Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglements. The schedule is recomputed periodically using well-known algorithms, such as Earliest Deadline First (EDF). However, a static schedule offers limited flexibility when outcomes are stochastic and arrivals are asynchronous. To overcome this limitation, we propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations. In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline. Furthermore, when the network is overloaded, the dynamic scheduler continues to construct deadline-feasible schedules and degrades gracefully.

Edge–cloud computing infrastructures are increasingly widespread as they combine the flexibility of cloud-native development tools with the performance and security of distributed computing environments. Function-as-a-Service has emerged as a powerful abstraction that overcomes the limitations of a micro-service architecture. However, it generally does not support stateful functions, making it unsuitable for many practical applications in, e.g., Internet of Things (IoT) and real-time analytics. In this paper, we explore a novel paradigm, based on stateful asynchronous agents, that goes beyond traditional serverless computing. We focus on several key technical aspects: programming model, deployment procedures, design of a flexible compute node, and state management. We illustrate our paradigm using the EDGELESS platform as a concrete implementation of this stateful agents’ pattern. We report proof-of-concept experiment results obtained in a testbed with heterogeneous resource-constrained edge nodes that showcase some distinguishing features of our platform: scalable management of lightweight function instances, the advantage of keeping the state local at function instances, and delegated orchestration to enable a third-party agent to make migration decisions in a group of local nodes.

Quantum computing addresses the construction and operation of quantum computers to solve more efficiently instances of specific problems that are difficult to tackle with classical computers. Even if we are currently in the so-called Noisy Intermediate Scale Quantum (NISQ), steady signs of progress are being made towards the realization of a fast and reliable quantum computer, materializing the basic building blocks of quantum circuits, i.e., quantum bits and gates. On the other hand, quantum communications cover the transmission of quantum states across distances. Recent advances in this context have led to the novel research area of quantum networking, which is set to define the programming interfaces and protocols for the practical operation of quantum communication and computing infrastructures. The tutorial has the objective of raising awareness about these emerging topics, i.e., quantum computing and quantum networking, in the research community by i) introducing briefly the latest technologies developed in each, then ii) providing hands-on examples of how to use them for simple use cases, and iii) finally sketching the more promising open research challenges.

EDGELESS is a serverless platform targeting edge computing that supports widely distributed deployments using heterogeneous devices. We present its components, architecture, and programming model. Our working prototype of EDGELESS enables executing lightweight functions and is already available as open-source.

5G and beyond support the deployment of vertical applications, which is particularly appealing in combination with network slicing and edge computing to create a logically isolated environment for executing customer services. Even if serverless computing has gained significant interest as a cloud-native technology its adoption at the edge is lagging, especially because of the need to support stateful tasks, which are commonplace in, e.g., cognitive services, but not fully amenable to being deployed on limited and decentralized computing infrastructures. In this work, we study the emerging paradigm of stateful Function as a Service (FaaS) with lightweight task abstractions in WebAssembly. Specifically, we assess the implications of deploying inter-dependent tasks with an internal state on edge computing resources using a stateless vs. stateful approach and then derive a mathematical model to estimate the energy consumption of a workload with given characteristics, considering the power used for both processing and communication. The model is used in extensive simulations to determine the impact of key factors and assess the energy trade-offs of stateless vs. stateful.

The advent of distributed computing systems will offer great flexibility for application workloads, while also imposing more attention to security, where the future advent and adoption of quantum technology can introduce new security threats. For this reason, the Multi-access Edge Computing (MEC) working group at ETSI has recently started delving into security aspects, especially motivated by the upcoming reality of the MEC federation, which involves services made of application instances belonging to different systems (thus, different trust domains). On the other side, Quantum Key Distribution (QKD) can help strengthen the level of security by enabling the exchange of secure keys through an unconditionally secure protocol, e.g., to secure communication between REST clients and servers in distributed computing systems at the edge. In this paper, we propose a technical solution to achieve this goal, building on standard specifications, namely ETSI MEC and ETSI QKD, and discussing the gaps and limitations of current technology, which hamper full-fledged in-field deployment and mass adoption. Furthermore, we provide our look-ahead view on the future of secure distributed computing through the enticing option of federating edge computing domains.

