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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.

Second Harmonic Generation (SHG) microscopy has gained much interest in the histopathology field since it allows label-free imaging of tissues simultaneously providing information on their morphology and on the collagen microarchitecture, thereby highlighting the onset of pathologies and diseases. A wide request of image analysis tools is growing, with the aim to increase the reliability of the analysis of the huge amount of acquired data and to assist pathologists in a user-independent way during their diagnosis. In this light, we exploit here a set of phasor-parameters that, coupled to a 2-dimensional phasor-based approach (μMAPPS, Microscopic Multiparametric Analysis by Phasor projection of Polarization-dependent SHG signal) and a clustering algorithm, allow to automatically recover different collagen microarchitectures in the tissues extracellular matrix. The collagen fibrils microscopic parameters (orientation and anisotropy) are analyzed at a mesoscopic level by quantifying their local spatial heterogeneity in histopathology sections (few mm in size) from two cancer xenografts in mice, in order to maximally discriminate different collagen organizations, allowing in this case to identify the tumor area with respect to the surrounding skin tissue. We show that the “fibril entropy” parameter, which describes the tissue order on a selected spatial scale, is the most effective in enlightening the tumor edges, opening the possibility of their automatic segmentation. Our method, therefore, combined with tissue morphology information, has the potential to become a support to standard histopathology in diseases diagnosis.

Edge computing is emerging as an effective solution to fulfil the requirements of time-critical Internet of Things (IoT) applications by enabling the execution of data processing tasks at the network edge, in proximity to data producers. In particular, Multi-access edge computing (MEC) is attracting considerable interest in the mobile telco industry, as it supports edge computing capabilities within the mobile cellular network. 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 first formulating the resource allocation, device association and data routing problem in a multi-cell MEC network as a mixed-integer non-linear programming problem that minimises the utilisation of communication and edge resources. 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.

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.

Fog computing provides resources and services in proximity to users. To achieve latency and throughput requirements of mobile users, it may be useful to migrate fog services in accordance with user movement - a scenario referred to as follow me cloud. The frequency of migration can be adapted based on the mobility pattern of a user. In such a scenario, the fog computing infrastructure should simultaneously accommodate users with different characteristics, both in terms of mobility (e.g., route and speed) and Quality of Service requirements (e.g., latency, throughput, and reliability). Migration performance may be improved by leveraging "network slicing", a capability available in Software Defined Networks with Network Function Virtualisation. In this work, we describe how we extended our simulator, called MobFogSim, to support dynamic network slicing and describe how MobFogSim can be used for capacity planning and service management for such mobile fog services. Moreover, we report an experimental evaluation of how dynamic network slicing impacts on container migration to support mobile users in a fog environment. Results show that dynamic network slicing can improve resource utilisation and migration performance in the fog.

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.

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.

The Constrained Application Protocol (CoAP) is an IETF standard application protocol for the future Internet of Things (IoT). Since IoT devices are often interconnected by networks characterized by high packet error rates and low throughput, congestion control will be crucial to ensure proper and timed communication in these networks. Therefore, CoCoA+, an advanced congestion control algorithm for CoAP, is currently being specified by the IETF. In this work, we present a critical analysis of CoCoA+ performance and highlight some of its shortcomings and pitfalls. Two different scenarios are considered: one with an increasing traffic load due to an increasing number of CoAP requests, and another with an interfering traffic concurrently transmitted in the network characterized by a bursty pattern. In the former scenario, we show how CoCoA+ may be characterized by many spurious retransmissions at some offered loads close to congestion. In the latter, we show instead how the weak estimator is not particularly effective in adapting to changing traffic loads. In order to overcome such limitations, a number of modifications to CoCoA+ are proposed. The resulting solution, named precise Congestion Control (pCoCoA), is shown to reduce the number of retransmissions, while guaranteeing throughputs and delays comparable to those of CoAP and CoCoA+.

