The rise of technologies like AI and IoT has increased the need for high-performance, low-latency computing. Edge computing addresses this by extending cloud resources closer to client applications. Effective workload distribution across this edge-cloud continuum must however consider both network proximity and available computing resources to avoid potential overloads. To achieve this, we developed a computing-aware framework for dynamic traffic steering using Kubernetes API, MP-BGP, and SRv6. Specifically, we use Kubernetes APIs to determine computing capacity and BGP to advertise it, while SRv6 steers traffic based on proximity, resource availability, and QoS requirements. We implemented a proof-of-concept of the proposed framework and validated it using GNS3 and Unix-based routers running FRRouting, demonstrating its effectiveness and performance.

Current edge computing (EC) solutions face the significant challenge of limited computational capacities. Effectively allocating resources and controlling the system to ensure task timeliness remains an open problem. The age of information (AoI) is a metric to measure the freshness of information that circulates in a system. While the AoI is primarily influenced by packet generation rate, transmission latency, and queuing delays, the processing time becomes notably significant when dealing with Internet-of-Things (IoT) computationally intensive tasks. Such IoT applications necessitate processing before embedded information can emerge and status can be acquired. This paper proposes a combined system control and resource assignment policy, in new-generation EC environments, where edge nodes have limited capacity and task flows are computationally intensive. The objective is to assign task flows to dedicated resource capacity, minimizing the worst AoI experienced by flows. For this purpose, three problem formulations for the flow-resource assignment are considered: i) the assignment with fixed arrival and service processes; ii) the service process control problem; iii) the arrival process control problem. For each problem formulated, a matching game with externalities is designed, and preference lists are built considering the mean AoI of an M/G/1 system, here exploited as reference model to represent each computation partition. The stability of matching games proposed is investigated, and experimental results are presented to highlight the validity of the matching approaches, providing critical discussion about the performance impact of the three problems addressed, also compared with a reservoir learning approach. The proposed matching algorithm surpasses the state-of-the-art Deferred Acceptance method by achieving a lower maximum AoI, thereby meeting the optimization objective. It also demonstrates improved performance over the data-driven approach. While comparable maximum AoI values can be attained with sufficiently large training datasets, the proposed algorithm consistently yields superior results.

The primary task of a quantum repeater network is to deliver entanglement among end nodes. Most of existing entanglement distribution protocols do not consider purification, which is thus delegated to an upper layer. This is a major drawback since, once an end-to-end entangled connection (or a portion thereof) is established it cannot be purified if its fidelity (F) does not fall within an interval bounded by Fmin (greater than 0.5) and Fmax (less than 1). In this paper, we propose the Ranked Entanglement Distribution Protocol (REDiP), a connection-oriented protocol that overcomes the above drawback. This result was achieved by including in our protocol two mechanisms for carrying out jointly purification and entanglement swapping. We use simulations to investigate the impact of these mechanisms on the performance of a repeater network, in terms of throughput and fidelity. Moreover, we show how REDiP can easily be configured to implement custom entanglement swapping and purification strategies, including (but not restricted to) those adopted in two recent works.

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.

Interoperability is one of the main issues concerning the Internet of Things (IoT). The Arrowhead Framework (AHF) is an open-source platform that aims at tackling this problem. In this work, we first integrate a set of legacy IoT devices (i.e., an ECG and a smart plug) into the AHF by leveraging W3C Web of Things (WoT) as an interface between the two worlds. The result is an architecture where AHF consumers are deployed in a local cloud and consume Web Things, which can be deployed on IoT devices as well as on servers at the network edge to provide edge computing services. In this context, IoT device mobility raises a problem. When the IoT device moves, indeed, the edge-hosted service needs to follow it to preserve proximity, which is key to edge computing. In this case, the IP addresses of both the Web Things (i.e., the one hosted on the IoT device and the one hosted at the edge) may change, causing problems of mutual reachability after mobility/migration. WoT does not currently handle this issue. In this work, we extend WoT in this direction and evaluate our approach over a small-scale edge computing testbed.

