Emerging sixth-generation (6G) solutions demand learning agents that are not only autonomous and efficient, but also robust in dynamic environments and transparent in decision making. However, conventional deep reinforcement learning (DRL) approaches for mobile networks lack sufficient explainability and suffer catastrophic forgetting under dynamic conditions. In this paper, we propose a Bayesian reasoning-based explainable deep active inference (BRAIN) model, the embodied AI-inspired approach applied to mobile networks. BRAIN employs a deep generative model and minimizes variational free energy to unify perception (Bayesian state estimation) and action (resource allocation) in a single framework. Unlike DRL baselines, our agent inherently eliminates catastrophic forgetting through continuous belief updates without retraining and provides built-in explainability by exposing posterior beliefs and free energy components at runtime. Deployed as an eXtended application (xApp) on an Open, GPU-accelerated, AI-RAN testbed; BRAIN demonstrates (i) causal reasoning for slice-specific quality of service (QoS) adherence (throughput/latency/reliability), (ii) 28.3% higher robustness to traffic shifts versus DRL baselines, and (iii) real-time interpretability of resource-allocation decisions via operator-accessible belief states.

The latest ITU IMT-2030 recommendations for sixth generation (6G) networks have imposed strict specifications on communication systems. Some of these requirements include increased throughput, ultra-low delay and jitter, differentiated services, and energy efficiency. Current dynamic bandwidth allocation (DBA) schemes for passive optical networks (PON) may meet some of these requirements, yet they fail to fulfill other recommendations, especially energy efficiency. Therefore, we propose a new PON architecture, whose objective is to offer flexibility in meeting the IMT-2030 recommendations. It uses a multiclass power efficient dynamic bandwidth allocation (MPE-DBA) scheme that helps achieving both differentiated services and sustainability in terms of energy and cost. For computational efficiency, we propose a two stages Mamdani fuzzy inference system (FIS). The inputs of the first FIS are the latency and cost of the 6G traffic, whereas the latency and cost of the non-6G (N6G) traffic are the inputs of the second FIS stage. Both FISs use the variation in the number of channels as output. The proposed algorithm achieves less than 100 μs delay, less than 10μs jitter and high aggregate throughput for the 6G packets. In addition, it reduces the power consumption by three times and the cost of traffic transmission by four times as compared to the state-of-the art solution.

The future intelligent transport systems (ITSs) promise to improve traffic safety and security along with reducing the driver workload. In this article, we consider a pre-emptive emergency situation, wherein we design a safe-driving maneuver problem for transportation Metaverse (TransVerse) to compensate for driver reaction delay. The proposed scheme balances the risk of collision, and the spectral and computation resources required by the network. This combination of the spectral and computation resources required is also known as utility of the vehicular network. Further, an emergency maneuver is proposed to suggest a lane-changing maneuver based on the safe lane quality index for vehicles in vicinity of the emergency vehicle, also known as collision risk vehicles (CRVs). Moreover, based on the current position of the vehicles and the proposed safe maneuver, we propose a driving mode advisory to the drivers to provide a speed profile and a real-time high-level driving modes advice on acceleration, cruise, and engine brake. We evaluate the performance of the proposed safe-maneuver planning for CRVs in terms of collision probability, velocity, driving mode, and end-to-end lane changing delay. It can be observed that with the use of the proposed scheme, there is no effect on the performance of the high-CRV (HCRV). However, for the second following vehicle (HCRV2), the probability of collision decreases by 34.78%, and the velocity of the vehicle increases by 61.70% with the use of TransVerse. Moreover, a tradeoff between resource allocation and end-to-end delay of the network is also analyzed with and without TransVerse scenario.

Temperature monitoring system (TMS) aims to reduce the infection spread and outbreak of COVID-19 through early detection. Conventional and currently deployed TMS have high implementation cost and require a substantial amount of space. Also, the performance often depends on the accuracy of the thermal camera. To address this, we propose Edge TMS wherein a multi-task cascaded convolutional neural networks (MTCNN)-based TMS is deployed on an edge AI device. To overcome the resource constraints of edge AI devices, an optimization method is applied to compress MTCNN up to 100×. The compressed MTCNN is deployed on the local PC, Jetson Xavier, Jetson TX2, and Jetson Nano which yields a Pruning-Per-Reduction Ratio (PPRR) values of 1.21, 1.63, 1.99, and 2.10, respectively. We proposed the PPRR metric to measure the performance of the compressed model. Low PPRR values indicate an improvement in the hardware performance and computational efficiency of the optimized model. The optimized model deployed in all the Jetson series achieved an average percent power reduced (%R) of 53.18% with a percent difference of 35.9% from the results of the local PC.

Motivated by the six generation (6G) system’s stringent latency and throughput requirements, we propose a high performance dynamic bandwidth allocation (DBA) scheme for the next generation passive optical network (PON). The proposed algorithm performs simultaneous time and wavelengths scheduling, which make the optimization problem extremely complex. For this reason, we propose a novel adaptive neuro-fuzzy inference system based DBA (ANFIS-DBA) algorithm, which simplifies our complex scheduling algorithm. The proposed ANFIS-DBA has the latency and traffic cost as inputs and the allocated channels and bandwidth as output. Its prediction model is based on input–output experimental data generated from the original optimization problem which is then used to create a Sugeno type fuzzy inference system (FIS) for estimating the number of channels that will be scheduled for an optical network unit (ONU). Predicted data resulting from the proposed ANFISDBA are compared with the optimal values, indicating that both models perform nearly the same with a root mean square error (RMSE) of less than one. In addition, a sensitivity analysis is conducted showing that the proposed ANFIS-DBA is more sensitive to the traffic cost input. The numerical achievements of the paper show that the proposed ANFIS-DBA is capable of accommodating a network throughput higher than 1.5 Tbps while securing a latency and jitter below 100 μs and 10 μs, respectively, which fully meets the 6G network performance requirements.

Social distance monitoring (SDM) systems are vital in fighting the spread of the coronavirus (COVID-19). Existing SDM systems employ bounding box-method, which imposes inaccurate distance estimation due to the high variance in its output coordinates. To solve this problem, an SDM system based on multitask cascaded convolutional neural networks (MTCNN) is proposed. Instead of using bounding box coordinates, face detection and facial landmarks localization of MTCNN are used to provide fixed coordinates and increase the distance estimation accuracy of SDM. However, while the accuracy issue is solved by using MTCNN, the SDM system suffer from large computational requirements due to the cascaded networks added on top of the distance estimation process. To deal with this challenge, a constrained optimization technique is employed to each stage of MTCNN with the goal of reducing its hardware requirements while keeping the same reliability as the original implementation. Experimental results show that the SDM system based on the optimized MTCNN achieves higher accuracy performance with reduced computational requirements as compared with conventional SDM systems. This allows the proposed SDM system using optimized MTCNN to be deployed efficiently on edge devices.

Smart streetlight is an outdoor infrastructure that uses technologies, such as sensors and actuators, to provide intelligent outdoor lighting and replace the power-consuming traditional streetlights. Although the current smart streetlight systems are employing these technologies for the maintenance, they simply gather data wirelessly on a periodical basis and still have drawbacks to provide real-time monitoring operation. To address these issues, the implementation of an IoT-based low-delay smart streetlight monitoring system is proposed in this article. In addition, a data filtering algorithm is also proposed in this article where redundant data are ignored to avoid overloading and excessive data storage consumption. Implementation results show that the proposed monitoring system and data filtering algorithm are able to provide real-time monitoring of smart streetlights with minimal time execution up to 0.11 ms and greatly reduce data storage usage up to 88.57%, respectively.

