet has evolved a long way from transporting basic web data to transporting traffic instigated by new emerging applications that are available today. One such application is remote human-to-machine interaction, in which touch and actuation related information is delivered over the network. These applications are extremely sensitive to latency, with some reporting latency requirements in the millisecond range. Therefore, it is crucial to minimize latency experienced in each and every segment of the network. In this paper, we consider a scenario in which tactile body-worn devices are connected using the IEEE 802.11 network, and analyze the wireless transmission latency from these devices to the wireless access point, when the hybrid coordination function controlled channel access (HCCA) MAC protocol is used. In particular, we use queuing theory based approach to derive closed-form expressions for the average latency. We use the insights gained from our latency analysis to propose a new service interval selection method that outperforms the HCCA reference design. We show for the first time the parameters that have significant impact on latency and how these can be manipulated to meet the stringent latency constraints.

In the post-4G era, "low latency" has become one of the most important network requirements along with support for ultra-high capacity and ultra-high reliability. This has led to the evolution of "cloudlets" to support similar services provided by legacy cloud technology viz., storage capacity and computational capability. In this paper, we propose a novel framework for cloudlet-empowered-cloud network design and planning, based on optical access infrastructures. Our focus is on network planning to optimize network infrastructure cost by formulating a nonlinear programming model to identify placement locations of cloudlet servers subjected to capacity and latency constraints. We demonstrate the feasibility of the proposed model against urban, suburban and rural scenarios, providing guidance on the installation and maintenance costs. Furthermore, we assess the percentage of incremental energy arising from the presence of cloudlets in the optical access network. The proposed framework is a first in yielding insights that will serve as a foundation for further cloudlet network planning strategies.

Wireless body area networks (WBANs) are expected to support control/steering and haptic communication via on-body actuators in future Tactile Internet enabled e-health systems. However, little is known to date about the capability of WBANs in realizing remotely-controlled applications since current WBANs are uplink-transmission dominated for the purpose of health monitoring and supervision. In this paper, we present the first downlink delay performance evaluation based on the recently-proposed Smart Body Area Network (SmartBAN). To meet the stringent 1-ms Tactile Internet delay requirement for real-time tactile feedback and control delivery, we make comparisons between two downlink transmission mechanisms: (a) conventional exhaustive transmission; and (b) fixed-length exhaustive transmission based on the SmartBAN medium access control (MAC) layer protocol. M/D/1 and embedded Markov chain models are developed to evaluate downlink delay for SmartBANs adopting the transmission mechanisms above. The accuracy of our models is validated by simulations. Analytical and simulation results show that compared to the conventional mechanism, the fixed-length exhaustive transmission can effectively reduce downlink delay to less than 1 ms by increasing downlink transmission duration and improved energy performance can potentially be achieved thanks to the fixed MAC configuration. Further, with our model, suitable downlink durations can be determined by considering delay constraints in practical applications.

The Smart Body Area Network (SmartBAN) is a recently proposed system for wireless body area networks (WBANs). Unlike widely-used WBANs that employ contention-based medium access control (MAC) protocols, the SmartBAN MAC specifies a joint time division multiple access (TDMA) and slotted ALOHA access framework. To date, a criterion for determining the time duration of different channel access phases remains unaddressed in current WBAN MAC designs. There is also a lack of understanding of how the duration of these timing periods impacts the SmartBAN's performance. In this paper, we derive closed-form analytical models for the uplink transmission delay, which is defined as the duration a data packet generated by a sensor has to wait prior to its uplink transmission. We adopt a flexible channel access mechanism considering the characteristics of two major traffic patterns in medical applications: periodic monitoring traffic and Poisson-distributed emergency traffic. Then, based on both analytical models and simulation of the MAC timing parameters, and in conjunction with the behavior of a typical SmartBAN in terms of delay and energy consumption with aggregated traffic load, we present our solution to determine different access periods of the SmartBAN MAC. With extensive simulations, the accuracy of the delay model is validated and a blocking state of the SmartBAN is discussed, where the delay and energy performance is degraded significantly due to the queuing at each sensor. The results obtained in this paper provide a first in understanding how timing parameters and the traffic features impact SmartBANs' delay and energy performance.

Vision is one of the most advanced and important sensory input in humans. However, many people have vision problems due to birth defects, uncorrected errors, work nature, accidents, and aging. The white cane and guide dog are the most widely used means of navigation for the vision-impaired. With advancements in technology, electronic devices have been created using different sensors and technologies to help navigate the blind. Electronic Travel Aids (ETAs) assist in navigating a person by collecting information about the environment and relaying this information in a form that allows a blind or vision-impaired person to understand the nature of the environment. However, there is still a lack of devices to detect potholes and uneven pavements, which inhibits mobility after dark. This pilot study proposes a computer vision based pothole and uneven surface detection approach to assist blind people in meeting their mobility needs. The system includes projecting laser patterns, recording the patterns through a monocular video, analyzing the patterns to extract features and then providing path cues for the blind user. With over 90% accuracy in detecting potholes, the proposed system aims to assist blind people in real-time navigation.