ETSI MEC is a standard for edge computing which allows the execution of services - called MEC applications - on hosts in user proximity. One of the emerging concepts within ETSI MEC is that of MEC application relocation, i.e., the migration of a MEC application between edge hosts. ETSI MEC identifies several approaches to relocate a MEC application along with its internal state. However, some of these approaches devote the transfer of the application state to the application itself, whereas others rely on costly virtual-machine migration procedures. To overcome the above limitations, in this work we extend ETSI MEC to support MEC application relocation by exploiting container-migration technologies. We evaluate the performance of our implementation over a small-scale edge testbed, showing the overall benefits of the proposed approach.

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.

Edge computing allows to run microservices in close proximity to end user devices. This proximity lets edge computing support emerging 5G application scenarios that need low latency and high bandwidth (e.g., augmented reality, autonomous vehicles). Given its interest, edge computing is fastly gaining momentum and is currently being standardised by the European Telecommunications Standards Institute (ETSI) as Multi-Access Edge Computing (MEC). Notwithstanding its strengths, edge computing is significantly challenged by device mobility, as this can reduce proximity to the edge microservice, putting edge computing benefits at risk. A way to solve this problem is to migrate the edge microservice across edge servers, to let it follow the application component running on the mobile device. Besides, if the microservice is stateful (i.e., it maintains a state associated to the user), its state needs to be migrated as well. Within ETSI MEC, this concept is expressed as stateful application relocation. The standard identifies three different high-level ways to transfer the application state. However, all of them assume that it is up to the application to actually relocate the state. In this work, we assume that applications at the edge run as containers, and we extend ETSI MEC to let it support stateful application relocation by leveraging container migration techniques. This approach allows to transfer the application state in a transparent way to the application itself. We implemented our solution and tested it over a small-scale edge computing testbed to extract initial results.

Multi-access edge computing (MEC) is emerging as an effective solution to fulfil the requirements of time-critical Internet of Things (IoT) applications. However, to improve the network efficiency and QoS support of MEC systems, it is essential to jointly optimise edge resource management, IoT data collection and IoT device association in the context of heterogeneous services and edge resources. In this study, we address these challenges by formulating the resource allocation, device association and data routing problem in a multi-cell MEC network as a mixed-integer non-linear programming problem. We also propose a best-fit greedy heuristic method to determine an approximate solution to the optimisation problem for online resource management. Simulation results confirm the effectiveness of the proposed algorithm compared to three alternative benchmarks.

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.

In modern cloud and edge computing environments, services are typically provided as Virtual Machines (VMs). More recently, containers have been gaining momentum as a lightweight form of VMs. Container migration is used for several reasons, one of which being to maintain proximity between edge computing services and mobile users. When migrating containers, however, it is important to consider that they typically have ongoing communications with other endpoints, e.g., users' applications. Moreover, in case of connection-oriented protocols, communicating endpoints share a state (i.e., the connection), which needs to be migrated as well. Connection-oriented protocols like TCP were not designed having connection migration in mind, thus their connections cannot survive a change of IP address or port number. On the other hand, the QUIC protocol provides a mechanism for client-side connection migration, i.e., when a client device changes IP address (e.g., after a wireless handover), QUIC transparently migrates ongoing connections to the new address. Nonetheless, server-side connection migration in QUIC is not yet implemented nor investigated. In this paper, we extend QUIC to support server-side connection migration when a container is migrated between hosts. More specifically, we design two different strategies to achieve this purpose. Besides, we describe a proof-of-concept implementation based on aioquic, a Python open-source implementation of QUIC. We also verify that our implementation does not break QUIC specification nor undermines aioquic interoperability. Finally, we evaluate our solution by testing both the considered strategies using different container migration techniques and against a no-migration scenario.