This article presents a novel cloud-based charging management system for electric vehicles (EVs). Two levels of cloud computing, i.e., local and remote clouds, are employed to meet the different latency requirements of the heterogeneous EVs while exploiting the lower-cost computing in remote clouds. Specifically, we consider time-sensitive EVs at highway exit charging stations and EVs with relaxed timing constraints at parking lot charging stations. We propose algorithms for the interplay among EVs, charging stations, system operator, and clouds. Considering the contention-based random access for EVs to a 4G Long-Term Evolution network, and the quality of service metrics (average waiting time and blocking probability), the model is composed of: queuing-based cloud server planning, capacity planning in charging stations, delay analysis, and profit maximization. We propose and analyze aprice-incentive methodthat shifts heavy load from peak to off-peak hours, acapacity expansion methodthat accommodates the peak demand by purchasing additional electricity, and a hybrid method of price incentives and capacity expansion that balances the immediate charging needs of customers with the alleviation of the peak power grid load through price-incentive based demand control. Numerical results demonstrate the effectiveness of the proposed methods and elucidate the tradeoffs between the methods.

While 5G was primarily developed to address the anticipated capacity growth demand and enable the increasing importance of IoT based industry verticals, 6G will require a substantially more holistic approach by embracing a much wider community. 6G will become more human-centered than 5G, putting people at the center of a future super-smart society. In this paper, we elaborate on the anticipated shift of research focus from an IoT based Industry 4.0 to a more human-centered Society 5.0 paradigm in future 6G mobile networks, whereby so-called cyber-physical-social systems (CPSS) will play a central role. To illustrate the potential of CPSS, we present two prominent examples of human-in-the-loop centric systems: The emerging Tactile Internet, one of the most interesting 5G low-latency applications, and the future Internet of No Things, an important stepping stone toward ushering in the 6G post-smartphone era. Finally, we take a brief trip into the emerging field of robonomics in the 6G era and touch on the potential of advanced blockchain technologies such as oracles to enable the on-chaining of blockchain-external off-chain information stemming from human users. In doing so, they leverage on human intelligence rather than machine learning only for the social integration of robots into human society.

Remote robotic surgery, one of the most interesting 5G-enabled Tactile Internet applications, requires an ultra-low latency of 1 ms and high reliability of 99.999%. Communication disruptions such as packet loss and delay in remote robotic surgery can prevent messages between the surgeon and patient from arriving within the required deadline. In this paper, we advocate for scalable Gaussian process regression (GPR) to predict the contents of delayed and/or lost messages. Specifically, two kernel versions of the sequential randomized low-rank and sparse matrix factorization method ($\ell _{1}$-SRLSMF and SRLSMF) are proposed to scale GPR and address the issue of delayed and/or lost data in the training dataset. Given that the standard eigen decomposition for online GPR covariance update is cost-prohibitive, we employ incremental eigen decomposition in$\ell _{1}$-SRLSMF and SRLSMF GPR methods. Simulations were conducted to evaluate the performance of our proposed$\ell _{1}$-SRLSMF and SRLSMF GPR methods to compensate for the detrimental impacts of excessive delay and packet loss associated with 5G-enabled Tactile Internet remote robotic surgery. The results demonstrate that our proposed framework can outperform state-of-the-art approaches in terms of haptic data generalization performance. Finally, we assess the proposed framework’s ability to meet the Tactile Internet requirement for remote robotic surgery and discuss future research directions.

Wireless propagation loss modeling has gained significant attention due to its critical importance in forthcoming dynamic wireless technologies. Stochastic and map-based propagation models require more information (elevation extension, statistical scattering characteristics) than required by empirical models (i.e., operating frequency, distance between transceivers, and height of the antennas), but such information is not always available. Thus, empirical models are still widely used to evaluate coverage, link budget, and received signal strength. The drawback of empirical models is inaccuracy in highly dynamic transmitter and receiver environments. To reduce the error caused by the use of a single environment, we divide a geographical terrain to employ a specific propagation model in each segment of the wireless link. We enhance a deep learning (DL) encoder-decoder architecture to extract semantic information from satellite imagery to divide an environment into three classes. Our DL architecture achieved a segmentation accuracy of 89.41%, 86.47%, and 87.37% in urban, suburban, and rural classes, respectively. Simulation results indicate that estimating propagation loss with our multi-environment model reduced the root mean square deviation (RMSD) with respect to two publicly available wireless tracing datasets, CU-WART and Portland MetroFi, by 3.79dB and 4.09dB, respectively.

Multi-access edge computing (MEC) is an emerging paradigm to meet the rapidly growing computation demands of mobile applications. This paper investigates the performance gains of cooperative computation offloading for MEC enabled FiWi enhanced HetNets with capacity-limited backhaul links. After presenting the envisioned two-tier MEC architecture for a FiWi based networking infrastructure, we propose a simple but efficient offloading strategy, which relies on the flexible trilateral cooperation between end-device, edge servers, and the remote cloud. We then present an analytical framework to estimate the average response time and energy consumption of mobile users for various offloading scenarios with different wireless access modes (i.e., WiFi and 4G LTE-A). The presented analysis flexibly allows for incorporating both offloaded and conventional human-to-human (H2H) traffic of mobile users as well as fixed (wired) subscribers. Finally, we present our self-organization based mechanism, which enables mobile users to make suitable energy-delay trade-offs by jointly minimizing the average task execution time and energy consumption, using only their local information. The obtained results demonstrate the feasibility of the proposed cooperative self-organizing offloading strategy and its superior performance over schemes with MEC- or cloud-only offloading strategies.

In this paper, we propose new scheduling algorithms to improve the performance of the code division multiple access (CDMA) enabled next generation Ethernet passive optical networks (NG- EPON). In the NG-EPON standard, stringent requirements are applied to the packet delay. In addition, lowering the end-to-end latency/jitter will be instrumental in the realization of future 5G applications. For this reason, we propose two new scheduling algorithms for NG-EPON to assign a grant size on each upstream code in order to guarantee the newly required network throughput and latency performance of 5G systems. Results show that both newly proposed scheduling schemes can significantly improve the network performance in terms of delay/Jitter and throughput. In addition, the results are compared to numerous dynamic wavelength and bandwidth allocation (DWBA) algorithms. It is shown that CDMA can be a good alternative for WDM for multi-channels based NG-EPON. Also, it is shown that the proposed algorithms can reach a high capacity and a very low latency/jitter to accommodate 5G services.

