Guido Oddi

MQ-Routing

Mobile-Ad-Hoc-Networks (MANETs) are self-configuring networks of mobile nodes, which communicate through wireless links. The main issues in MANETs include the mobility of the network nodes, the scarcity of computational, bandwidth and energy resources. Thus, MANET routing protocols should explicitly consider network changes and node changes into the algorithm design. MANETs are particularly suited to guarantee connectivity in disaster relief scenarios, which are often impaired by the absence of network infrastructures. Moreover, such scenarios entail strict requirements on the lifetime of the device batteries and on the reactivity to possibly frequent link failures. This work proposes a proactive routing protocol, named MQ-Routing, aimed at maximizing the minimum node lifetime and at rapidly adapting to network topology changes. The proposed protocol modifies the Q-Routing algorithm, developed via Reinforcement Learning (RL) techniques, by introducing: (i) new metrics, which account for the paths availability and the energy in the path nodes, and which are dynamically combined and adapted to the changing network topologies and resources; (ii) a fully proactive approach to assure the protocol usage and reactivity in mobile scenarios. Extensive simulations validate the effectiveness of the proposed protocol, through comparisons with both the standard Q-Routing and the Optimized Link State Routing (OLSR) protocols.

A dynamic load balancing algorithm for Quality of Service and mobility management in next generation home networks

Heterogeneity of connection technologies and nodes mobility open new challenges in home networks control strategies. Moreover, user’s needs are changing towards applications requiring high transmission speeds such as 3D gaming, enhanced interactivity and high definition video. Each of those applications puts several constraints on the network capabilities to guarantee requirements on the Quality of Service. In this paper we introduce an innovative concept based on fast load balancing algorithm operating on top of a convergence layer, in order to rapidly react to network changes and contemporaneously to satisfy strict application demands. We formulated the load balancing problem as a Multi-Commodity Flow and resolved it with a column generation approach using Lagrangian Relaxation and Dijkstra algorithm. The load balancing problem computational complexity is decreased with respect to state of the art load balancing solutions based on linear programming techniques. Proof of concept simulation results are reported.

A distributed multi-path algorithm for wireless ad-hoc networks based on Wardrop routing

Wireless ad-hoc networks are collections of devices which are able to communicate each other through wireless links. Those networks differ from infrastructure-based wireless networks for the absence of a centralized coordinator which handles all the communications among the devices. This leads to higher probability of packets collision, congestion of links, etc. Moreover, wireless links are characterized by an intrinsic high and time varying packet loss ratio, due to external noise and interferences. The objective of this paper is to present a new distributed multi-path algorithm (i.e., traffic is split among multiple paths) for wireless ad-hoc networks with the aims of (i) increasing the throughput of the applications running onto the network (ii) explicitly accounting for the packet loss of the wireless links and (iii) guaranteeing that the routing process converges to stable paths. The algorithm is developed by using the concept of Wardrop equilibrium. Simulation results show the higher throughput achieved by the proposed routing algorithm, compared to shortest path routing protocols, based on hop count and on packet loss metrics.

A Resource Allocation Algorithm of Multi-cloud Resources Based on Markov Decision Process

Cloud technologies can nowadays be considered as commodities. The possibility of getting access to storage, computing and networking virtual resources empowers any business that needs dynamic IT capabilities. The Cloud Management Broker (CMB) plays a crucial role to handle heterogeneous virtualized cloud resources in order to offer a unique set of interfaces to the cloud users. Moreover, the CMB is in charge of optimizing the usage of the cloud resources, satisfying the requirements declared by the users. This paper proposes a novel multi-cloud resource allocation algorithm, based on a Markov Decision Process (MDP), capable of dynamically assigning the resources requests to a set of IT resources (storage or computing resources), with the aim of maximizing the expected CMB revenue. Simulation results show the feasibility and the higher performances obtained by the proposed algorithm, compared to a greedy approach.

Admission Control and Drop strategies in a UPnP-QoS controlled home network

The spread of novel multimedia services such as audio and video streaming, HDTV and online gaming have become a ordinary in the home networks. Therefore, the possibility to manage the Quality of Service (QoS) within an home environment becomes a crucial factor for the satisfaction of the user. In this paper we propose an enhanced Admission Control and Drop strategies for a UPnP-QoS controlled home network to guarantee QoS for multiple concurrent services. The UPnP-QoS architecture works well in the case of moderate traffic loads, but may fail whenever the network becomes overloaded. The paper demonstrates how the combined use of the proposed admission control and drop solutions with the UPnP-QoS framework avoids network congestion and guarantees QoS to all the admitted traffic streams. A proof of concept Java implementation has been developed to demonstrate the expected results in a real home environment.

