Gabriele Monti

W-Grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks

Data-centric sensor networks are advanced ad hoc networks that act like a distributed database managing and indexing sensed data in order to efficiently perform advanced in-network tasks, such as routings, searches, data processing, fusion and analysis. The supplied distributed services, such as routing, content location and information sharing should be provided anywhere and at any time optimizing energy consumptions, computational resources, memory occupation and radio transmissions. Moreover, the network traffic should be equally balanced among participants in order to avoid premature discharge of some devices that may partition the network. This work describes a fully decentralized infrastructure able to self-organize nodes in ad hoc networks by exploiting local interactions and topology learning among devices. In this solution all nodes are peers and nothing prevent the approach to be used in wireless mesh networks as well. Differently from existing solutions, our proposal does not require global information or external help, such as the Global Positioning System, which works only outdoor with a precision and an efficacy both limited by weather conditions and obstacles. The infrastructure natively enables devices to perform routing and data management without using message broadcast/flooding operations. The work introduces also a feature, called full learning, that improves routing performances while balancing the traffic among devices. We report an extensive number of simulations comparing the new solution results with four existing proposals, two of which deriving from preceding versions of the infrastructure.

W-Grid: a Cross-Layer Infrastructure for Multi-Dimensional Indexing, Querying and Routing in Wireless Ad-Hoc and Sensor Networks

Large scale wireless ad-hoc networks of computers, sensors, PDAs etc. (i.e. nodes) are revolutionizing connectivity and leading to a paradigm shift from centralized systems to highly distributed and dynamic environments. A plethora of routing algorithms have been proposed for the network path discovery ranging from broadcasting/flooding-based approaches to those using Global Positioning Systems (GPS). In this paper we propose a novel decentralized infrastructure that self-organizes wireless devices in an ad-hoc network, where each node has one or more virtual coordinates through which both message routing and data management occur without reliance on either flooding/broadcasting operations or GPS. The resulting ad-hoc network does not suffer from the dead-end problem, which happens in geographic-based routing when a node is unable to locate a neighbor closer to the destination than itself. The multi-dimensional data management capability will be described showing, as an example, how the location service reduces to a simple query, like for any other data type. Extensive performance analysis and experiments have been conducted and the results compared to GPSR, which is considered the most efficient routing solution not using broadcast operations. Our approach shows significant performance gains

W*-Grid: A Robust Decentralized Cross-layer Infrastructure for Routing and Multi-Dimensional Data Management in Wireless Ad-Hoc Sensor Networks

Network coding is an emerging field of research with sound and mature theory supporting it. Recent works shows that it has many benefits like improved fault tolerance, higher flexibility in selection of file parts to transfer and resiliency to network partitions [4, 3]. Despite those appealing properties there is no wide usage of network coding in real file sharing applications. In this work, we try to bridge the gap between theory of network coding and practice. From the one hand, we deploy one the most successful file sharing client, the BitTorrent client. We use the BitTorrent algorithm for optimizing the neighbor selections for maximizing the upload bandwidth. From the other hand, we propose several simple heuristics that improve significantly the efficiency of the network coding deployed. In a nutshell, we propose computation intensive variant of network coding that can be applied to most of the existing network coding protocols. By changing the random selection of coded parts to a selection based on feedback from the network, we significantly improve the network utilization and the efficiency of the protocol. In this paper we report our work in progress building the BitCod client. Using extensive simulations we demonstrate that our technique can compete with the performance of the state-of-the-art BitTorrent [2] file sharing client. Next, we plan to implement and test a prototype of the BitCod client over the WAN.Sensor networks are usually composed by small units able to sense and transmit to a sink elementary data which are successively processed by an external machine. However recent improvements in the memory and computational power of sensors, together with the reduction of energy consumptions, are rapidly changing the potential of such systems, moving the attention towards data-centric sensor networks. This paper presents W*-Grid, a fully decentralized and robust infrastructure for self-organizing data- centric sensor networks, where wireless communications occur through multi-hop routing among devices. The solution extends W-Grid by strongly improving the network recovery performance from link and/or device failures. In particular W*-Grid guarantees, by construction, at least two disjoint paths between each couple of nodes. This implies that the recovery in W*-Grid occurs without broadcasting transmissions and guaranteeing robustness while drastically reducing the energy consumption. An extensive number of simulations show the efficiency, robustness and traffic load of resulting networks under several scenarios of device density and of number of coordinates.