Apache Kafka is a widely-used event streaming platform for reliable high-volume real-time data exchange following a producer–consumer pattern. Despite its popularity, Apache Kafka requires expertise and attention to detail, and there are no default guidelines that can be applied to all use cases without careful consideration. In this paper, we propose a novel approach to optimise the number of partitions and brokers in Apache Kafka, which are two key configuration parameters, under the given characteristics and constraints of the target applications. In particular, we consider the distribution of data-intensive real-time flows exchanged between a set of producers and consumers, which is representative of fog computing environments for ML/AI analytics. We introduce a methodology for modelling the topic partitioning process in Apache Kafka and formulate an optimisation problem to determine the optimal number of partitions to satisfy the application requirements and constraints. We propose two efficient heuristics to solve the optimisation problem, considering the trade-off between resource utilisation and application performance. We evaluate the performance of our approach through numerical simulations, and we demonstrate its practicality by implementing a prototype on an Apache Kafka cluster and conducting experiments in three different scenarios focused on mass consumption vs. production and real-time data streaming. To carry out repeatable experiments in controlled conditions, we developed a reusable framework that fully automatises cluster setup and performance assessment, and we make it available to the community as open-source software.

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions.

In this demonstration, we showcase the realization of a prototype of an edge computing network, where the client and edge domains both host simulated Quantum Key Distribution devices, for a hospital use case. In particular, digital health applications using the Function-as-a-Service (FaaS) paradigm will invoke remote functions provided by an Apache OpenWhisk cluster deployed in the edge infrastructure, where the arguments and return value are encrypted using keys generated through an underlying simulated QKD point-to-point network. All the interactions in the control/management plane are handled through standard interfaces defined by the ETSI MEC and QKD industry study groups.

Quantum Key Distribution (QKD) enables secure communications via the exchange of cryptographic keys exploiting the properties of quantum mechanics. Nowadays the related technology is mature enough for production systems, thus field deployments of QKD networks are expected to appear in the near future, starting from local/metropolitan settings, where edge computing is already a thriving reality. In this paper, we investigate the interplay of resource allocation in the QKD network vs. edge nodes, which creates unique research challenges. After modeling mathematically the problem, we propose practical online policies for admitting edge application requests, which also select the edge node for processing and the path in the QKD network. Our simulation results provide initial insights into this emerging topic and lead the way to upcoming studies on the subject.

In the future, quantum computers will become widespread and a network of quantum repeaters will provide them with end-to-end entanglement of remote quantum bits. As a result, a pervasive quantum computation infrastructure will emerge, which will unlock several novel applications, including distributed quantum computing, that is the pooling of resources on multiple computation nodes to address problem instances that are unattainable by any individual quantum computer. In this paper, we first investigate the issue of service differentiation in this new environment. Then, we define the problem of how to select which computation nodes should participate in each pool, so as to achieve a fair share of the quantum network resources available. The analysis is performed via an open source simulator and the results are fully and readily available.

In the world of cloud technologies, serverless computing has now settled as a stable and promising resident. This gives a cloud provider the flexibility to provide its users with both Platform-as-a-Service (PaaS), i.e., the back-end application runs in a dedicated container, or Function-as-a-Service (FaaS), i.e., the back-end logic is offered as elementary functions that are invoked by the client applications. In parallel, edge computing has attracted a significant interest, due its enticing promises of reducing the outbound traffic of telco operators, while at the same time cutting down the user latency. As a result, in the near future, PaaS and FaaS containers are going to cohabit in a versatile computation infrastructure spanning from the far edge up to the cloud. In this paper we propose a mathematical formulation of a resource allocation problem that optimizes the assignment of both types of containers and can be solved efficiently by an edge orchestrator. We evaluate the proposed solution via extensive simulation experiments, which show that our approach, which takes into account the characteristics of PaaS vs. FaaS, provides significant performance benefits compared to less sophisticated strategies, despite its relatively low run-time complexity.