Cooperative human–agent–robot teamwork (HART) provides enormous opportunities for present-day human users to orchestrate their real-time tasks in a coordinated fashion. However, given human users’ different preferences for real-time HART task execution, e.g., lower delay and monetary cost, the selection of proper task coordination services has emerged as an important research problem by taking dynamically changing cloud agent/robot resources, network bandwidth utilization, as well as delay-sensitive and delay-tolerant HART task properties into account. To cope with these challenges, in this paper, we explore the synergy between caching, computation, and communications for achieving cost-effective HART task execution. To exploit the locality of different HART-centric tasks and local/non-local cloud agent/robot resources for different HART-centric task execution, we consider integrated fiber-wireless (FiWi) enhanced networks with computation task offloading as well as fiber backhaul sharing and WiFi offloading capabilities. More precisely, to minimize task execution delay and monetary cost, we propose a user preference aware HART task coordination framework that selects the appropriate dedicated or non-dedicated robot and cloud agent for given caching and computing HART task execution requirements. Further, to cope with varying bandwidth resources, we propose a proactive bandwidth allocation policy for the execution of both delay-sensitive and delay-tolerant HART tasks execution across FiWi enhanced network infrastructures. We evaluate the performance of our proposed preference aware task offloading scheme and compare it to various baseline schemes in terms of different key performance indicators, including the task execution time and monetary cost saving ratio, communication to computation ratio, and offloading gain overhead ratio. Our findings indicate that the proposed delay cost saving policy exhibits a 27% higher task execution time saving ratio and a 48% lower monetary cost saving ratio than the proposed monetary cost saving policy in a typical scenario.

With the advent of safe collaborative robots, their seamless integration into human teams as teammates is starting to gain steam as part of the vision of the emerging Tactile Internet. The Tactile Internet lies at the nexus of computerization, automation, and robotization. While necessary, low task execution time and ultra-reliable human-robot connectivity are not sufficient to unleash the full potential of the resultant human-agent-robot teamwork (HART) applications. In this paper, we propose a context- and self-aware HART-centric allocation scheme for both physical and digital tasks to coordinate the automation and augmentation of mutually beneficial human-machine coactivities while spreading ownership of robots across users over integrated fiber-wireless (FiWi) Tactile Internet infrastructures. In addition to realizing collective context-awareness via HART-centric task coordination, we aim at exploiting local self-awareness in order to improve the energy-delay performance of robots. Further, we present an analytical framework to estimate the packet transmission delay and human-robot connection reliability. Our results indicate that our proposed context- and self-aware HART-centric task coordination scheme obtains a low task execution time while minimizing the energy consumption and operational expenditures (OPEX) of mobile robots.

With the advent of semi-autonomous robotic assistance systems, their integration into human teams is starting to gain steam as part of the vision of human+machine hybrid activities. Unlike their fully autonomous counterparts, semi-autonomous robotic systems mainly rely on human assistance from time to time via teleoperation when human expertise is needed to accomplish a given task. As these robots will need to request human assistance via teleoperation, mapping these requests to human teleoperators stands as a difficult optimization problem. In this paper, after shedding some light on our envisioned FiWi enhanced network infrastructure and its role in realizing the emerging Tactile Internet, we formulate the problem of joint prioritized scheduling and assignment of delay-constrained teleoperation tasks to human operators with the objective to minimize the average weighted task completion time, maximum tardiness, and average operational expenditure (OPEX) per task. We then propose our context-aware prioritized scheduling and task assignment (CAPSTA) algorithm to achieve suitable trade-offs between the contradicting objectives of the problem. Further, to estimate the end-to-end packet delay of local and non-local teleoperation over FiWi enhanced networks, we develop our analytical framework, which flexibly allows for the coexistence of conventional human-to-human (H2H) and haptic human-to-machine (H2M) traffic.

Low task execution time and low energy consumption of collaborating mobile human users and robots are important requirements of emerging human-agent-robot teamwork (HART)-centric Tactile Internet applications. In particular, task migration among mobile HART members has emerged as an important research topic, taking different task types, task deadlines, collaborative node capabilities, and mobility patterns into account. We propose a context-aware task migration scheme for efficiently orchestrating the real-time collaboration among human mobile users, central and decentralized computational agents (cloud/cloudlets), and collaborative robots (cobots) across converged fiber-wireless (FiWi) communications infrastructures. We investigate the problem of whether and, if so, when and where a HART-centric task should be best migrated to. For resource-efficient task execution, the migration decision is made according to given task processing capabilities of cloud/cloudlet agents and cobots, task execution deadline, energy consumption of involved cobots and mobile devices, and task migration latency. We evaluate the performance of our context-aware HART-centric task migration scheme and compare it to conventional task execution without migration. Towards this end, we develop an analytical framework for quantifying its performance in terms of a variety of task migration key performance metrics, including task migration gain-overhead ratio, deadline-miss ratio, task response time, and energy consumption efficiency.

With the emergence of the Tactile Internet and advent of remote-controlled robots, proper task allocation among robots has attracted significant attention to enable robotic applications and services based on the human-to-robot communications paradigm. However, limited computing, energy, and storage resources of robots may hinder the successful launch of such applications. Task offloading to collaborative nodes is a promising approach to improve the task execution time and energy efficiency of robots. In this paper, we investigate a proper task allocation strategy by combining suitable host robot selection and computation task offloading onto collaborative nodes. We exploit conventional cloud, decentralized cloudlets, and neighboring robots as collaborative nodes for computation offloading in support of a host robot’s task execution. More specifically, our proposed task allocation policy selects a suitable robot based on several key parameters, including robot availability, remaining energy, and task execution time. Furthermore, our proposed computation offloading strategy examines the suitability of collaborative nodes in terms of task response time and energy consumption and then chooses an appropriate collaborative node to conduct the requested computation. We introduce an adaptive resource allocation model and develop an analytical framework to evaluate the task allocation delay, energy consumption, and task response time for noncollaborative and collaborative task execution scheme across integrated fiber-wireless multirobot networks. The results show that the proposed collaborative task execution scheme outperforms the noncollaborative scheme in terms of task response time and energy consumption efficiency.

Demand response (DR) plays a key role for optimum asset utilization and to avoid or delay the need of new infrastructure investment. However, coordinated execution of multiple DRs is desired to maximize the DR benefits. In this study, we propose a hierarchical DR architecture (HDRA) to control and coordinate the performance of various DR categories such that the operation of every DR category is backed-up by time delayed action of the others. A reliable, cost-effective communication infrastructure based on ZigBee, WiMAX, and fibers is designed to facilitate the HDRA execution. The performance of the proposed HDRA is demonstrated from the power system and communication perspectives in a cosimulation environment applied to a 0.4 kV/400 kVA real distribution network considering electric vehicles as a potential DR resource (DRR). The power simulation is performed employing a real time digital simulator whereas the communication simulation is performed using OMNeT++. The HDRA performance demonstrated the maximum utilization of available DR potential by facilitating simultaneous execution of multiple DRs and enabling participation of single DRR for multiple grid applications.

The advent of Internet of Things and 5G applications renders the need for integration of both centralized cloud computing and emerging mobile-edge computing (MEC) with existing network infrastructures to enhance storage, processing, and caching capabilities in not only centralized but also distributed fashions for supporting both delay-tolerant and mission-critical applications. This paper investigates performance gains of centralized cloud and MEC enabled integrated fiber-wireless (FiWi) access networks. A novel unified resource management scheme incorporating both centralized cloud and MEC computation offloading activities into the underlying FiWi dynamic bandwidth allocation process is proposed. Both MEC and cloud traffic are scheduled outside the transmission slot of FiWi traffic by leveraging time division multiple access. An analytical framework is developed to model packet delay, response time efficiency, gain-offload overhead ratio, and communication-to-computation ratio for both cloud and broadband access traffic. In addition, given the importance of reliability in optical backhaul and MEC, this paper develops a probabilistic survivability analysis model to assess the impact of both fiber cuts and MEC server failures. The obtained results demonstrate the feasibility of implementing conventional cloud and MEC in FiWi access networks, without affecting network performance of broadband access traffic.