Convergence in Home Gigabit Networks: Implementation of the inter-MAC layer as a pluggable kernel module

Many new broadband, emerging applications such as high definition 3D video streaming or on-line gaming are going to be part of the future home. Current home networks are still not capable to cope with the tight requirements asked by these kind of applications. Moreover, many underlying wired and wireless transmission technologies are available, but no solution to reach a convergent framework is available. In this paper, we present a preliminary software implementation of a novel solution, called Inter-MAC, that realizes the convergence at MAC layer enabling a reliable and high performance delivery of application data and services. Our results show how it is possible to split flows onto different technologies when the overloading of a single link would cause unacceptable delay and packet loss results. This is done in a plug and play fashion: loading a kernel module in a running Linux machine.

A proactive link-failure resilient routing protocol for MANETs based on reinforcement learning

Mobile-Ad-Hoc-Networks (MANET) are self-configuring networks of mobile nodes, which communicate through wireless links. One of the main issues in MANETs is the mobility of the network nodes: routing protocols should explicitly consider network changes into the algorithm design. MANETs are particularly suited to guarantee connectivity in disaster relief scenarios, which are often impaired by the absence of network infrastructures. This work proposes a proactive routing protocol, developed via Reinforcement Learning (RL) techniques, to dynamically choose the most stable path, basing on GPS information, among the feasible ones and to consequently increase resiliency to link failures. Simulations show the effectiveness of the proposed protocol, through comparison with the Optimized Link State Routing (OLSR) protocol.

Energy balancing in multi-hop Wireless Sensor Networks: an approach based on reinforcement learning

Wireless Sensor Networks (WSNs) are made of spatially distributed autonomous sensors, which cooperate to monitor a certain physical or environmental condition and pass their data through a network to a central data sink. A promising field of application of WSNs is planet exploration, in which a continuous monitoring of the surface is necessary, to have a clear notion of planet conditions and prepare for a future manned mission. The potentially large size of the region to be monitored and the line-of-sight limitations on remote planets (for instance the Moon, as studied in the SWIPE project [1]), impose constraints on the possibility to have 1-hop sensor-sink communication. Therefore, the sensors must be able to create and maintain a multi-hop ad hoc network, to allow sensed data to reach the sink. This paper extends the Q-Routing algorithm, designed for fixed and mobile networks, in order to be applicable in WSNs. The proposed routing algorithm aims at optimizing the network lifetime, by balancing the routing effort among the sensors, taking into account their current residual batteries, while minimizing the control overhead. Simulation results show an increase of performances, in grid-based networks, which are common topologies for WSNs.

Space Wireless Sensor Networks for planetary exploration: Node and network architectures

Wireless Sensor Networks (WSNs) are made of a usually large number of nodes deployed over an area of interest in order to monitor specific phenomena. WSNs constitute a promising technology for planetary exploration, since they can be deployed in order to monitor the environmental conditions on a planets surface, also in view of possible manned missions. This paper deals with the design of node and network architectures in a WSN targeted for planetary exploration, with a particular focus on the challenges, the driving principles and the design solutions adopted at WSN node and network level. Also, the paper introduces the basic architecture supporting data fusion at node and network level, which plays a fundamental role in order to increase overall WSN performances.

A distributed algorithm for Ad-hoc network partitioning based on Voronoi Tessellation

This paper presents a data sink node election algorithm for multi-hop Wireless Sensor Networks (WSNs) with multiple data sink nodes. For energy-saving considerations, these nodes should be evenly (spatially) distributed on the network area. To achieve this objective, it proposed a distributed and iterative algorithm, which periodically re-assigns the data sink roles to selected WSN nodes. The main innovation of the algorithm is that, even if it does not need to explicitly compute the Voronoi partition of the WSN at each iteration, it eventually partitions the network according to a Centroidal Voronoid Tessellation, which leads to a spatially well-balanced distribution of the data sink nodes. Analytical proofs as well as simulation results validate the approach.