The evolution of quantum computing technologies has been advancing at a steady pace in the recent years, and the current trend suggests that it will become available at scale for commercial purposes in the near future. The acceleration can be boosted by pooling compute infrastructures to either parallelize algorithm execution or solve bigger instances that are not feasible on a single quantum computer, which requires an underlying Quantum Internet: the interconnection of quantum computers by quantum links and repeaters to exchange entangled quantum bits. However, Quantum Internet research so far has been focused on provisioning point-to-point flows only, which is suitable for (e.g.) quantum sensing and metrology, but not for distributed quantum computing. In this paper, after a primer on quantum computing and networking, we investigate the requirements and objectives of smart computing on distributed nodes from the perspective of quantum network provisioning. We then design a resource allocation strategy that is evaluated through a comprehensive simulation campaign, whose results highlight the key features and performance issues, and lead the way to further investigation in this direction.

Serverless computing has emerged as a very popular cloud technology, together with its companion Function-as-a-Service (FaaS) programming model enabling invocations of stateless functions from clients. An evolution of serverless is now taking place, shifting it towards the edge of the network and broadening its scope to stateful functions, as well. In this paper we argue that stateless vs. stateful is not a dichotomy of the application per se, but rather a time-varying property of most (if not all) applications, as confirmed by the analysis of real traces collected in a production environment. Based on this observation, we propose a mathematical formulation of a resource allocation problem that jointly encompasses both operation modes, dubbed lambda vs. mu, which can be solved efficiently at run-time by an edge orchestrator. We evaluate the proposed solution via simulation experiments in realistic network and workload conditions, which leads the way to the practical realization of a system where applications can freely adapt their current operation mode and optimize their performance at a minimum cost of operation from the network's perspective.

In this paper, we address the problem of supporting chains of stateful function invocations following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios, such as wide-area Internet of Things (IoT) networks, and we propose three different solutions: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We show via simulation that StateLocal, by applying the data locality principle, can significantly enhance the performance by reducing the application delay due to data transfer and keeping a lower traffic volume in the network. This study sheds light on some aspects within the unexplored area of stateful FaaS, which is very promising among the edge computing technologies and has several open research directions associated.

Deployment of computing infrastructures at the edge of the network will drive a revolution in integrated solutions for smart mobility in the cities of the future, thanks to the promises of reduced latency and outbound traffic. The adoption of serverless computing will help realising this vision since it simplifies management while at the same time providing the application developers with a neat and clean Function-as-a-Service (FaaS) programming model. Today FaaS relies on HTTP over TCP, but QUIC is emerging fast as a replacement because it is more robust to packet losses and it allows connection roaming: both these advantages are especially important for mobile scenarios. In this paper we report the results of a preliminary evaluation of QUIC+HTTP/3 when used instead of TCP+HTTP within a framework for decentralized dispatching of FaaS function invocations, which shows that this direction is promising and deserves to be delved further in the future.

Serverless computing is becoming widely adopted among cloud providers, thus making increasingly popular the Function-as-a-Service (FaaS) programming model, where the developers realize services by packaging sequences of stateless function calls. The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edge computing systems, which are expected to be typical for Internet of Things (IoT) applications. In this article, we fill this gap by proposing a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize the response times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available. Our solution is shown to be simple enough to be installed on devices with limited computational capabilities, such as IoT gateways, especially when using a hierarchical forwarding extension. We evaluate the proposed platform by means of extensive emulation experiments with a prototype implementation in realistic conditions. The results show that it is able to smoothly adapt to the mobility of clients and to the variations of their service request patterns, while coping promptly with network congestion.