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular, the delay performance of FiWi enhanced LTE-Advanced (LTE-A) heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints. We use recent and comprehensive smartphone traces of the PhoneLab data set to verify whether the previously reported assumption that the complementary cumulative distribution function of both WiFi connection and interconnection times fit a truncated Pareto distribution is still valid. In this paper, we put a particular focus on the 5G key attributes of very low latency and ultra-high reliability and investigate how they can be achieved in FiWi enhanced LTE-A HetNets. Furthermore, given the growing interest in decentralization of future 5G networks (e.g., user equipment assisted mobility), we develop a decentralized routing algorithm for FiWi enhanced LTE-A HetNets.

Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource management in the context of multi-tenant cloud computing is becoming one of the most complex task due to the inherent heterogeneity and resource isolation. This paper proposes a novel cloud-based workflow scheduling (CWSA) policy for compute-intensive workflow applications in multi-tenant cloud computing environments, which helps minimize the overall workflow completion time, tardiness, cost of execution of the workflows, and utilize idle resources of cloud effectively. The proposed algorithm is compared with the state-of-the-art algorithms, i.e., First Come First Served (FCFS), EASY Backfilling, and Minimum Completion Time (MCT) scheduling policies to evaluate the performance. Further, a proof-of-concept experiment of real-world scientific workflow applications is performed to demonstrate the scalability of the CWSA, which verifies the effectiveness of the proposed solution. The simulation results show that the proposed scheduling policy improves the workflow performance and outperforms the aforementioned alternative scheduling policies under typical deployment scenarios.

This paper proposes to enhance capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with computation- and storage-centric cloudlets to provide reliable cloud services at the edge of FiWi networks and thereby realize the vision of mobile-edge computing (MEC). To reduce offload delay and prolong battery life of edge devices, a novel cloudlet-aware resource management scheme is proposed that incorporates offloading activities into the underlying FiWi dynamic bandwidth allocation process. The whole system is designed in two time division multiple access layers to enhance the network performance. To allow for the efficient coexistence of FiWi and MEC traffic, the offloaded traffic is scheduled outside the FiWi transmission slots. To thoroughly study the scheme's performance, a comprehensive analytical framework is developed that covers a rich set of performance metrics, including packet delay of both FiWi and MEC traffic, response time efficiency, offload gain-overhead ratio, energy efficiency, and battery life. Analytical results demonstrate the feasibility and effectiveness of the cloudlet-enhanced FiWi networks for MEC by employing the proposed solution. Further, we develop an experimental testbed to validate the accuracy of our analytical model via real-world measurements.

This paper aims at designing an ecoconscious future-proof sensor enhanced fiber-wireless (SFiWi) network based on EPON, WLAN, wireless sensor (WS), and fiber optic sensor (FOS) technologies as a shared communications infrastructure for broadband access and smart grids. A total cost of ownership (TCO) model is developed to help utilities decide whether to deploy WSs or FOSs in different scenarios and estimate sensor-related costs. To prolong battery life of wireless devices and maximize the overall energy efficiency, a novel energy conservation scheme for SFiWi networks (ECO-SFiWi) is proposed. ECO-SFiWi designs the whole network in three TDMA layers to enhance network performance, while scheduling network components to sleep outside their transmission slots. A comprehensive energy saving model accounting for both optical backhaul and wireless front-end components and a delay analysis based on M/G/1 queuing are presented. Results reveal that with their extremely long lifetime and ability to sustain in harsh environments, FOSs are superior to WSs when advanced interrogation techniques are deployed to reduce their total cost. ECO-SFiWi achieves more than 89% of energy savings, while maintaining low delay for both broadband and smart grid traffic in typical scenarios. FPGA hardware emulation and analytical results match well verifying the effectiveness of ECO-SFiWi.

Fiber-wireless sensor networks (Fi-WSNs) composed of a hybrid fiber-wireless (FiWi) network enhanced with sensors will play a key role in supporting machine-to-machine (M2M) communications to enable a wide range of Internet of Things (IoT) applications, of which smart grids represent an important real-world example. This paper explores opportunities of designing an energy-efficient Fi-WSN based on EPON/10G-EPON, WLAN, wireless sensors, and passive fiber optic sensors as a shared communications infrastructure for broadband services and smart grids. A novel energy conservation scheme for sensor enhanced FiWi networks (ECO-SFiWi) is proposed to reduce the overall energy consumption. ECO-SFiWi maximizes energy efficiency by leveraging TDMA to schedule power-saving modes of EPON's optical network units, wireless stations, and wireless sensors and incorporate them into EPON's bandwidth allocation algorithm. To study the performance, a comprehensive energy saving model and a delay analysis of both FiWi traffic and sensor data based on M/G/1 queue modeling are presented. FPGA-based hardware emulation and demonstration are performed to verify the effectiveness of the proposed solution. Results provide deep insights into the tradeoff between energy savings and frame delays. Noticeably, ECO-SFiWi achieves significant amounts of energy saving, while maintaining low delay for FiWi traffic and sensor data under typical deployment scenarios.

Integrated fiber-wireless (FiWi) access networks aim at taking full advantage of the reliability and high capacity of the optical backhaul along with the flexibility, ubiquity, and cost savings of the wireless/cellular front-end to provide broadband services for both mobile and fixed users. In FiWi access networks, energy efficiency issues must be addressed in a comprehensive fashion that takes into account not only wireless front-end but also optical backhaul segments to extend the battery life of wireless devices and allow operators to reduce their OPEX, while not compromising quality of service (QoS). This paper proposes an energy conservation scheme for FiWi networks (ECO-FiWi) that jointly schedules power-saving modes of wireless stations and access points and optical network units to reduce their energy consumption. ECO-FiWi maximizes the overall network performance by leveraging TDMA to synchronize the power-saving modes and incorporate them into the dynamic bandwidth allocation (DBA) process. A comprehensive energy saving model and an M/G/1 queuing-based analysis of downstream and upstream end-to-end frame delays are presented accounting for both backhaul and front-end network segments. Analytical results show that ECO-FiWi achieves significant amounts of energy saving, while preserving upstream delay and incurring a low delay for downstream traffic.

Google recently introduced U.S. pilot Project Fi in cooperation with two major 4G LTE network operators. Project Fi intelligently connects mobile users to free open WiFi hotspots or otherwise, if not possible, seamlessly moves them between the two partner LTE networks for delivering the most reliable and fastest available wireless service. Project Fi may be an important step towards meeting the 5G requirements of ultra-high reliability and very low latency while taking economic considerations into account. Following the integrative vision of 5G, this paper attempts to provide further insights into the potential of Project Fi wireless service by elaborating on the integration of 4G LTE-Advanced (LTE-A) heterogeneous networks (HetNets) and low-cost data-centric Ethernet based fiber-wireless (FiWi) broadband access networks with not only WiFi offloading but also cost-saving fiber infrastructure sharing capabilities for small cell backhaul, which is becoming a major performance-limiting factor in mobile networks. Furthermore, given that decentralization is another important aspect of the 5G vision, we develop a decentralized routing algorithm for the resultant FiWi enhanced LTE-A HetNets.

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular the delay performance of FiWi enhanced LTE-A heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints.