The Internet of Things (IoT) extends the Internet connectivity into devices and everyday objects. This huge volume of connected devices needs to be managed considering the severe energy, memory, processing, and communication limitations of IoT devices and networks. OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices and related service enablement. In this work we propose the introduction of a LWM2M Proxy in between IoT devices and the management server, in order to control the flow of LWM2M requests sent to IoT devices over a low-power and lossy network, and therefore avoid that device and network resources get overloaded. We evaluate the proposed solution by simulation and show that it strongly improves the performance of LWM2M in terms of service delay as compared to the standard case with no Proxy.

The integration between fog computing and the Internet of Things (IoT) creates plenty of new opportunities. Fog computing nodes run complex tasks on behalf of IoT devices, and the topological proximity of fog computing to the IoT enables several advantages (e.g., low latency). However, some IoT devices are mobile, and mobility may compromise the fog advantages. When a device moves, the communication path to the corresponding fog service may increase, with an impact on the fog advantages (which are a consequence of fog proximity) and overall performance. To overcome this issue, the fog service may be migrated across the fog computing infrastructure and maintained close enough to the served IoT device(s). It is worth noting, though, that service migration comes at a cost and may affect application Quality of Service (QoS). In this paper, we consider a fog service to be implemented as multiple containers, having one of them encapsulating an MQTT broker. Our contribution is the evaluation of the impact of container migration, which is considered in various flavours, on application QoS as perceived by mobile things. To this purpose, we consider an augmented reality application based on the MQTT protocol and conduct a set of experiments over a real fog computing testbed. Results show how migrating the fog service gives some benefits on the experienced QoS with respect to a case where no migration is performed.

Integration between fog computing and the Internet of Things (IoT) paves the way to a plethora of promising opportunities. Device mobility might however impair fog computing benefits (e.g., low latency), which are indeed an outcome of fog proximity to end users/devices. A solution to this problem is to migrate the fog service across the fog infrastructure, thus to keep the distance to the served mobile device as low as possible. In this paper, we consider a fog service to be implemented as the combination of two containers, and we detail the demo through which we plan to show the impact of fog service migration on application performance. To this purpose, we plan to deploy an Augmented Reality (AR) application that detects vehicles in video frames and augments the latter with bounding boxes built around the detected vehicles. We offer to the audience the possibility to: (i) interact with the employed testbed by triggering device mobility; (ii) visualise the difference between migrating and not migrating the fog service in response to device mobility.

Wireless Sensor Networks (WSNs) are one of the main building blocks of the future Internet of Things (IoT). Thanks to the introduction of the 6LoWPAN standard, WSNs can also be integrated into IP networks in a seamless manner. In a previous work, we defined SD-6LoWPAN, an architecture specifically designed to enable software defined networking in 6LoWPANs. In this work, we focus on the implementation of the proposed SDN solution on top of real constrained devices powered by the Contiki operating system. We illustrate the main challenges discovered, and how they have been addressed by extending the Contiki network stack in order to provide a working prototype of SD-6LoWPAN. The latter is then validated by running a set of experiments on a real testbed.

Wireless Sensor Networks (WSNs)play a major role in the expansion of the Internet of Things (IoT)market as they allow the deployment of low cost and low power networks possibly in large areas. The introduction of the 6LoWPAN standard allowed WSN to move into the IPv6 era, with a seamless integration of sensors into external networks. The recent application of IoT in industrial systems is now introducing new traffic patterns - machine to machine (M2M)above the others - and communication challenges that can modify the classical multipoint-to-point pattern of WSN, having the sink as the root of every communication. To solve these problems, we propose an architecture to enable an efficient and controlled multi-hop forwarding over 6LoWPANs. We base our approach on the standard 6LoWPAN protocol stack and we exploit the Software Defined Networking paradigm to achieve network reconfigurability. We evaluate the performance of our architecture in a simulated environment, with both multipoint-to-point and M2M traffic, and we demonstrate its feasibility in a real testbed.

Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name thisCompanion Fog Computing(CFC), since the Fog service behaves as a "companion" of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations.

As for the Cloud, essential features of Fog Computing are both virtualization and the capability to migrate virtual environments among nodes. In this paper, we thoroughly report both the state-of-the-art virtualization and migration techniques and their available implementations. In particular, we investigate the aptness of such technologies for a specific layer of the Fog hierarchy, namely the network edge. Indeed, this layer presents some characteristics that distinguish it from the Cloud so that a virtualization or migration technique that represents a good compromise for the Cloud might not be likewise suitable for the edge.

Future Smart Cities will be enabled by a pervasive fabric of sensors and actuators, seamlessly integrated within information systems. This large-scale Internet of Things (IoT) will bring a new level of challenges to existing communication networks and wireless standards. In order to analyze these issues and study possible solutions, a quick and rapid experimentation is of paramount importance to ensure that IoT protocols and network architectures can handle such challenges. In this paper, we present the ECOAP project that aims at evaluating the performance of the Constrained Application Protocol (CoAP), the application protocol defined to interconnect IoT devices with information systems. The project will carry out an evaluation of CoAP through the WiSHFUL platform, a platform for large-scale experimentation of network systems. The project will focus on assessing different congestion control strategies in order to evaluate the scalability of the protocol in future large-scale scenarios.

CoAP is an application protocol that provides standardised RESTful services for IoT devices. Since COAP messages are encapsulated into UDP datagrams, COAP specification provides: i) optional reliability mechanisms through retransmissions, and ii) simple congestion control mechanisms based on retransmission timeouts. Recent studies have demonstrated that these congestion control schemes may significantly underperform when operating with bursty traffic. To address these limitations, in this paper we propose COAP-R, an alternative solution for regulating the sending rate of CoAP sources, which adopts a rate-based approach for traffic control. Key features of COPA-R are: i) to leverage the tree-based routing structure of IoT networks to estimate the maximum throughput that can be obtained on the bottleneck link of every upward route, and ii) to perform in a distributed manner a max-min fair allocation of available network capacity on the basis of estimated bottleneck bandwidths. The proposed approach is evaluated by means of simulations considering a scenario in which traffic is generated in bursts, for instance as consequence of events detected by sensors. Our simulations demonstrate that the proposed approach ensures a fair allocation of network resources, and leads to a 40% decrease of the data collection delays when compared to COAP.

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things (IoT) will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this paper, we present an edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the Constrained Application Protocol (CoAP) to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a peer-to-peer overlay implemented by a distributed hash table, which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

The Internet of Things (IoT) conceives a world where everyday objects are able to join the Internet and exchange data as well as process, store, collect them from the surrounding environment, and actively intervene on it. An unprecedented number of services may be envisioned by exploiting the Internet of Things. Fog Computing, which is also known as Edge Computing, was proposed in 2012 as the ideal paradigm to support the resource-constrained IoT devices in data processing and information delivery. Indeed, the Fog, which does not replace the centralized Cloud but cooperates with it, distributes Cloud Computing technologies and principles anywhere along the Cloud-to-Things continuum and particularly at the network edge. The Fog proximity to the IoT devices allows for several advantages that must be continuously guaranteed, also when end devices move from one place to another. In this paper, we aim at examining in depth what it means to provide mobility support in a Fog environment and at investigating what are the main challenges to be faced. Besides, in order to highlight the importance of this topic in everyday life, we provide the reader with three scenarios where there is an integration between the IoT and Fog Computing, and in which mobility support is essential. We finally point out the main research directions in the field.