To many, fog computing is considered the next step beyond the current centralized cloud that will support the forthcoming Internet of Things (IoT) revolution. While IoT devices will still communicate with applications running in the cloud, localized fog clusters will appear with IoT devices communicating with application logic running on a proximate fog node. This will add proximity-based machine-to-machine (M2M) communications to standard cloud-computing traffic, and it calls for efficient mobility management for entire fog clusters and energy-efficient communication within them. In this context, long-term evolution-advanced (LTE-A) technology is expected to play a major role as a communication infrastructure that guarantees low deployment costs, native mobility support, and plug-andplay seamless configuration.

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 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.

The Constrained Application Protocol (CoAP) is gaining attention as a standardised RESTful interface for the Internet of Things (IoT). Recent studies have focused on different congestion control strategies for CoAP, in order to ensure proper operation of large-scale IoT deployments. In this paper, we carry out a performance evaluation of different congestion control policies for CoAP in a realistic environment by exploiting WiSHFUL, a platform for large-scale experimentation of network architectures. Our goal is to analyse different congestion control policies and their interplay with the routing protocol in a real environment, where unstable links and route fluctuations are frequent, due to channel variability. The results of our experiments highlight that the dynamics of the routing protocol have a noticeable impact and can impair significantly the performance of the congestion control algorithm. Specifically, the influence of the routing protocol depends on the specific congestion control policy adopted: an aggressive policy that re-transmits messages more frequently, e.g. CoCoA, is more penalised than others, in terms of throughput.

In the traveling salesman problem with pickup, delivery, and ride-time constraints (TSPPD-RT), a vehicle located at a depot is required to service a number of requests where the requests are known before the route is formed. Each request consists of (i) a pickup location (origin), (ii) a delivery location (destination), and (iii) a maximum allowable travel time from the origin to the destination (maximum ride-time). The problem is to design a tour for the vehicle that (i) starts and ends at the depot, (ii) services all requests, (iii) ensures that each request's ride-time does not exceed its maximum ride-time, and (iv) minimizes the total travel time required by the vehicle to service all requests (objective function). A capacity constraint that may be present is that the weight or volume of the undelivered requests on the vehicle must always be no greater than the vehicle's capacity. In this article, we concurrently analyze the TSPPD-RT with capacity constraints and without capacity constraints. We describe two mathematical formulations of the problem. These formulations are used to derive new lower bounds on the solution to the problem. Then, we provide two exact methods for finding the optimal route that minimizes the total travel cost. Our extensive computational analysis on both versions of the TSPPD-RT shows that the proposed algorithms are capable of solving to optimality instances involving up to 50 requests. © 2015 Wiley Periodicals, Inc. NETWORKS, 2015

The integration between the Internet of Things (IoT) and fog computing can pave the way to a plethora of applications. Fog computing indeed allows IoT devices to offload complex tasks to computing resources, known as fog nodes, that are in their proximity (e.g., at the network edge). Fog proximity enables important advantages, first and foremost low latency. However, IoT device mobility endangers those advantages, as the IoT device gets farther away from the serving fog node. Migrating the fog service among fog nodes, following the device route, permits to maintain proximity and preserve low latency. In this work, we propose an OpenStack-based platform that implements a fog service as a container and migrates the latter to support device mobility. We performed experiments over a real testbed to: (i) evaluate the impact of hardware resources of fog nodes on migration performance; (ii) validate our platform. Results are encouraging, as the average round-trip latency between the mobile device and the fog layer was as low as 10 ms and exceeded the maximum value allowed by the considered application (i.e., 20 ms) in 1.5% of the experiment duration.

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.

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., submitted to the 20TH IEEE INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS (IEEE WOWMOM 2019)

Many new Internet-of-things (IoT) devices and solutions appear in the market every day. Although commercial IoT products are the majority, Do-It-Yourself (DIY) solutions implemented by independent developers still represent a significant driving force. In this scenario, the availability of development tools for both less experienced developers and professionals to reduce the time to create prototypes is crucial. In this paper, we first review the tools available to implement all the components of a typical IoT architecture in different programming languages, then, we analyze how Python can be used to implement all the components of a typical IoT architecture. As a practical example, we illustrate the implementation of a smart home system built exploiting low-cost off-the-shelf hardware and programmed only through Python.