In this paper, we shed some light on the latency and reliability issues of mobile backhaul networks, which have been largely ignored in the past, and examine their impact on LTE-A heterogeneous networks (HetNets). Specifically, we propose a backhaul-aware user association algorithm for fiber-wireless (FiWi) enhanced LTE-A HetNets. The performance limiting factors of state-of-the-art fiber backhaul infrastructures are highlighted and a variety of solutions are described. To mitigate the vulnerability of the backhaul against fiber cuts, we introduce different advanced protection techniques. Accounting for the given conditions of the backhaul in terms of delay and reliability, we present a distributed load-balancing algorithm for user association in FiWi-LTE HetNets. The proposed algorithm is analyzed and evaluated numerically by comparing its performance with state-of-the-art alternative approaches in terms of average delay, blocking probability, average achievable throughput, and service interruption percentage. The obtained results demonstrate that our algorithm outperform its counterparts in terms of delay and service interruption percentage, while its average achievable throughput is the same as that of a backhaul-unaware alternative solution. In addition, the blocking probability of the proposed backhaul-aware load-balancing method is shown to be higher than that of backhaul-unaware ones.

In this paper, we analyze the exact node- and network-wise operation scenarios in elastic optical networks (EONs). First, the busy/idle patterns of optical spectrum in a stand-alone node are modeled by utilizing a continuous Markov chain. In the node-wise perspective, four operation scenarios are investigated, which are determined based on the spectrum allocation methods. We present an algorithmic procedure to derive the global balance equations of the corresponding Markov chains for all operation scenarios. Furthermore, the network-wise operation is assessed by analyzing the end-to-end blocking probability for two operation modes, with and without spectrum conversion capabilities at the intermediate nodes. Because the computational complexity of exact model increases exponentially versus the number of spectrum slots, we present two approximate alternatives. The results of the exact models, approximations, and verifying simulations are compared for small scale problem. Comparison reveals that the exact model and simulation match very well. In addition, the accuracy of both approximations is acceptable. The approximate solutions are also examined under large scale scenarios by considering simulation results as a benchmark. The accuracy of the first approximation is not degraded in large scale cases, as opposed to the second one, which is only applicable to small scale problems.

In this paper, we study the performance of multi-tier integrated fiber-wireless (FiWi) smart grid communications infrastructures based on low cost, simple, and reliable next-generation passive optical network (PON) with quality-of-service (QoS) enabled wireless local area networks (WLANs) in terms of capacity, latency, and reliability. We study the coexistence of human-to-human (H2H), e.g., triple-play traffic and machine-to-machine (M2M) traffic originating from wireless sensors operating on a wide range of possible configurations. Our analysis enables the quantification of the maximum achievable data rates of both event- and time-driven wireless sensors without violating given upper delay limits of H2H traffic. By using experimental measurements of real-world smart grid applications, we investigate the impact of variable H2H traffic loads on the sensor end-to-end delay performance. The obtained results show that a conventional Ethernet PON may cause a bottleneck and increase the delay for both H2H and M2M traffic. In contrast, by using a 10 G-EPON or wavelength division multiplexing (WDM) PON, the bottleneck arises in the wireless network. Furthermore, we study the interplay between time- and event-driven nodes and show that the theoretical upper bound of time-driven sensors decreases linearly as a function of the number of sensors, whereas with event-driven sensors, the upper bound decrease is nonlinear and more pronounced.

Availability is one of the most important quality attributes for smart grid communications, as qualitatively defined in the IEEE P2030 standard. However, the availability metric must be quantified to validate given smart grid application requirements. In recent related work, availability has been quantified for wireless and optical backhaul networks in terms of communications reachability, while in some other work availability was not formally defined in a fine-grained manner and was assumed to be known. In this paper, we develop a novel multi-class probabilistic availability model for integrated passive optical network (PON) and WiMAX networks to quantify this metric according to medium access control (MAC) protocol limits as well as fiber and base station failures. The obtained numeric results show interesting availability behaviors, including the impact on availability depending on the number of base stations. We also investigate optical traffic re-routing through WiMAX when fiber faults occur and show that there exists a maximum amount of re-routed traffic for maximizing availability. Furthermore, we investigate a scenario of real-world smart grid traffic configurations shared with regular traffic and find the maximum sensor data rate to meet the availability requirements.

Current Gigabit-class passive optical networks (PONs) evolve into next-generation PONs, whereby high-speed 10+ Gb/s time division multiplexing (TDM) and long-reach wavelength-broadcasting/routing wavelength division multiplexing (WDM) PONs are promising near-term candidates. On the other hand, next-generation wireless local area networks (WLANs) based on frame aggregation techniques will leverage physical-layer enhancements, giving rise to Gigabit-class very high throughput (VHT) WLANs. In this paper, we develop an analytical framework for evaluating the capacity and delay performance of a wide range of routing algorithms in converged fiber-wireless (FiWi) broadband access networks based on different next-generation PONs and a Gigabit-class multiradio multichannel WLAN-mesh front end. Our framework is very flexible and incorporates arbitrary frame size distributions, traffic matrices, optical/wireless propagation delays, data rates, and fiber faults. We verify the accuracy of our probabilistic analysis by means of simulation for the wireless and wireless-optical-wireless operation modes of various FiWi network architectures under peer-to-peer, upstream, uniform, and nonuniform traffic scenarios. The results indicate that our proposed optimized FiWi routing algorithm (OFRA) outperforms minimum (wireless) hop and delay routing in terms of throughput for balanced and unbalanced traffic loads, at the expense of a slightly increased mean delay at small to medium traffic loads.

According to the Organisation for Economic Co-operation and Development (OECD), broadband access networks enable the emergence of new business models, processes, inventions, as well as improved goods and services. In fact, broadband access is viewed as a so-called general purpose technology (GPT) that has the potential to fundamentally change how and where economic activity is organized. In this paper, we focus on the implications of the emerging Third Industrial Revolution (TIR) economy, which goes well beyond current austerity measures, and has recently been officially endorsed by the European Commission as the economic growth roadmap toward a competitive low carbon society by 2050. This roadmap has been receiving an increasing amount of attention by other key players, e.g., the Government of China most recently. More specifically, we describe a variety of advanced techniques to render converged bimodal fiber-wireless (FiWi) broadband access networks dependable, including optical coding based fiber fault monitoring techniques, localized optical redundancy strategies, wireless extensions, and availability-aware routing algorithms, to improve their reliability, availability, survivability, security, and safety. Next, we elaborate on how the resultant dependent FiWi access networks can be exploited to enhance the dependability of other critical infrastructures of our society, most notably the future smart power grid and its envisioned electric transportation, by means of probabilistic analysis, co-simulation, and experimental demonstration.

Toward the vision of complete fixed-mobile convergence, a plethora of wireless, integrated optical-wireless, multipath, and energy-aware routing algorithms were proposed for legacy EPON/WLAN-mesh based bimodal fiber-wireless (FiWi) broadband access networks. In this paper, we present the first comprehensive analytical framework for providing deeper insights into the capacity and delay performance of routing algorithms in next-generation FiWi networks based on emerging powerful optical and wireless technologies such as long-reach 10+ Gb/s TDM/WDM PONs and Gigabit-class VHT WLANs.