Edge computing is an emerging architecture in 5G networks where computing power is provided at the edge of the fixed network, to be as close as possible to the end users. Computation offloading, better communication latency, and reduction of traffic in the core network are just some of the possible benefits. However, the Quality of Experience (QoE) depends significantly on the network performance of the user device towards the edge server vs. cloud server, which is not known a priori and may generally change very fast, especially in heterogeneous, dense, and mobile deployments. Building on the emergence of standard interfaces for the installation and operation of thirdparty edge applications in a mobile network, such as the MultiAccess Edge Computing (MEC) under standardization at the European Telecommunications Standards Institute (ETSI), we propose MECPerf, a tool for user-driven network performance measurements. Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization.

Future pervasive applications, like mobile augmented reality, have huge bandwidth and computation demands and very stringent delay constraints. Edge computing has been proposed to cope with such challenging requirements, since it shortens significantly the distance between the end users and the servers. On the other hand, serverless computing is emerging among cloud technologies to respond to the need of highly scalable event-driven execution of stateless tasks. In this paper, we investigate the convergence of the two to enable very low-latency execution of short-lived stateless tasks whose computation is offloaded from the user terminal to servers hosted by or close to edge devices in mobile pervasive environments. We realized a proof-of-concept implementation to delve into the specific issue of efficient dispatching of tasks in a distributed manner to achieve high scalability. We evaluated our proposed algorithm with experiments in a large-scale emulated network environment, showing that our solution achieves similar or better delay performance than a centralized solution, with far less network utilization.

We consider a Software Defined Networking (SDN)-enabled edge computing domain, where networking devices also have processing capabilities. In particular, we investigate the problem of dynamic allocation of stateless computations, that we call lambda functions, and propose an architectural framework through which requests for execution of lambda functions originated by mobile nodes can be appropriately routed to specific edge devices following a serverless model. In addition, we propose a detailed emulation environment to test the architecture. Our framework supports many possible distributed algorithms to dynamically adapt the choice where requests should be executed, in order to optimize a given performance target. In the paper we consider a few such policies, to test the flexibility of the architecture. We thus present extensive performance results of the considered policies.

It is our great pleasure to welcome you to the 5th edition of the successful workshop on the Internet of Things: Smart Objects and Services (IoT-SoS), which is organized this year in conjunction with WoWMoM 2016. The main focus of this wokshop is to bring together experts from the research community, the industry and standardisation bodies and discuss in the context of heterogeneous networking technologies for the Internet of Things (IoT), aiming to identify solutions for addressing the key challenges that IoT brings to the networking domain.

In IEEE 802.16, the wireless resources are logically partitioned into 5-ms frames, which extend in two dimensions: time and frequency. To break down the complexity of resource allocation at the base station, a split approach has been proposed in the literature, where the tasks of scheduling packets and allocating them into frames are solved in separate and subsequent stages. In this paper, we focus on the allocation task alone, which is addressed in its full complexity, i.e., by considering that data within the frame must be allocated as bursts with rectangular shape, each consisting of a set of indivisible sub-bursts, and that a variable portion of the frame is reserved for in-band signaling. After proving that the resulting allocation problem is NP-hard, we develop an efficient heuristic algorithm, called Recursive Tiles and Stripes (ℜTS), to solve it. ℜTS, in addition to handling a more general problem, is shown to perform better than state-of-the-art solutions via numerical analysis with realistic system parametrization. Furthermore, an extensive evaluation of the interaction between the scheduler and the allocator is carried out in a wide variety of network scenarios .

Due to the growing market and research interests on communication protocols for applications that require little or no human intervention, called Machine-to-Machine (M2M) applications, the European Telecommunications Standardization Institute (ETSI) founded in 2009 a Technical Committee on the topic, which has released its first complete set of technical recommendations in October 2011 as ETSI M2M Release 1. The standard is agnostic with respect to both the application domains in which it can be used, ranging from, e.g., smart grid to personal health systems, and the underlying data transport and access technologies. We demonstrate the feasibility of an ETSI-compliant complete end-to-end system, which uses the Constrained Application Protocol (CoAP) protocol under definition at the Internet Engineering Task Force (IETF), showing its potential to meet the expectations of M2M applications of practical interest.