The reliance on the loss-of-signal (LOS) of upstream transmissions to indicate fiber/component failure is potentially unsuitable in networks that implement sleep/doze mode operation. In such networks, the transition into sleep/doze mode would result in no signal transmission, and when used in conjunction with conventional LOS to indicate network failure, would result in erroneous triggering of false alarm and subsequently protection switching. Recently, converged access networks using a hybrid passive optical architecture, have been favored as a low-cost and high-bandwidth solution to deliver high-bandwidth applications to both fixed access and mobile users. These networks are referred to as Hybrid PON Converged Access Networks. Protection against fiber/equipment failures in these networks is critical considering the customer base, network span, and traffic supported. This paper proposes four survivable architectures for such converged access networks. These architectures combine rapid fault detection and protection switching against high impact failures but without the need to rely on upstream transmissions for LOS detection. A comparison of the four architectures across three different area densities under three deployment scenarios, is presented. Guidance for selecting the best protection architecture to be deployed, considering area densities and deployment scenarios, is provided.

The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today's mobile networks' usage is dominated by over-the-top (OTT) applications, and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior toward a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared with a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.

We previously proposed and numerically analyzed a theoretical framework of an energy-efficient offline dynamic wavelength and bandwidth allocation (DWBA) algorithm designed for a delay-constrained time and wavelength division multiplexed passive optical network (TWDM-PON). This DWBA algorithm exploits the tunability and the sleep/doze capabilities of a 10 Gbps vertical-cavity surface-emitting optical network unit (10G-VCSEL-ONU) to improve the energy-savings at the OLT and the ONUs, respectively. In this work, using simulation results on the number of active wavelengths and the percentage of energy-savings, we verify the theoretical framework proposed in our previous study. Most importantly, we show that the average delay of upstream packets are not adversely affected by the proposed energy-saving mechanism and is kept below the specified maximum.

This paper proposes an advanced sleep-aware dynamic bandwidth allocation (ASDBA) scheme for 10G-EPONs that aims to maximize ONU energy efficiency with sleep mode. In the proposed ASDBA scheme, both upstream (US) and downstream (DS) transmissions are scheduled in the same transmission slot whose duration is minimized based on both DS and US bandwidth requests and the ONU transceiver is switched off outside the slot for saving energy. The ASDBA swaps the conventional order of control message exchange utilized in legacy 10G-EPON to convert the ONU idle time between a REPORT message and its replying GATE message into ONU sleep time. This enables the ONU to sleep continuously after sending a REPORT message until the beginning of the next transmission slot, further improving ONU energy-savings. Results show that the proposed scheme significantly saves ONU energy whilst incurring acceptable frame queuing delays.

In addressing the requirements of next-generation passive optical networks, the time and wavelength division multiplexed PON (TWDM-PON) has been selected as the next technology solution beyond 10 Gbps PONs. Due to the increased network reach and customer base, many of which are business customers, rapid fault detection and subsequent restoration of services are critical. Fault protection for conventional PONs has previously been extensively explored. Application of these existing schemes is however inappropriate for TWDM-PONs as an increased network reach and customer base necessitate highly sensitive monitoring modules for fiber/device fault detection. The existing use of upstream transmissions as a loss of signal (LOS) indicator at the central office is also unsuitable due to the sleep/doze mode nature of the optical network units. Here, survivable TWDM-PON architectures which combine rapid fault detection and protection switching to provide resilience are proposed. These architectures do not rely on upstream transmissions for LOS activation. Each exploits highly-sensitive monitoring modules with fast-response fault detection and subsequent protection switching and requiring only very low levels of monitoring input power. The maximum achievable network reach and split ratio, and the survivability of all three schemes are analyzed and compared.

Today, video-on-demand (VoD) systems are challenged by a growing number of users, growing sizes of libraries, and increasing video streaming rates. Therefore, scalability and the bandwidth efficiency of VoD systems have become important considerations. In this paper, we propose a scalable and bandwidth efficient delivery scheme for VoD systems, which optimally exploits the storage and multicast capabilities to reduce the consumption of server capacity resources. Our proposed scheme, which we call prepopulation assisted batching with multicast patching (PAB-MP), facilitates video multicast from the server by strategic preplacement of initial segments of videos at the end-users' devices. Using an analytical approach, we show how the parameters for the PAB-MP scheme can be selected to achieve optimal performance. Moreover, we propose methods to replicate and place the initial video segments (IVSs) across the end-users' devices such that load arising from IVSs is evenly distributed among user-nodes. Using simulations, we show that our proposed scheme effectively reduces the load on the server especially under high load, while imposing less burden on individual user nodes. Simulation results indicate that our proposed PAB-MP scheme is significantly more scalable than the other popular approaches that exploit multicast and storage.