Fog computing is an extension of the cloud towards the network edge that brings resources and services of computing in closer proximity to end users. This proximity provides several benefits such as reduced latency that improves user experience. However, user mobility may limit such benefits in practice, as the distance to a fog service may vary as a user moves from one location to another. Migration of a fog service may be one possible mitigation strategy, enabling the service to always be close enough to a user. Although many simulators exist for evaluating application behaviour and performance within a fog computing environment, none allows evaluation of service migration solutions to support mobility. MobFogSim is presented in this work to overcome this limitation. It extends iFogSim to enable modelling of device mobility and service migration in fog computing. MobFogSim is validated by comparing simulation results with those obtained from a real testbed where fog services are implemented as containers. Additional experiments are carried out in MobFogSim taking account of various mobility patterns of a user, derived from Luxembourg SUMO Traffic (LuST). We use an experiment-based approach to study the impact of user mobility on container migration in fog computing.

The Internet of Things (IoT) is becoming real, and recent studies highlight that the number of IoT devices will significantly grow in the next decade. Such massive IoT deployments are typically made available to applications as a service by means of IoT platforms, which are aware of the characteristics of the connected IoT devices&ndash, usually constrained in terms of computation, storage and energy capabilities&ndash, and dispatch application&rsquo, s service requests to appropriate devices based on their capabilities. In this work, we develop an energy-aware allocation policy that aims at maximizing the lifetime of all the connected IoT devices, whilst guaranteeing that applications&rsquo, Quality of Service (QoS) requirements are met. To this aim, we formally define an IoT service allocation problem as a non-linear Generalized Assignment Problem (GAP). We then develop a time-efficient heuristic algorithm to solve the problem, which is shown to find near-optimal solutions by exploiting the availability of equivalent IoT services provided by multiple IoT devices, as expected especially in the case of massive IoT deployments.

The internet of things (IoT) is essential for the implementation of applications and services that require the ability to sense the surrounding environment through sensors and modify it through actuators. However, IoT devices usually have limited computing capabilities and hence are not always sufficient to directly host resource-intensive services. Fog computing, which extends and complements the cloud, can support the IoT with computing resources and services that are deployed close to where data are sensed and actions need to be performed. Virtualisation is an essential feature in the cloud as in the fog, and containers have been recently getting much popularity to encapsulate fog services. Besides, container migration among fog nodes may enable several emerging use cases in different IoT domains (e.g., smart transportation, smart industry). In this paper, we first report container migration use cases in the fog and discuss containerisation. We then provide a comprehensive overview of the state-of-the-art migration techniques for containers, i.e., cold, pre-copy, post-copy, and hybrid migrations. The main contribution of this work is the extensive performance evaluation of these techniques that we conducted over a real fog computing testbed. The obtained results shed light on container migration within fog computing environments by clarifying, in general, which migration technique might be the most appropriate under certain network and service conditions.

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).

The aim of this chapter is to describe and comment on analytical approaches for characterizing intentionally added substances (IASs) with specific migration limits (SMLs) and non-intentionally added substances (NIASs) present in polyolefins used in food contact applications. Current EU regulations require the monitoring of packaging material components that could migrate into food, to avoid possible safety and quality issues. Generally, an extraction step involving static headspace (SHS), dynamic headspace and solvent extraction is applied for this purpose. Mass spectrometry coupled with chromatography is the main technique that can provide quantification and identification in line with regulatory requirements. Also, flame ionization detection (FID) is widely used as it allows universal quantitation in many cases. The described techniques can be applied to most of the plastic materials used in food packaging. In this chapter, only polyolefin-based polymers are considered and examples are focused mainly on polypropylene (PP) and its copolymers.

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.

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 this Companion 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.

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.