Future intelligent transportation systems and applications are expected to greatly benefit from the integration with a cloud computing infrastructure for service reliability and efficiency. More recently, fog computing has been proposed as a new computing paradigm to support low-latency and location-aware services by moving the execution of application logic on devices at the edge of the network in proximity of the physical systems, e.g. in the roadside infrastructure or directly in the connected vehicles. Such distributed runtime environment can support low-latency communication with sensors and actuators thus allowing functions such as monitoring and remote control, e.g. for remote telemetry of public transport vehicles or remote control under emergency situations, respectively. These applications will require support for some basic functionalities from the runtime. Among them, discovery of sensors and actuators will be a significant challenge considering the large variety of sensors and actuators and their mobility. In this paper, a discovery service specifically tailored for fog computing platforms with mobile nodes is proposed. Instead of adopting a centralized approach, we propose an approach based on a distributed hash table to be implemented by fog nodes, exploiting their storage and computation capabilities. The proposed approach supports by design multiple attributes and range queries. A prototype of the proposed service has been implemented and evaluated experimentally.

The Internet of Things (IoT) envisions billions of devices seamlessly connected to information systems, thus providing a sensing platform for applications. The availability of such a huge number of smart things will entail a multiplicity of devices collecting overlapping data and/or providing similar functionalities. In this scenario, efficient discovery and appropriate selection of things through proper context acquisition and management will represent a critical requirement and a challenge for future IoT platforms. In this work we present a practical approach to model and manage context, and how this information can be exploited to implement QoS-aware thing service selection. In particular, it is shown how context can be used to infer knowledge on the equivalence of thing services through semantic reasoning, and how such information can be exploited to allocate thing services to applications while meeting QoS requirements even in case of failures. The proposed approach is demonstrated through a simple yet illustrative experiment in a smart home scenario.

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.

Abstract The continuously increasing interest in developing efficient vehicle‐roadside communication networks to provide on‐board connectivity has recently brought to the definition of several solutions based on different wireless technologies. Among them, wireless local area network‐based solutions emerged as an attracting alternative to guarantee high‐quality connectivity enabling services, like video streaming, that require stringent quality of service guarantees. In this work, we propose a handoff procedure based on a forecasting model of the link quality for mobile routers operating in vehicle‐roadside wireless local area network‐based networks. First, a preliminary set of experiments is performed in a realistic environment to study the behavior of the wireless channel when mobility in urban environment is considered. Then, considering the hands‐on experience gained from the initial set of experiments, a novel handoff procedure is designed, which exploits a forecasting technique to predict link channel quality. The proposed procedure is then exploited in a cross‐layer manner to proactively reduce the number of transmitted layers during handoff in the case of real‐time video traffic based on H.264/SVC encoding. The proposal is assessed by means of simulation and compared with existing solutions. Results demonstrate that our proposal guarantees performance comparable with other algorithms. The advantage of predicting the handoff point is demonstrated by means of simulations employing a realistic video streaming traffic model, showing how the quality of experience perceived by end‐users can be improved through the adaptation of the traffic load. Copyright © 2016 John Wiley & Sons, Ltd.

The future Web of Things (WoT) foresees a web in which applications can seamlessly access physical objects through the same REST interface used today to access web services. A key enabler of this shift is the Constrained Application Protocol (CoAP), a redesign of the popular HTTP protocol that aims at supporting resource-constrained devices for Machine-to-Machine applications. In this work we propose a proxy virtualization framework to support scalability and easy implementation of custom functionalities in large WoT deployments. The functionalities offered by a CoAP proxy are exploited to transparently decouple applications from servers and to guarantee the implementation of custom policies and functionalities through virtualization techniques. A solution based on Linux Containers is implemented in a real test bed made of off-the-shelf hardware and open-source software, demonstrating the feasibility of the proposed design. Experimental results have shown that the response time is highly improved by our solution thanks to central coordination of concurrent requests from different virtual proxy instances. Moreover, to highlight the potential of the proposed frame-work, a priority-based Quality of Service policy is also implemented and evaluated.