Video-on-demand has been highlighted as one of the highest growing traffic generators to the global IP networks. Benefits of using distributed caching servers within passive optical networks to optimize the traditional video-on-demand delivery has been discussed in literature. In our previous work, we introduced a dual-receiver dynamic bandwidth allocation algorithm to improve such video-on-demand services using a local storage placed within the access network. The main drawback of this algorithm lies in the additional power consumption at the optical network unit that arise from using two receivers. In this paper, we extend our study to present a novel single-receiver dual-channel dynamic bandwidth allocation algorithm to further optimize local storage based video-on-demand delivery over passive optical networks. Through packet-level simulations and formulation of power consumption models, we show that this algorithm improves energy-efficiency without compromising the quality-of-service performance of the network.

The use of vertical cavity surface emitting lasers (VCSELs) in energy efficient ONUs is critically examined using energy consumption models and numerical analyses of energy savings in sleep and doze mode operations. The implication of polling cycle times and network loads on the resulting energy savings and upstream utilization is investigated in the context of a 10 GE-PON with VCSEL ONUs, and for the first time, guidance on the tradeoffs between various network and protocol parameters is provided.

Video on Demand (VoD) service has become an increasingly popular service in recent years as a result of the rapid deployment of Fiber-to-the-Home networks. Due to its enormous bandwidth and stringent quality of service requirements, deploying an efficient and scalable VoD service still remains a challenge. In this paper, we propose a peer-to peer (P2P) VoD distribution scheme for PONs in which the P2P delivery is localized to within the same access network and in which a selected set of movies is pre-fetched into the customer premises equipment during off-peak hours. The proposed delivery scheme mitigates the load from the VoD server by exploiting the participation of the customer equipment in VoD distribution. In turn, this optimizes bandwidth consumption of the VoD service in both core and metro networks as the P2P video traffic is localized within the access network. We formulate movie pre-fetching as an optimization problem which determines the number of copies of each movie to be pre-fetched, and we propose a heuristic algorithm to solve it. Using simulations we show that our proposed replication algorithm performs much better than existing popularity based replication algorithm that has been proposed for similar purposes. Moreover, we show that our proposed delivery scheme effectively reduced the server load in busy hours while having high but random server load reduction in off-peak hours of service.

We present energy consumption models of video-on-demand (VoD) services delivered through newly proposed localized hybrid peer-to-peer (P2P) and localized peer-assisted patching with multicast video delivery methods over optical access networks. In this paper, we demonstrate through simulations that the localized peer-assisted patching with multicast video delivery method consumes the lowest network transport energy for popular VoD channels while localized hybrid P2P method consumes the lowest network transport energy for less popular channels. Nevertheless, both schemes outperform the conventional server centric content distribution network in term of overall network energy consumption.

The long-reach passive optical network (LR-PON) is receiving significant research attention due to its potential in delivering future bandwidth intensive services to a significantly higher number of customers at a lower unit cost of bandwidth. Carrying substantially higher traffic over increased distances as compared to conventional PONs, the survivability of these networks is a key feature that must be addressed to ensure end-to-end network reliability. Further, as these networks are optically amplified to extend its coverage, measures to detect and remove hazardous high power exposure at the fiber break are also critical. Here, we propose, experimentally demonstrate, and characterize a simple automatic protection switching scheme that exploits the use of a highly-sensitive and fast-response protection module to achieve traffic diversion to the protection path within 12 ms of failure detection. The protection module also provides an additional flexibility of activating amplifier shutdown within 2 ms of failure detection, thereby removing the hazard at the fiber break. We also perform numerical analyses of LR-PON survivability based on the probabilistic nature of fiber link failures, for conventional traffic as well as for peer-to-peer sharing over the LR-PON. Our results highlight that the level of improvement in survivability from optical protection in an LR-PON is dependent on the probability link failure of each stage of the network.

Video on Demand (VoD) service is regarded as one of the most promising services over increasingly deployed next generation broadband access networks. The distributed server architecture, in which the popular content is cached at a location closer to the viewer, is a widely used methodology to optimize the transport capacity of VoD delivery. However, due to the dynamic (change with time) nature of movie popularity distribution, identifying the popular content and updating the cache servers accordingly is not straightforward. In this paper, using a novel caching architecture for Passive Optical Networks (PON), we discuss the need for a fast caching algorithm that can respond to time changing movie popularity distribution, and we propose a novel Last-k caching algorithm, which identifies the popular content using the most recent statistics. The proposed algorithm estimates movie popularity using most recent inter-arrival times of movie requests and updates the cache accordingly such that the most popular content at any given time would reside in the cache. Simulations indicate that the proposed algorithm out-performs existing algorithms by effectively responding to the dynamic nature of movie popularity distribution.

This paper proposes an alternate solution for Ethernet passive optical networks. Our solution uses a novel protocol named full utilization local loop request contention multiple-access protocol to efficiently provide communications in passive optical networks. We study the physical layer implementation, as well as medium access control (MAC) layer protocol performance to illustrate the feasibility and benefit of our solution. The performance studies show that the MAC protocol is capable of offering 95% channel utilization under heavy load conditions. The performance results also indicate that delivery of multimedia traffic with a high quality-of-service can be achieved with our solution.