In the Internet of Things (IoT) the edge devices often rely on gateways to connect to the Internet, to reduce costs and energy consumption. For those scenarios where the gateway itself does not have a wired Internet connection, LTE is a candidate solution due to its high spectral efficiency, bandwidth, and coverage. In this paper, the suitability of LTE for the interconnection of aggregated edge devices is investigated. The study is carried out under the assumption that the Constrained Application Protocol (CoAP), which is becoming increasingly popular, is used. The performance analysis is carried out through packet-level simulation with the open source simulator ns3.

It is a great pleasure to welcome you to the first edition of the IEEE Workshop on the Internet of Things: Smart Objects and Services.

The IEEE 802.16 standard uses Orthogonal Frequency Division Multiple Access (OFDMA) for mobility support. Therefore, the medium access control frame extends in two dimensions, i.e., time and frequency. At the beginning of each frame, i.e., every 5 ms, the base station is responsible both for scheduling packets, based on the negotiated quality of service requirements, and for allocating them into the frame, according to the restrictions imposed by 802.16 OFDMA. To break down the complexity, a split approach has been proposed in the literature, where the two tasks are solved in separate and subsequent stages. In this paper we focus on the allocation task alone, which is addressed in its full complexity, i.e., by considering that data within the frame must be allocated as bursts with rectangular shape, each consisting of a set of indivisible sub-bursts, and that a variable portion of the frame is reserved for in-band signaling. After proving that the resulting allocation problem is NP-hard, we develop an efficient heuristic algorithm, called Recursive Tiles and Stripes (RTS), to solve it. RTS, in addition to handle a more general problem, is shown to perform better than state-of-the-art solutions via numerical analysis with realistic system parametrization.

Power saving is an important feature of mobile Broadband Wire-less Access (BWA) systems, since it allows user terminals to switch off the radio transceiver when there is no network activity, thus increasing the battery lifetime. In this paper, it is shown that even greater energy savings can be obtained if user terminals are put to sleep while there is network activity, by trading off performance for battery duration in a controllable way. To this aim, a framework is proposed, in which existing wireless scheduling algorithms can be fit to pursue their original goals, e.g., maximizing throughput or fairness, while improving the energy efficiency of user terminals. The framework is specifically tailored to the IEEE 802.16/WiMAX technology. Its effectiveness is assessed in this context through an extensive packet-level simulation campaign with realistic FTP data traffic.

Mobile Broadband Wireless Access (BWA) networks will offer in the forthcoming years multiple and differentiated services to users with high mobility requirements, connecting via portable or wearable devices which rely on the use of batteries by necessity. Since a relatively large fraction of energy is consumed by such devices for transmitting/receiving data over-the-air, mechanisms are needed to reduce power consumption, in order to increase the lifetime of devices, and hence, improve user's satisfaction. The IEEE 802.16, which supports mobile BWA since its "e" amendment in 2005, defined power saving functions at the Medium Access Control (MAC) layer, which are designed to be operated during open traffic sessions for the greatest energy consumption reduction. However, enabling power saving usually increases the transmission latency, which can negatively affect the Quality of Service (QoS) experienced by users. On the other hand, imposing stringent QoS requirements may limit the amount of energy that can be saved. In this paper, an extensive study of the mutual interaction between power saving mechanisms and QoS support is carried out in the context of the IEEE 802.16e. In particular, two types of delay-constrained applications with different requirements are considered, i.e., Web and Voice over IP (VoIP) for which the IEEE 802.16e standard specifies two different power saving classes. The performance is assessed via detailed packet-level simulation, with respect to several system parameters. To capture the relative contribution of all the factors on the energy- and QoS-related metrics, part of the evaluation is carried out by means of 2k· ¿! analysis.