Cloud computing is a key enabler for the development and deployment of large-scale IoT service platforms. The integration into such platforms of different sensing and actuating systems will imply the availability of many similar IoT services with common functionalities though with different QoS and cost. A cloud-based IoT platform can then benefit from implementing QoS-aware service selection algorithms to match application demand to IoT services, whilst guaranteeing to meet the respective QoS requirements. Such algorithms must however take into account that IoT service providers are usually constrained devices with limited computation, storage and energy capabilities. In this work we formulate the QoS-aware service selection problem for IoT cloud platforms as an integer optimization problem, whose solution minimizes the energy consumption so as to maximize the lifetime of battery-powered devices, whilst guaranteeing the fulfillment of real-time QoS requirements. We then propose a computationally efficient heuristic algorithm to solve the problem, and show through extensive numerical analysis that it is able to find solutions very close to the optimal one in all considered scenarios.

Lightweight link quality estimation is crucial in wireless sensor networks. Indeed, devices with limited capabilities shall trade off between consuming their resources to maintain a precise view of the neighbours' link quality and to build routes almost blindly. For instance, the Routing Protocol for Low-Power and Lossy Networks (RPL), which has been recently standardised by the IETF to enable IPv6-based sensor networks, only estimates the quality of the links used to deliver data packets. However, this solution has been demonstrated to cause periods of routing instability and reduced packet delivery rates since it estimates only the quality of utilised links. To address this issue in this work we propose a lightweight link estimation procedure that exploits Trickle-based topology maintenance techniques to simultaneously estimate link qualities and propagate routing information. Our proposed scheme has been integrated in the Contiki's RPL prototype implementation. Simulation results demonstrate that our proposal is capable of measuring the quality of the links to neighbours with small overhead, which results into better routing decisions and improved packet delivery rates.

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 .

In recent years, wireless mesh networks have become a popular solution for a wide range of scenarios because of their flexibility, reliability and fast deployment. In particular, in the context of providing network connectivity to mass transit vehicles, wireless mesh networks are often preferred to cellular networks because they allow custom network deployments. However, when high mobility is involved, an efficient handoff procedure is of paramount importance to ensure seamless connectivity. In this work, we propose a handoff procedure for wireless mesh networks which exploits a forecasting technique to predict link channel quality. The proposal is assessed by means of simulation and compared with existing solutions. Results demonstrate that our proposal outperforms existing algorithms and provides an overall link quality which is close to the optimal.

It is our great pleasure to welcome you to the Fourteenth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2013.

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.

It is our great pleasure to welcome you to the twelfth edition of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2011), taking place in the historical and charming city of Lucca, at the very heart of Tuscany, Italy, on June 20–23, 2011.

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.

The Mesh Coordinated Channel Access (MCCA) defined by the IEEE 802.11s draft standard implements a TDMA-based Medium Access Control (MAC) protocol by allowing mesh routers to negotiate collision-free transmission opportunities, called MCCAOPs, in a hop-by-hop manner. The procedure to determine the duration, in time slots, and the periodic schedule of MCCAOPs is however left unspecified by the standard. In this paper, we propose a Dynamic Delay-balancing Slot Allocation (D2SA) algorithm to deal with dynamic traffic conditions in the context of IEEE 802.11s MCCA. D2SA is fully distributed and aims at exploiting locally at each node the statistical multiplexing of different traffic flows in order to mitigate temporary congestion or under-utilization of the available capacity due to variable traffic demand. This is accomplished by dynamically balancing over the smallest possible time scale the queuing delays experienced by packets relayed to different neighbors. By means of extensive packet-level simulations under realistic network assumptions, we evaluate the effectiveness of D2SA and show that, by improving both the average and the percentiles of the delay per link at each node, it is able to yield better end-to-end performance than in the static case at basically no additional overhead cost.

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.

Interdomain Traffic Engineering (TE) across domains employing Path Computation Elements should allow a source domain to select a good AS path, i.e. one likely to allow the actual setup of an interdomain tunnel. This is impossible if the AS path is computed online during path setup (which happens, e.

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.