This paper provides a review of the recent developments that had a major impact on the current state-of-the-art exact algorithms for the vehicle routing problem (VRP). The paper reviews mathematical formulations, relaxations and recent exact methods for two of the most important variants of the VRP: the capacitated VRP (CVRP) and the VRP with time windows (VRPTW). The paper also reports a comparison of the computational performances of the different exact algorithms for the CVRP and VRPTW.

We present the result of research, developed within Nokia Siemens Networks, to solve the downlink sub-frame allocation problem in Mobile WiMAX (IEEE 802.16) technology in its full complexity, while simultaneously fulfilling real-life constraints on processing power and delay. We describe the IEEE 802.16 standard, and introduce two system models. A theoretical analysis of the two-dimensional packing problems originated by such models shows that they are both 𝒩𝒫-hard in the strong sense. From a practical point of view, the processing budget for scheduling in the base station was estimated to be 1 ms on a state-of-the-art PC. Thus, we introduce two highly efficient heuristics that were developed to handle the system practically. A thorough computational analysis of their optimization characteristics and a system-level evaluation in realistic scenarios proved that the algorithms offer significant capacity gain in Mobile WiMAX systems that translate to increased operator revenues. This paper was accepted by Dimitris Bertsimas, optimization.

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.

The Internet of Things is expected to radically reshape many processes in a broad range of domains, from personal to industrial. In many of these heterogeneous scenarios, IoT systems will need to guarantee required levels of reliability and latency in order to provide high-quality services to end users. Quality of service support in IoT system will demand for explicit support at different levels. At the network level, on the one hand, specific technical communication standards will be necessary to ensure timed and reliable data delivery. At the application level, instead, dedicated support from application protocols and design of novel resource allocation algorithms will be mandatory to cope with concurrent access and implement proper management of resources. In this chapter, an overview of the current solutions for ensuring QoS in IoT systems is provided. Specifically, we first survey the current approaches at the network level through a summary of all the mechanisms included in the main communication standards for IoT. We then deliver an analysis of the current solutions available in IoT protocols and platforms to enforce QoS at the application level.

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 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 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.

The Constrained Application Protocol (CoAP) is expected to be the de-facto standard application protocol for the future Internet of Things (IoT). Future IoT devices will be interconnected by networks characterized by high packet error rates and low throughput. For this reason, congestion control will be crucial to ensure proper and timed communication in these networks. In this context, CoCoA+, an advanced congestion control for CoAP, is currently under standardization. In this work, we present a critical analysis of CoCoA+ by means of simulation, and highlight some of its shortcomings and pitfalls. We considered a typical scenario with an increasing traffic load due to an increasing number of CoAP requests. We show how CoCoA+ may be characterized by many spurious retransmissions at some offered loads close to congestion.

Over the last few years, standardisation efforts are consolidating the role of the Routing Protocol for Low-Power and Lossy Networks (RPL) as the standard routing protocol for IPv6-based Wireless Sensor Networks (WSNs). Although many core functionalities are well defined, others are left implementation dependent. Among them, the definition of an efficient link-quality estimation (LQE) strategy is of paramount importance, as it influences significantly both the quality of the selected network routes and nodes’ energy consumption. In this paper, we present RL-Probe, a novel strategy for link quality monitoring in RPL, which accurately measures link quality with minimal overhead and energy waste. To achieve this goal, RL-Probe leverages both synchronous and asynchronous monitoring schemes to maintain up-to-date information on link quality and to promptly react to sudden topology changes, e.g. due to mobility. Our solution relies on a reinforcement learning model to drive the monitoring procedures in order to minimise the overhead caused by active probing operations. The performance of the proposed solution is assessed by means of simulations and real experiments. Results demonstrated that RL-Probe helps in effectively improving packet loss rates, allowing nodes to promptly react to link quality variations as well as to link failures due to node mobility.