The increased propagation delay of future long-reach passive optical networks (LR-PONs) may lead to a significantly increased idle time and delay if optical network units (ONUs) use conventional report-grant mechanisms. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed propagation delay in LR-PONs. In this study, we evaluate three dynamic bandwidth allocation (DBA) frameworks in terms of frame (packet) delay; namely, we consider conventional (interleaved) polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We enhance MT-P and RT-P by applying the just-in-time framework. Next, we provide an analytical framework for evaluating the end-to-end frame delay in our enhanced MT-P and RT-P frameworks. We compare their performance with conventional polling and double-phase polling and investigate their shortcomings and advantages in an LR-PON setting. The simulation results closely match the analysis for this framework. Also, our results indicate that RT-P significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling frameworks.

Designing low latency polling schemes is one of the most important parts for passive optical networks (PONs), particularly for long-reach PONs (LR-PON) which suffer from long propagation delays. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed transmission delay in LR-PONs. In this work, we evaluate three dynamic bandwidth allocation methods in terms of transmission delay. Namely, we consider conventional or interleaved polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We examine various flavors of each scheduling method and investigate their shortcomings and advantages in a LR-PON setting. Furthermore, we provide an analytical framework for obtaining packet delay in an enhanced version of RT-P method. The simulation results highly match the analysis for this framework. Also, our results indicate that RT-P method significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling methods.

Survivability will play a more prominent role in emerging high-speed, multichannel, long-reach next-generation passive optical networks (NG-PONs) than it did in conventional PONs. In this paper, we report on some of the most promising NG-PON survivability techniques such as dual homing, hitless protection switching by means of equalization delays, interconnection fibers, protection rings, and meshed PON topologies, including their cost-benefit analysis for different population densities. We pay particular attention to the design of advanced in-service monitoring techniques for fault detection and localization in PON distribution fibers and study the merits of combining partial optical protection with wireless protection for realizing reliable fiber-wireless (FiWi) access networks and exploiting them to improve the survivability of other critical networks such as the future smart grid.

Ethernet Ring Protection (ERP) has recently emerged to provide protection switching for Ethernet ring topologies with sub-50 ms failover capabilities. ERP's promise to also provide protection in multi-ring mesh packet transport networks will position Ethernet as a serious competitor to conventional SONET/SDH and the technology of choice for carrier networks. Operating ERP in multi-ring mesh networks however comes with unique challenges. This paper presents an optimal ERP capacity design formulation by jointly solving the problem of routing, RPL placement, and ring hierarchy selection. When prior work relied on exhaustive enumeration, our current design is formulated as an integer linear program (ILP) which is shown to be both more capacity and computationally efficient.

Plug-in electric vehicles (PEVs) have great potential of being the alternative for the next-generation of transportation. Uncoordinated PEV charging, however, may put a significant pressure on the distribution grid. In this paper, on a modified IEEE-13 Node distribution network of 342 residential customers, we propose a converged fiber-wireless infrastructure based on EPON, WiMAX, wireless mesh network and sensor technologies to support coordinated charging of PEVs. To measure the performance of both the communications and power system perspectives of proactive scheduling algorithms and proposed reactive control protocols, our recently developed hybrid co-simulator based on OMNeT++ and OpenDSS is used. Co-simulation results show that the proposed low-cost communications infrastructure enables to efficiently schedule PEV chargings and quickly stabilize the voltage in a stress scenario.

Carrier Ethernet exhibits an enormous potential to be a cost-effective and less complex replacement of SONET/SDH especially after the ratification of IEEE standard 802.1Qay Provider Backbone Bridge-Traffic Engineering (PBB-TE). The recent IEEE standard 802.3az Energy Efficient Ethernet (EEE) presents another opportunity for service providers to select Ethernet as a technology of choice in the backbone while leveraging on its promise for achieving green transport networks. In this paper, we propose two novel architectures of Photonic PBB-TE (PPBB-TE) core and edge switches, which enhance the usability of PBB-TE networks by reducing power consumption in individual switches in conjunction with passive optical bypassing and EEE. We also formulate the problem of energy-aware scheduling as an optimization problem whose objective is to minimize the overall energy consumption for transmitting Ethernet frames while satisfying their delay requirements. This model will be used as a benchmark for evaluating the performance of packet coalescing, a recently proposed performance-enhancing technique, as well as the performance of EEE.

Dynamic bandwidth allocation and grant scheduling are among the major issues in the design of passive optical networks. In this paper, we investigate the problem of optimal scheduling and bandwidth allocation in next generation 10GEPON coexisting with 1G WDM-PONs. We first propose a network architecture for providing this coexistence. Then, we derive an ILP model for offline joint scheduling and bandwidth assignment for 10G-TDM and 1G-WDM ONUs. In order to address the scalability of the ILP model, we introduce a Tabu Search based heuristic for obtaining near optimal solutions while the computation time is remarkably reduced. We further explore the tradeoff which exists in terms of delay, scheduling length, and channel utilization, when separate or the same DBA modules are used for 1G- and 10G-ONUs.

This study addresses the problem of resource allocation for a multi-class time-slotted optical overlapped code-division multiple access network. A delay-constraint fair resource allocation (DC-FRA) scheme is considered with the quality of service requirements on both physical layer signal-to-interference ratio and network layer average packet delay. A cross-layer approach in allocating the transmission power and rate for every class of users in the network is considered. The performances of the S-ALOHA, R-ALOHA and R3T medium access control protocols have been investigated in the DC-FRA scheme. It is shown that the R-ALOHA protocol has the highest throughput and the minimum average packet delay. On the other hand, although the DC-FRA allocates the largest bandwidth under the R-ALOHA protocol, it forces the users to transmit with relatively higher power in comparison with the power required for the S-ALOHA and the R3T protocols. In addition, the packets exhibit much smaller delay when using the DC-FRA, especially at low and moderate throughput for the three protocols.

Building on the Ethernet Passive Optical Network (EPON) and Gigabit PON (GPON) standards, Next-Generation (NG) PONs (i) provide increased data rates, split ratios, wavelengths counts, and fiber lengths, as well as (ii) allow for all-optical integration of access and metro networks. In this paper we provide a comprehensive probabilistic analysis of the capacity (maximum mean packet throughput) and packet delay of subnetworks that can be used to form NG-PONs. Our analysis can cover a wide range of NG-PONs through taking the minimum capacity of the subnetworks forming the NG-PON and weighing the packet delays of the subnetworks. Our numerical and simulation results indicate that our analysis quite accurately characterizes the throughput-delay performance of EPON/GPON tree networks, including networks upgraded with higher data rates and wavelength counts. Our analysis also characterizes the trade-offs and bottlenecks when integrating EPON/GPON tree networks across a metro area with a ring, a Passive Star Coupler (PSC), or an Arrayed Waveguide Grating (AWG) for uniform and non-uniform traffic. To the best of our knowledge, the presented analysis is the first to consider multiple PONs interconnected via a metro network.

Passive optical networks (PONs) are currently evolving into next-generation PONs (NG-PONs) which aim at achieving higher data rates, wavelength channel counts, number of optical network units (ONUs), and extended coverage compared to their conventional counterparts. Due to the increased number of stages and ONUs, NG-PONs face significant challenges to provide the same level of survivability like conventional PONs without exceeding the budget constraints of cost-sensitive access networks. Toward this end, partial optical protection, in combination with interconnecting a subset of ONUs through a wireless mesh network (WMN) front-end, are promising solutions to render NG-PONs survivable in a cost-effective manner. In this paper, we present a probabilistic analysis of the survivability of NG-PONs and hybrid fiber-wireless (FiWi) access networks, taking both optical and wireless protection into account. In addition, we propose different selection schemes to wirelessly upgrade a subset of ONUs, and investigate their performance for a wide range of fiber link failure scenarios and different NG-PON topologies.