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

Multiservice networks host heterogeneous applications, requiring different qualities of service (QoS), the coexistence of which can be efficiently accounted by employing scheduling algorithms which are capable of providing different QoS simultaneously. In a previous work, we defined a reference dual-class (DC) paradigm, according to which rate-guaranteed flows are restrained from using more than their minimum guaranteed rate in the presence of backlogged best-effort flows and the latter share all the remaining capacity according to predetermined weights. The timed token service discipline (TTSD), which applies at the output link of a switch the same rules used to control medium access by the timed token protocol, was also introduced and analyzed therein. It was proven that TTSD shares most of the capacity which is not strictly needed by the rate-guaranteed flows among the best-effort ones, thus achieving one of the goals of the DC paradigm. However, in TTSD, best-effort flows can only share the available capacity equally. We take into account the issue of differentiating the capacity sharing among the best-effort flows: We define a generalized TTSD (GTTSD) in which the latter actually share capacity according to predefined weights in a weighted fair queuing service discipline. Formal analysis and simulation results show that GTTSD closely approximates the DC paradigm.

Deficit Round-Robin (DRR) is a scheduling algorithm devised for providing fair queueing in the presence of variable length packets. The main attractive feature of DRR is its simplicity of implementation: in fact, it can exhibit O(1) complexity, provided that specific allocation constraints are met. However, according to the original DRR implementation, meeting such constraints often implies tolerating high latency and poor fairness. In this paper, we first derive new and exact bounds for DRR latency and fairness. On the basis of these results, we then propose a novel implementation technique, called Active List Queue Method (Aliquem), which allows a remarkable gain in latency and fairness to be achieved, while still preserving O(1) complexity. We show that DRR achieves better performance metrics than those of other round-robin algorithms such as Pre-Order Deficit Round-Robin and Smoothed Round-Robin. We also show by simulation that the proposed implementation allows the average delay and the jitter to be reduced.

Interconnection networks of parallel systems are used for servicing traffic generated by different applications, often belonging to different users. When multiple users contend for channel bandwidth, fairness in bandwidth sharing becomes a key requirement. In fact, enforcing a fair sharing of channel bandwidth improves flow isolation, thus preventing misbehaving flows from affecting the performance of other flows. We present a novel packet scheduling algorithm, called eligibility-based round robin (EBRR), devised to provide fair queueing in interconnection networks. In fact, EBRR meets the constraints imposed by wormhole switching, which is the most popular switching technique in interconnection networks of parallel systems. It can also be applied to packet switching wide area networks (WANs), such as IP and ATM. We show that EBRR has O(1) complexity and better delay and fairness properties than existing algorithms of comparable complexity. Here, we also investigate the means for assessing the fairness of a scheduler: we show that using the relative fairness bound as a fairness measure may lead to erroneous results. We then propose an alternative measure, called the generalized relative fairness bound, that allows fairness to be assessed more precisely.

Multiservice networks face the challenge of managing several traffic classes simultaneously. Service disciplines devised for multiservice networks therefore need to be flexible, i.e., able to provide different types of service, in order to accommodate different traffic classes efficiently. In this paper, we present and analyze an innovative Timed Token Service Discipline (TTSD, which has been given Patent Application No. TO2000A001000) which is able to schedule both rate-guaranteed and best-effort traffic simultaneously at a feasible computational complexity. The service paradigm approximated by TTSD is unveiled by means of a fluid-flow analysis and proven to be different from the well-known Generalized Processor Sharing. We then describe the TTSD properties, deriving minimum rate guarantees and delay bounds for leaky-bucket constrained traffic, both in a single node and in a multinode environment. Furthermore, algorithms for selecting the TTSD parameters in order to achieve a prespecified rate and delay guarantees are proposed.