Multidimensional Range Query and Load Balancing in Wireless Ad Hoc and Sensor Networks

Sensor networks are usually composed by spatially distributed devices able to monitor physical or environmental conditions (pressure, temperature, motion, etc.). This kind of networks were originally built with the idea of transmitting elementary information to external sinks for further processing and querying. Nowadays, the growth of sensors memory and computational capability together with the significant reduction of energy consumptions have changing the potential of sensor networks allowing in-network storage and processing. The W-Grid infrastructure follows a data centric approach that indexes data according to any number of attributes so that it is possible to query events of interest through multi-dimensional range queries. Differently from existing data centric solutions W-Grid does not use either sensors physical position (i.e. GPS) nor estimation of their positions. For this reason W-Grid can be applied to a wider number of scenarios than existing solutions, as it works both indoor and outdoor, and can be easily suitable to other kind of ad-hoc networks, such as mesh networks and wireless community networks. In this paper we describe how W-Grid is able to efficiently managing and querying data in wireless sensor networks and we report, by means of an extensive number of simulations, several performance measures of its efficiency in comparison with a well-know competitor solution in literature.

W R _grid: a scalable cross-layer infrastructure for routing, multi-dimensional data management and replication in wireless sensor networks

In this paper we propose a fully decentralized cross-layer infrastructure that creates self-organizing networks where wireless communications occur through multi-hop routing among sensors. The resulting sensor network is able to efficiently index and query multi-dimensional data without reliance either on Global Positioning System (GPS) or flooding/broadcasting operations. It does not suffer from the dead-end problem, which occurs in geographic-based approaches, and both routing and querying operations can scale to large/dense sensor networks. The efficiency and robustness of resulting sensor networks is controlled through a data replication strategy, which helps also in balancing the energy consumption among devices by distributing the workload among them.

Network Attack Detection Based on Peer-to-Peer Clustering of SNMP Data

Network intrusion detection is a key security issue that can be tackled by means of different approaches. This paper describes a novel methodology for network attack detection based on the use of data mining techniques to process traffic information collected by a monitoring station from a set of hosts using the Simple Network Management Protocol (SNMP). The proposed approach, adopting unsupervised clustering techniques, allows to effectively distinguish normal traffic behavior from malicious network activity and to determine with very good accuracy what kind of attack is being perpetrated. Several monitoring stations are then interconnected according to any peer-to-peer network in order to share the knowledge base acquired with the proposed methodology, thus increasing the detection capabilities. An experimental test-bed has been implemented, which reproduces the case of a real web server under several attack techniques. Results of the experiments show the effectiveness of the proposed solution, with no detection failures of true attacks and very low false-positive rates (i.e. false alarms).

Self-organization and Local Learning Methods for Improving the Applicability and Efficiency of Data-Centric Sensor Networks

In data-centric sensor networks each device is like a minimal computer with cpu and memory able to sense, manage and transmit data performing in-network processing by means of insertions, querying and multi-hop routings. Saving energy is one of the most important goals, therefore radio transmissions, which are the most expensive operations, should be limited by optimizing the number of routings. Moreover the network traffic should be balanced among nodes in order to avoid premature discharge of some devices and then network partitions. In this paper we present a fully decentralized infrastructure able to self-organize fully functional data centric sensor networks from local interactions and learning among devices. Differently from existing solutions, our proposal does not require complex devices that need global information or external help from systems, such as the Global Positioning System (GPS), which works only outdoor with a precision and an efficacy both limited by weather conditions and obstacles. Our solution can be applied to a wider number of scenarios, including mesh networks and wireless community networks. The local learning occurs by exploiting implicit cost-free overhearing at sensors. The work reports an extensive number of comparative experiments, using several distributions of sensors and data, with a well-know competitor solution in literature, showing that an approach fully based on self-organization is more efficient than traditional solutions depending on GPS.

Self-Organizing Mobile Mesh Networks with Peer-to-Peer Routing and Information Search Services

The work presents a decentralized protocol that allows self-organization of autonomous wireless devices in mobile mesh networks. In the resulting infrastructure the routing and information services are provided in a peer-to- peer fashion. Both services are performed through multi- hop radio transmissions among participant nodes with no fixed infrastructure required and avoiding to use broadcast of messages. Since links among nodes may be continuously destroyed and created, as a consequence of nodes movement and/or variations in connectivity range (e.g. due to temporary obstacles), our protocol is based on virtual network addresses which are dynamically assigned to groups of nodes (called islands). Routing requests are performed through unicast transmissions when traveling among islands and exploiting wireless overhearing when involving members of the same island (in- island routing). We will demonstrate that assigning virtual coordinates to groups of nodes guarantees network flexibility with a reasonable amount of overheard and that the amount of radio transmissions required is scalable with the number of nodes. Simulations have shown that the proposed solution can generate reliable networks, despite the unpredictable topology, assuming the use of devices with common radio range connectivity moving at pedestrian velocity.