Carlo Vallati

Trickle-F: Fair broadcast suppression to improve energy-efficient route formation with the RPL routing protocol

RPL (IPv6 Routing Protocol for Low Power and Lossy networks) is a routing protocol recently standardized by the IETF. RPL has been designed to operate in energy-constrained networks with thousands of nodes, and therefore it is one of the most promising candidate routing protocols for Advanced Metering Infrastructure (AMI) networks. In this paper a performance evaluation of RPL is presented. An extensive study of the protocol is carried out with particular focus on Trickle, the algorithm adopted to control routing update distribution across the network. The performance of the protocol is analyzed considering different Trickle parameters in order to capture their impact on route formation and node power consumption. Results highlight that the nondeterministic nature of Trickle can lead to sub-optimal route formation especially when high message suppression is per-formed. In order to mitigate this issue, an enhanced version of the protocol, namely Trickle-F, is proposed in order to guarantee fair broadcast suppression. Trickle-F is demonstrated to be effective in obtaining more efficient routes with the same power consumption of the original version.

Exploiting LTE D2D communications in M2M Fog platforms: Deployment and practical issues

Fog computing is envisaged as the evolution of the current centralized cloud to support the forthcoming Internet of Things revolution. Its distributed architecture aims at providing location awareness and low-latency interactions to Machine-to-Machine (M2M) applications. In this context, the LTE-Advanced technology and its evolutions are expected to play a major role as a communication infrastructure that guarantees low deployment costs, plug-and-play seamless configuration and embedded security. In this paper, we show how the LTE network can be configured to support future M2M Fog computing platforms. In particular it is shown how a network deployment that exploits Device-to-Device (D2D) communications, currently under definition within 3GPP, can be employed to support efficient communication between Fog nodes and smart objects, enabling low-latency interactions and locality-preserving multicast transmissions. The proposed deployment is presented highlighting the issues that its practical implementation raises. The advantages of the proposed approach against other alternatives are shown by means of simulation.

BETaaS: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things

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.

Performance evaluation of H.264/SVC video streaming over mobile WiMAX

Mobile broadband wireless networks, such as mobile WiMAX, have been designed to support several features like, e.g., Quality of Service (QoS) or enhanced data protection mechanisms, in order to provide true access to real-time multimedia applications like Voice over IP or Video on Demand. On the other hand, recently defined video coding schemes, like H.264 scalable video coding (H.264/SVC), are evolving in order to better adapt to such mobile environments with heterogeneous clients and time-varying available capacity. In this work we assess the performance of H.264/SVC video streaming over mobile WiMAX under realistic network conditions. To this aim, we make use of specific metrics, like PSNR (Peak Signal to Noise Ratio) or MOS (Mean Opinion Score), which are related to the quality of experience as perceived by the end user. Simulation results show that the performance is sensitive to the different available H.264/SVC encoding options, which respond differently to the loss of data in the network. On the other hand, if aggressive error recovery based on WiMAX data protection mechanisms is used, this might lead to unacceptable latencies in the video play out, especially for those mobiles with poor wireless channel characteristics.

QoE-Oriented performance evaluation of video streaming over WiMAX

Mobile broadband wireless networks, such as WiMAX, have been developed over the last years in order to support wireless access to real-time multimedia applications like Voice over IP or Video on Demand. To this aim, these networks are designed to provide guarantees in terms of network-related QoS parameters, like loss, delay, or jitter. However, the assessment of the performance of multimedia applications requires to take into account also the Quality of Experience (QoE), which is related to the quality of the service as it is perceived by the user, and is better measured by different metrics, like MOS or PSNR. In this paper, we first describe the simulation framework we developed to assess the QoE of video streaming over WiMAX. The framework integrates a WiMAX simulator based on ns-2 with tools for analyzing H.264/SVC compressed video, and allows to estimate the PSNR of actual videos whose streaming is simulated over WiMAX by means of ns-2. Based on this framework, we carry out a performance evaluation by considering videos encoded with different H.264/SVC scalability options and under realistic network conditions.

A distributed Delay-balancing Slot Allocation algorithm for 802.11s Mesh Coordinated Channel Access under dynamic traffic conditions

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.

CoAPthon: Easy development of CoAP-based IoT applications with Python

The Internet of Things (IoT) vision foresees billions of devices seamlessly integrated into information systems. In this context, the Constrained Application Protocol (CoAP) has been defined as a technology enabler to allow applications to interact with physical objects. In this work we present CoAPthon, an open-source Python-based CoAP library, which aims at simplifying the development of CoAP-enabled IoT applications. The library offers software developers a simple and easy-to-use programming interface to exploit CoAP as a communication protocol for rapid prototyping and deployment of IoT systems. The CoAPthon library is fully compliant with the CoAP RFC and implements in addition popular extensions such as the block-wise transfer and resource observing.

Ns2Voip++, an enhanced module for VoIP simulations

In the last ten years, many circuit-switched networks for voice have been replaced with packet-switched ones. Hence, simulating Voice over IP has become of paramount importance in assessing the performance of a network. However, a sound performance analysis should be carried out in conditions which are as close as possible to a real deployment. In this paper we present enhancements to ns2voip [1], a module for simulating realistic VoIP traffic with the ns2 simulator. In detail, we add new features, i.e. a correlated model for packet generation in a two-way conversation and implementing a set of realistic playout buffers to simulate the behavior of the receiver. Our code is available at http://cng1.iet.unipi.it/wiki/index.php/Ns2voip.

Energy-Efficient QoS-aware Service Allocation for the Cloud of Things

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.

Mobile-Edge Computing Come Home Connecting things in future smart homes using LTE device-to-device communications.

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.