HIPERLAN Type 1 is a standard for high-speed wireless local area networks, developed by the European Telecommunications Standards Institute (ETSI). It supports directed point-to-point communication based on ad hoc network topology at a data transfer rate on the air interface of 23.5 Mbit/s. The paper deals with an analytical and simulation analysis of the HIPERLAN/1 MAC protocol. General closed formulas are derived for the major channel access performance indexes, which are used both to prove the operational stability of the MAC protocol, and to derive a figure for the maximum channel utilization achievable. A simulation analysis is then carried out, focusing both on the influence of traffic burstiness on protocol performance, and on the capability to manage traffic classes with different quality of service (QoS) requirements. Simulation results show that HIPERLAN/1 is well suited for managing the bursty traffic that is expected to be generated by wireless LAN applications. Furthermore, HIPERLAN/1 provides a good isolation of the delay-sensitive traffic from the best effort traffic.

The MAC protocol, known as MASCARA (Mobile Access Scheme based on Contention And Reservation for ATM), is an infrastructure-based, adaptive TDMA scheme, which combines reservation- and contention-based access methods to provide multiple access efficiency and Quality-of-Service (QoS) guarantees to wireless ATM terminal connections that share a common radio channel. Radio channel sharing is performed by the PRADOS (Prioritized Regulated Allocation Delay Oriented Scheduling) algorithm. In this paper we assess the capability of PRADOS to guarantee to voice and data traffic types the QoS they need. The analysis leads to the conclusion that PRADOS cannot avoid the interference between the various types of traffic. A criterion to alleviate this drawback is also outlined at the end of the paper.

This paper thoroughly analyses the HIPERLAN MAC protocol, which is a standard for wireless local area networks (WLANs) defined by the European Telecommunications Standards Institute (ETSI). Since the HIPERLAN MAC protocol belongs to the class of carrier sensing multiple access with collision avoidance (CSMA/CA), we analyze the HIPERLAN stability problem. We also present an in depth performance analysis, by simulation, of the HIPERLAN MAC protocol. The analysis is performed by considering data traffic patterns (advanced data traffic) which have a very similar shape to traffic generated by WWW applications. Furthermore, in the analysis we consider Poissonian data traffic too. Our conclusion is that the HIPERLAN MAC protocol performs satisfactorily, although performance measures with advanced traffic are worse than the corresponding performance measures with Poissonian traffic. Furthermore, we broadened our analysis to include the influence of the packet lifetime and channel access priority mechanism on the performance measures provided by HIPERLAN.

Several multiple access control (MAC) protocols of industrial interest can be modeled by discrete-state discrete-time Markov chains, with finite lower block Hessenberg probability transition matrices P/sub N/, where the diagonal blocks have a size which decreases while passing from the top left block (A/sub 0,0/) down to the right bottom block (A/sub N,N/). Such matrices have been identified in the literature as funnel matrices. In general, for meaningful systems of engineering interest, the size of P/sub N/ is so large that the computation of the P/sub N/ stationary probabilities (/spl pi/) with common numerical methods cannot be performed due to the computational cost involved. To calculate the stationary probabilities of P/sub N/ we have developed an innovative computational method which fully exploits the block Hessenberg structure of the matrix I-P/sub N/. In this way, we drastically reduce the overall computational cost with respect to the customarily used LU factorization, while still keeping the strong numerical stability of Gaussian elimination with diagonal adjustment. The potential of the new method is exploited to assess the performance of an access protocol for third-generation mobile systems, called PRMA++ (packet reservation multiple access protocol). This was designed within the European project RACE and has so far been studied, to the best of our knowledge, via simulative analysis. PRMA++ has received a lot of attention from manufacturers in the ongoing fifth framework program of the European Community.

The traffic performance of third generation mobile systems is greatly influenced by the multiple access protocols used in the radio access subsystem. The paper introduces an access protocol, SIR (service integration radio access), which has the potential for accommodating the requirements of speech and bursty data traffic in an efficient way. SIR is evolved from a protocol (PRMA++) studied in the framework of a TDMA-based version of the European Universal Mobile Telecommunication System, UMTS, and uses dedicated mechanisms for data bandwidth request collection and servicing.