The performance of multi-radio multi-channel wireless mesh networks (WMNs) based on the IEEE 802.11 technology depends significantly on how the channels are assigned to the radios and how traffic is routed between the access points and the gateways. In this paper we propose an algorithmic approach to this problem, for which no conclusive solution has been put forward in the literature so far. The core of our scheme, called PaMeLA, consists of splitting the overall joint channel assignment and routing (JCAR) problem into a number of local optimization sub-problems, one for every node of the WMN, that are solved sequentially. Any sub-problem is formulated as an integer linear optimization problem (ILP), whose optimal solution can be found using branch-and-cut in a reasonable amount of time. The final solution is obtained after a post-processing phase. In its current form, the algorithm is tailored to suit WMNs with a single gateway in a square-grid topology, which is of practical interest in many application scenarios. PaMeLA is compared through detailed packet-level simulation with several state-of-the-art JCAR algorithms and it is shown to attain better performance, in terms of the packet loss rate.

We propose five slot scheduling algorithms for wireless mesh networks (WMNs) using a time division multiple access (TDMA)-based medium access control (MAC) protocol. A preliminary comparative study is carried out through simulation.

Deployment of Wireless Mesh Networks (WMNs) is becoming increasingly popular due to the low-impact and low- cost features of wireless devices. This is especially true for WMNs based on IEEE 802.11 which, however, does not include native support for multi-hop relaying. This gap is being filled by the Task Group 's' of the IEEE 802.11 which has recently published an amendment in order to add mesh functions to the popular standard IEEE 802.11. Among the enhancements proposed, Mesh Deterministic Access (MDA) allows mesh routers to negotiate periodic collision-free transmission opportunities, called MDAOPs, to the Medium Access Control (MAC) layer on a hop- by-hop manner. Control messages are exchanged to advertise the reserved MDAOPs in the two-hop neighborhood. MDA lays the foundations for enabling QoS provisioning functions in IEEE 802.11s WMNs, such as end-to-end bandwidth reservation, call admission control, and traffic engineering. In this paper we study the problem of scheduling MDAOPs, which is left unspecified by the standard, and propose two baseline algorithms. Furthermore, we provide evidence that performance under MDA can be significantly degraded by the unknown interference of traffic flows outside the two-hop neighborhood. A dynamic relocation procedure is proposed in order to combat this phenomenon, thus providing traffic flows established in the WMN with stable performance. This procedure does not need any modifications to the standard MDA procedure. To the best of our knowledge, this is the first study that tackles this problem in the context of IEEE 802.11s. The effectiveness of the proposed algorithms is evaluated by means of a packet-level simulation.

In wireless mesh networks, the end-to-end throughput of traffic flows depends on the path length, i.e. the higher the number of hops, the lower becomes the throughput. In this paper, a Fair End-to-end Bandwidth Allocation (FEBA) algorithm is introduced to solve this problem. FEBA is implemented at the Medium Access Control (MAC) layer of single-radio, multiple channels IEEE 802.16 mesh nodes, operated in a distributed coordinated scheduling mode. FEBA negotiates bandwidth among neighbors to assign a fair share to each end-to-end traffic flow. This is carried out in two steps. First, bandwidth is requested and granted in a round-robin fashion where heavily loaded links are provided with a proportionally higher amount of service than the lightly loaded links at each round. Second, at each output link, packets from different traffic flows are buffered in separate queues which are served by the Deficit Round Robin (DRR) scheduling algorithm. If multiple channels are available, all of them are shared evenly in order to increase the network capacity due to frequency reuse. The performance of FEBA is evaluated by extensive simulations and is shown to provide fairness by balancing the bandwidth among traffic flows.

IEEE 802.16 mesh does not include support for traffic flows with strict quality of service requirements. In this paper, we propose an end-to-end bandwidth reservation protocol (EBRP) in the backhaul of a wireless mesh network using IEEE 802.16 mesh. The distinctive feature of EBRP is that it is carried out at the medium access control (MAC) layer. Therefore, EBRP not only makes the resource reservation process extremely rapid, but it also allows the resources available to be allocated efficiently by exploiting technology-specific information available at the MAC. We present EBRP as part of a framework which also includes support for distributed call admission control (CAC). Preliminary simulation results obtained with VoIP traffic and nodes arranged in a grid topology are presented to show the effectiveness of EBRP under controlled channel conditions.