The two-dimensional orthogonal non-guillotine cutting stock problem (NGCP) appears in many industries (e.g. the wood and steel industries) and consists of cutting a rectangular master surface into a number of rectangular pieces, each with a given size and value. The pieces must be cut with their edges always parallel to the edges of the master surface (orthogonal cuts). The objective is to maximize the total value of the pieces cut. New upper bounds on the optimal solution to the NGCP are described. The new bounding procedures are obtained by different relaxations of a new mathematical formulation of the NGCP. Various procedures for strengthening the resulting upper bounds and reducing the size of the original problem are discussed. The proposed new upper bounds have been experimentally evaluated on test problems derived from the literature. Comparisons with previous bounding procedures from the literature are given. The computational results indicate that these bounds are significantly better than the bounds proposed in the literature.

The Observing Resources extension of the Constrained Application Protocol (CoAP) is expected to play a major role in future Internet of Things (IoT) applications, since it enables asynchronous monitoring of IoT resources in an efficient manner. As the number of IoT applications will grow, solutions are needed to manage multiple observe relationships for the same resource, especially if hosted by a constrained IoT device. The problem is further exacerbated since multiple observers may have heterogeneous Quality of Service (QoS) requirements on the minimum and maximum frequency with which to receive notifications, while the resource-constrained server may only be able to provide notifications with one fixed period for all. In this work, we propose an efficient solution to support multiple CoAP observers with QoS requirements. In particular, we formulate the problem of finding the notification period which minimizes the amount of messages sent by the server, so as to reduce energy consumption of IoT devices and congestion in constrained networks, while ensuring all observers' requirements are met. We then propose an algorithm, named Observation Period Selection (OPS), which is proved to find the optimal solution to this problem. OPS is also evaluated experimentally to analyze its effectiveness in some simple scenarios.

The Internet of Things (IoT) is rapidly evolving from a vision to reality. Practical realisations of IoT systems will require experimentation and trials on real testbeds to ensure proper functioning and scalability under realistic conditions and environments. To this aim, the availability of IoT testbeds will be of paramount importance to foster the future development of IoT solutions. In this paper we present the Pisa IoT Testbed, with the aim of providing a platform for rapid prototyping of IoT solutions, as well as to allow their assessment at different levels, from network to application. To show the capabilities of our testbed, in this paper we present a preliminary experimental study of the influence of different link quality assessment strategies on the performance of wireless sensor networks adopting RPL as routing protocol. In particular, we evaluate the trade-offs between passive and active link monitoring strategies in terms of overheads and responsiveness to link quality variations.

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.

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. European Commission's FP7

The performance of a wireless mesh network (WMN) can be significantly affected by node failures and medium-term time-varying phenomena in the wireless channel, which may lead to significant interruptions in packet delivery service. Because of their nature, WMNs are often deployed with an inherent degree of redundancy. In principle, this flexibility might be exploited to cope with the link failure problem by temporarily re-routing ongoing traffic on alternate paths. However, the existing routing protocols cannot be used for this purpose, since they either rely on link-state updates, which happen on a longer time scale than the phenomena under consideration, or the decisions are taken by the source node, which does not know the status on intermediate links. In this paper we present a Fast Re-Routing enabled architecture that is optimized for WMNs and allows nodes to react to local node or link failures by activating a pre-configured alternate path to reach a two-hop neighbor. The proposed scheme is enabled by label switching/stacking. Alternate paths are created automatically during the path setup phase via a lightweight protocol derived from LDP. The solution is proved to be effective through a testbed implementation.

In current mobile broadband wireless access (BWA) technologies, which are based on orthogonal frequency division multiple access (OFDMA), terminals close to cell edge experience poor channel quality, due to severe path-loss and high interference from concurrent transmissions in nearby cells. To mitigate this problem we propose: (a) to partition the set of sub-channels into chunks, which are assigned different power levels; (b) the design of a data scheduling and allocation algorithm positioned in the medium access control (MAC) layer of the base station (BS), exploiting such a partitioning. The framework is analyzed in a multi-cell IEEE 802.16 network by means of system-level packet-based simulations, with detailed MAC and physical layer abstractions in combination with realistic models of the wireless channel and interference.