In this paper, we focus on the MAC enhancements of emerging high-throughput WLANs and introduce a novel fiber-wireless (FiWi) network architecture that integrates next-generation WLAN and EPON in a pay-as-you-grow manner while providing backward compatibility with legacy infrastructure and protecting previous investments. Our work sheds light onto the benefits of extending advanced frame aggregation techniques from next-generation WLANs to EPONs as well. We examine different path selection schemes to improve FiWi network performance by means of wireless mesh networking, considering not only throughput and delay but also overhead and packet loss.

Bandwidth allocation and transmission grant scheduling are problems of particular interests to multichannel Passive Optical Networks (PONs). While a number of studies have been carried out on each of these subproblems separately, to the best of our knowledge a study on the joint problem has been lacking. In this paper, we investigate the joint problem of bandwidth allocation and transmission grant scheduling in multichannel optical access networks using a scheduling theoretic approach. We formulate the problem as an ILP model and compare it with the sequential model presented in pervious work. Our experiments declare that the joint scheduling and sizing algorithm achieves a reduction of up to 28% in scheduling cycle length when compared to the non-joint models. Since the model has shown to be very hard to solve, except for small sized network instances, we introduce a Tabu search heuristic for the joint problem that provides near optimal solutions in significantly shorter times. We also illustrate that the choice of buffer size of ONUs has a critical rule in the performance of our joint scheduling and bandwidth allocation solutions.

Hybrid fiber-wireless (FiWi) networks become rapidly mature and represent a promising candidate for reducing power consumption, costs, and bandwidth bottlenecks of next-generation broadband access networks. Two key FiWi technologies with similar design goals are Ethernet passive optical network (EPON) and WiMAX. In this paper, we develop a powerful and flexible techno-economic analysis to compare the two technologies, taking into account not only equipment and installation costs but also OAM related costs such as power consumption and repairing costs for a wide range of different network failure scenarios, terrain types, and wireless channel conditions. The presented results give insight into the cost-performance trade-offs of EPON and WiMAX networks.

At present there is a strong worldwide push towards bringing fiber closer to individual homes and businesses. Another evolutionary step is the cost-effective all-optical integration of fiber-based access and metro networks. STARGATE [1] is an all- optical access-metro architecture which does not rely on costly active devices and makes use of an overlay island of transparency with optical bypassing capabilities. In this paper we propose ONU architectures, and discuss several technical challenges, which allow STARGATE EPONs (SG-EPONs) to evolve in a pay-as-you grow manner while providing backward compatibility with legacy infrastructure and protecting previous investment. Using a simple bandwidth and wavelength allocation, we study the capacity and delay performance of SG-EPON through simulations.

Packet-switched unidirectional and bidirectional ring wavelength division multiplexing (WDM) networks with destination stripping provide an increased capacity due to spatial wavelength reuse. Besides unicast traffic, future destination stripping ring WDM networks also need to support multicast traffic efficiently. This article examines the largest achievable transmitter throughput, receiver throughput, and multicast throughput of both unidirectional and bidirectional ring WDM networks with destination stripping. A probabilistic analysis evaluates both the nominal capacity, which is based on the mean hop distances traveled by the multicast packet copies, and the effective capacity, which is based on the ring segment with the highest utilization probability, for each of the three throughput metrics. The developed analytical methodology accommodates not only multicast traffic with arbitrary multicast fanout but also unicast and broadcast traffic. Numerical investigations compare the nominal transmission, receiver, and multicast capacities with the effective transmission, receiver, and multicast capacities and examine the impact of number of ring nodes and multicast fanout on the effective transmission, reception, and multicast capacity of both types of ring networks for different unicast, multicast, and broadcast traffic scenarios and different mixes of unicast and multicast traffic. The presented analytical methodology enables the evaluation and comparison of future multicast-capable medium access control (MAC) protocols for unidirectional and bidirectional ring WDM networks in terms of transmitter, receiver, and multicast throughput efficiency.

EPONs are designed to deliver services for multiple applications, such as VoIP, standard and high-definition video, interactive video and best effort traffic. We present the first framework that will enable for per-stream/flow QoS protection in EPON networks using a two stage admission control (AC) system. While the first stage enables the ONU to perform flow admission locally according to the bandwidth availability, the second stage allows for global admission control at the OLT. Appropriate bandwidth allocation algorithms are presented as well. An event driven simulation model is implemented to study the effectiveness of the proposed scheme in providing and protecting QoS.

We propose an improved online scheduler for multichannel or Wavelength Division Multiplexed (WDM) Ethernet Passive Optical Network (EPON) upstream transmission. This scheduler employs a just-in-time online scheduling framework to increase the number of Optical Network Units (ONUs) that can be scheduled concurrently. We outline the overall structure of this scheduling framework and discuss adapting offline scheduling policies for use in this framework. We compare the average queueing delay performance of different schedulers that follow this new framework to a simple online scheduler that schedules ONUs as soon as their REPORTS are received at the Optical Line Terminal (OLT). Further, we show how this framework can be used to provide differentiated service to ONUs without waiting for all ONUs to REPORT. We conclude with some remarks regarding our performance findings and possibilities for future research.

Traffic forecasting is an important means to facilitate network capacity planning by predicting necessary link upgrades and link additions, but it is generally unable to take into account events that are hard to predict, e.g., breaking news, denial-of-service attacks, and failures. These events, which may be typically short-lived but whose impact on the network traffic load is significant, together with the hard to predict traffic due to the server farm based architecture of the Internet will increase the amount of unpredictable traffic. The so-called network lifetime metric, which measures the ability of a network to sustain unexpected changes and shifts in traffic load, is becoming increasingly important. Very recently reported preliminary results show that the widely deployed bidirectional rings provide the smallest network lifetime, whereas chordal rings perform best in terms of network lifetime. In this paper, we first provide extensive numerical investigations of the network lifetime of bidirectional rings for realistic initial traffic scenarios. We then provide more detailed insights into the network lifetime of chordal rings and show that chordal rings not necessarily outperform bidirectional rings. Finally, we examine meshed rings and a novel hybrid ring-star network which clearly outperform not only bidirectional but also chordal rings in terms of network lifetime for a wide range of unexpected traffic changes and shifts

We examine a recently proposed multichannel upgrade of optical single-channel ring networks where a subset of ring nodes is WDM upgraded and interconnected by a single-hop star WDM subnetwork in a pay-as-you-grow fashion. This evolutionary approach not only allows for fast and efficient multiple-failure recovery but also is well suited to efficiently sustain unpredictable changes and shifts in traffic loads. In this paper, we analytically investigate the maximum achievable capacity of the WDM star subnetwork upgrade of optical single-channel networks under a variety of unicast and multicast traffic scenarios and compare it to that of conventional WDM ring networks. In our analysis, we take priority of ring in-transit traffic, destination stripping, and maximum spatial reuse into account. Our findings show that under multicast traffic the configuration of the star subnetwork plays an important role in order to achieve high multicast capacity. Furthermore, under multicast traffic WDM upgrading and interconnecting a subset of ring nodes might be sufficient to achieve a larger multicast capacity than in WDM rings