IEEE 802.16 is a recent standard for Broadband Wireless Access networks, which includes a mesh mode operation for distributed channel access of peering nodes. In accordance with the IEEE 802.16 MAC protocol, time is partitioned into frames of fixed duration, each one divided into two sub-frames, for control and data transmission, respectively. Slots in the control sub-frame are used by nodes to negotiate the schedule of transmissions in data sub-frames, and are accessed by means of a collision-free distributed procedure, namely the mesh election procedure. In this paper, we analyze the performance of the mesh election procedure by means of extensive simulations, and identify the system configuration parameters that have the most impact on the performance of control message transmission. The analysis is carried out under the assumption that the wireless link is error-free.

IEEE 802.16 is a recent IEEE standard for broadband wireless access networks. In IEEE 802.16 networks, the medium access control (MAC) protocol is centralized and explicitly supports quality of service (QoS). That is to say, access to the medium by a number of subscriber stations (SSs) is centrally controlled by one base station (BS), which is responsible for allocating bandwidth to several MAC connections in order to provide them with the negotiated QoS guarantees. However, although the network can be operated in frequency division duplex (FDD) mode (that is, transmissions from the BS (downlink) and SSs (uplink) occur on separate frequency channels), the standard supports SSs with half-duplex capabilities. This means that they are equipped with a single radio transceiver which can be used either to transmit in the uplink direction or to receive in the downlink direction. This may severely hamper the capacity to support QoS. Therefore, in order to allocate bandwidth, an IEEE 802.16 BS has to solve two related issues: (1) how it can schedule bandwidth grants to SSs in order to meet the QoS requirements of their connections and (2) how it can coordinate the uplink and downlink scheduled grants so as to support half-duplex capabilities. In this paper, we derive sufficient conditions for a set of scheduled grants to be allocated so that the transmission of each half-duplex SS does not overlap with its reception. Based on this, we propose a grant allocation algorithm, namely, the half-duplex allocation (HDA) algorithm, which always produces a feasible grant allocation provided that the sufficient conditions are met. HDA has a computation complexity of 0(n), where n is the number of grants to be allocated. Finally, we show that the definition of HDA allows us to address the two issues mentioned above by following a pipeline approach. This is when scheduling and allocation are implemented by separate and independently running algorithms, which are just loosely coupled with each other. We show via extensive simulations that the performance of SSs with half-duplex capabilities, in terms of the delay of real-time and non-real-time interactive traffic, using HDA almost perfectly matches that of full-duplex SSs, whereas an alternative approach, based on the static partitioning of half-duplex SSs into separate groups, which are allocated alternately, is shown to degrade the performance.

The IEEE 802.16 is a standard for broadband wireless communication in metropolitan area networks (MAN). To meet the QoS requirements of multimedia applications, the IEEE 802.16 standard provides four different scheduling services: unsolicited grant service (UGS), real-time polling service (rtPS), non-real-time polling service (nrtPS), and Best Effort (BE). The paper is aimed at verifying, via simulation, the effectiveness of rtPS, nrtPS, and BE (but UGS) in managing traffic generated by data and multimedia sources. Performance is assessed for an IEEE 802.16 wireless system working in point-to-multipoint (PMP) mode, with frequency division duplex (FDD), and with full-duplex subscriber stations (SSs). Our results show that the performance of the system, in terms of throughput and delay, depends on several factors. These include the frame duration, the mechanisms for requesting uplink bandwidth, and the offered load partitioning, i.e., the way traffic is distributed among SSs, connections within each SS, and traffic sources within each connection. The results also highlight that the rtPS scheduling service is a very robust scheduling service for meeting the delay requirements of multimedia applications

In this paper we propose a scheduling algorithm for supporting quality of service (QoS) in an IEEE 802.11e network using the HCF controlled channel access (HCCA) function. The algorithm consists of an offline procedure that generates a service schedule over a base period, and an online procedure that applies the latter to actually schedule transmission opportunities to HCCA flows