The crew scheduling problem (CSP) appears in many mass transport systems (e.g., airline, bus, and railway industry) and consists of scheduling a number of crews to operate a set of transport tasks satisfying a variety of constraints. This problem is formulated as a set partitioning problem with side constraints (SP), where each column of the SP matrix corresponds to a feasible duty, which is a subset of tasks performed by a crew. We describe a procedure that, without using the SP matrix, computes a lower bound to the CSP by finding a heuristic solution to the dual of the linear relaxation of SP. Such dual solution is obtained by combining a number of different bounding procedures. The dual solution is used to reduce the number of variables in the SP in such a way that the resulting SP problem can be solved by a branch-and-bound algorithm. Computational results are given for problems derived from the literature and involving from 50 to 500 tasks.

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.

This work describes an architectural framework that allows inter-domain Traffic Engineering Label Switched Paths (TE-LSPs) with guaranteed quality of service (QoS) to be setup. Such TE-LSPs, called EQ-links, are setup by coordinating path computation elements (PCEs) of neighboring autonomous systems (ASs) along a pre-determined inter-AS path, computed through cooperative interaction between pairs of neighboring ASs. After defining the architectural requirements for the framework, we describe and analyze the Inter-AS Path Computation Protocol (IA-PCP), which computes an interdomain path at the AS level, i.e., selecting a sequence of ASs to the destination, based on a loose source routing approach. The results of the IA-PCP computations are then fed to the PCEs for complete path computation. The proposed architecture has been actually implemented within the testbed of the EuQoS project, which is aimed at enabling end-to-end QoS in the Internet. We report results related to the setup time of EQ-links, measured in the pan-European testbed of the EuQoS project, showing that path computation and setup takes an affordable time overhead.

Future 5G cellular networks are expected to play a major role in supporting the Internet of Things (IoT) due to their ubiquitous coverage, plug-and-play configuration, and embedded security. Besides connectivity, however, the IoT will need computation and storage in proximity of sensors and actuators to support timecritical and opportunistic applications. Mobile-edge computing (MEC) is currently under standardization as a novel paradigm expected to enrich future broadband communication networks [1], [2]. With MEC, traditional networks will be empowered by placing cloud-computing-like capabilities within the radio access network, in an MEC server located in close proximity to end users. Such distributed computing and storage infrastructure will enable the deployment of applications and services at the edge of the network, allowing operators to offer a virtualized environment to enterprise customers and industries to implement applications and services close to end users.

Interoperability between smart devices and applications across multiple architectures, platforms and communication technologies is a key challenge for the future Internet of Things (IoT). Many IoT open platforms are being developed to overcome this issue, such as the AllJoyn™ framework specified by the AllSeen industrial alliance. Due to heavy resource constraints, however, it's not always a feasible option to run such framework on low-power devices, where other mature technologies like, e.g., the Constrained Application Protocol (CoAP), are already successfully applied. In this work we present the design and open-source implementation of an application acting as a transparent bridge between AllJoyn and CoAP networks. The bridge leverages advanced CoAP features, i.e., caching and resource observation, for efficiency and scalability, and provides AllJoyn applications with a rich low-level interface to interact with multiple CoAP servers hosting CoAP resources. The implementation has been extensively validated in a real test case, and proved to properly work in a transparent manner.

The integration of everyday objects into the Internet represents the foundation of the forthcoming Internet of Things (IoT). Smart objects will be the building blocks of the next generation of applications that will exploit interaction between machines to implement enhanced services with minimum or no human intervention in the loop. A crucial factor to enable Machine-to-Machine (M2M) applications is a horizontal service infrastructure that seamlessly integrates existing IoT heterogeneous systems. The authors present BETaaS, a framework that enables horizontal M2M deployments. BETaaS is based on a distributed service infrastructure built on top of an overlay network of gateways that allows seamless integration of existing IoT systems. The platform enables easy deployment of applications by exposing to developers a service oriented interface to access things (according to a Things-as-a-Service model) regardless of the technology and the physical infrastructure they belong to.