In this paper we examine the relationship between the effective capacity (stability limit) of unidirectional and bidirectional packet-switched wavelength division multiplexing (WDM) ring networks for multicast traffic. We consider both bidirectional rings with one packet copy transmission per wavelength channel and two packet copy transmissions. We first prove bounds for the ratio of the multicast capacity of the bidirectional ring to the multicast capacity of the unidirectional ring. Specifically, we show that this ratio is at least two for two copy transmission in the bidirectional ring, and at most two for one copy transmission. We derive closed form expressions of the multicast capacity ratios for networks with a large number of nodes and from these expressions show that the ratios tend to two for a large number of multicast destinations. We demonstrate that for the bidirectional ring with two copy transmission the ratio becomes as large as 2.276. We also find that in the bidirectional ring, the capacity gain with two copy transmission over one copy transmission reaches 30.4%

A resource allocation policy using transmit Beam- forming is proposed for the downlink of multiuser systems. Users to be selected for transmission in a given time slot are examined from groups of users to which the proposed algorithm determines the resources to be allocated in terms of antennas and power. The transmit beam-vectors are computed according to a zero-forcing criterion. Users are selected from different groups so as to achieve a desired balance between maximizing throughput and achieving a high degree of fairness among groups of users as well as between users within a group. Such grouping allows the proposed algorithm to make full usage of the resources and to dynamically allocate these while avoiding the complexity of the NP-complete resource reservation and users' scheduling. Computational complexity is reduced by first estimating the resources to be allocated to the groups of users and then distributing these fairly among the selected users in each group. Such an approach allows the base station to assign its antenna and power resources to groups of users for which transmission would alleviate the interference that would result in neighboring cells, or to direct the resources toward overloaded regions in the cell. Simulation results and comparisons show that the proposed dynamic resource allocation strategy achieves multiuser diversity and a high efficiency in terms controlling the tradeoff between throughput and fairness.

Packet-switched unidirectional and bidirectional ring wavelength division multiplexing (WDM) networks with destination stripping provide an increased capacity due to spatial wavelength reuse. Besides unicast traffic, future destination stripping ring WDM networks also need to support multicast traffic efficiently. In this paper, we provide a probabilistic analysis of the mean hop distances traveled by multicast packet copies on the wavelength channels, and based on the mean hop distances analyze the nominal transmission capacity, reception capacity, and multicast capacity of both unidirectional and bidirectional ring WDM networks with destination stripping. The developed analytical methodology accommodates not only multicast traffic with arbitrary multicast fanout but also unicast and broadcast traffic. In our numerical investigations we examine the impact of number of ring nodes and multicast fanout on the transmission, reception, and multicast capacity of both types of ring networks for different unicast, multicast, and broadcast traffic scenarios and different mixes of unicast and multicast traffic. Our analytical methodology provides a foundation for extended analyses of the multicast capacity of WDM ring networks and enables the evaluation and comparison of future multicast-capable medium access control (MAC) protocols for unidirectional and bidirectional ring WDM networks in terms of transmitter, receiver, and multicast throughput efficiency.

Both wavelength-division-multiplexing (WDM) networks with a ring architecture and WDM networks with a star architecture have been extensively studied as solutions to the ever increasing amount of traffic in the metropolitan area. Studies typically focus on either the ring or the star and significant advances have been made in the protocol design and performance optimization for the WDM ring and the WDM star, respectively. However, very little is known about the relative performance comparisons of ring and star networks. In this paper, we conduct a comprehensive comparison of a state-of-the-art WDM ring network with a state-of-the-art WDM star network. In particular, we compare time-slotted WDM ring networks (both single-fiber and dual-fiber) with tunable-transmitter and fixed-receiver (TT-FR) nodes and an arrayed-waveguide grating-based single-hop star network with tunable-transmitter and tunable-receiver (TT-TR) nodes. We evaluate mean aggregate throughput, relative packet loss, and mean delay by means of simulation for Bernoulli and self-similar traffic models for unicast traffic with uniform and hot-spot traffic matrices, as well as for multicast traffic. Our results quantify the fundamental performance characteristics of ring networks versus star networks and vice versa, as well as their respective performance limiting bottlenecks and, thus, provide guidance for directing future research efforts.

Single-hop WDM networks based on a central passive star coupler (PSC) or arrayed-waveguide grating (AWG) hub have received a great deal of attention as promising solutions for the quickly increasing traffic in metropolitan and local area networks. These single-hop networks suffer from a single point of failure: if the central hub fails, then all network connectivity is lost. To address this single point of failure in an efficient manner, we propose a novel single-hop WDM network, the AWG/spl par/PSC network. The AWG/spl par/PSC network consists of an AWG in parallel with a PSC. The AWG and PSC provide heterogeneous protection for each other; the AWG/spl par/PSC network remains functional when either the AWG or the PSC fails. If both AWG and PSC are functional, the AWG/spl par/PSC network uniquely combines the respective strengths of the two devices. By means of analysis and verifying simulations we find that the throughput of the AWG/spl par/PSC network is significantly larger than the total throughput obtained by combining the throughput of a stand-alone AWG network with the throughput of a stand-alone PSC network. We also find that the AWG/spl par/PSC network gives over a wide operating range a better throughput-delay performance than a network consisting of either two load sharing PSCs in parallel or two load sharing AWGs in parallel.

Research on multicasting in single-hop wavelength-division-multiplexing (WDM) networks has so far focused on networks based on the passive star coupler (PSC), a broadcast device. It has been shown that multicasting performance is improved by partitioning multicast transmissions into multiple multicast copies. However, the channel bottleneck of the PSC, which does not allow for spatial wavelength reuse, restricts the multicast performance. We investigate multicasting in a single-hop WDM network that is based on an arrayed-waveguide grating (AWG), a wavelength routing device that allows for spatial wavelength reuse. In our network, optical multicasting is enabled by wavelength-insensitive splitters that are attached to the AWG output ports. Multicasts are partitioned among the splitters and each multicast copy is routed to a different splitter by sending it on a different wavelength. We demonstrate that the spatial wavelength reuse in our network significantly improves the throughput-delay performance for multicast traffic. By means of analysis and simulations, we also demonstrate that, for a typical mix of unicast and multicast traffic, the throughput-delay performance is dramatically increased by transmitting multicast packets concurrently with control information in the reservation medium access control protocol of our AWG-based network.

Metropolitan WDM networks play an important role in the emerging Internet hierarchy; they interconnect the backbone WDM networks and the local access networks. The current SONET/SDH-over-WDM-ring metropolitan networks are expected to become a serious bottleneck - the so-called metropolitan gap-as they are faced with an increasing amount of bursty data traffic and quickly increasing bandwidths in the backbone networks and access networks. Innovative metropolitan WDM networks that are highly efficient and able to handle variable-size packets are needed to alleviate the metropolitan gap. In this paper we study an AWG-based single-hop WDM metropolitan network. We analyze the photonic switching of variable-size packets with spatial wavelength reuse. We derive computationally efficient and accurate expressions for the network throughput and delay. Our extensive numerical investigations - based on our analytical results and simulations - reveal that spatial wavelength reuse is crucial for efficient photonic packet switching. In typical scenarios, spatial wavelength reuse increases the throughput by 60% while reducing the delay by 40%.