Matteo Danieletto

Autonomous discovery, localization and recognition of smart objects through WSN and image features

This paper presents a framework that enables the interaction of robotic systems and wireless sensor network technologies for discovering, localizing and recognizing a number of smart objects (SO) placed in an unknown environment. Starting with no a priori knowledge of the environment, the robot will progressively build a virtual reconstruction of the surroundings in three phases: first, it discovers the SOs located in the area by using radio communication; second, it performs a rough localization of the SOs by using a range-only SLAM algorithm based on the RSSI-range measurements; third, it refines the SOs localization by comparing the descriptors extracted from the images acquired by the onboard camera with those transmitted by the motes attached to the SOs. Experimental results show how the combined use of the RSSI data and of the image features allows to discover and localize the SOs located in the environment with a good accuracy.

Anti-spoofing and open GNSS signal authentication with signal authentication sequences

Global Navigation Satellite System (GNSS) signal authentication is a requirement for a number of applications. GNSS authentication has been proposed with aiding techniques that can be applied to the existing GPS and as a new security function for future GNSS. The paper proposes a concept of a new authentication scheme based on signal authentication sequences that can be integrated in GNSS. The method works on systems that provide an open and encrypted service on the same frequency. The scheme would require minimum impact to the system. The architecture is explained in the different components of ground, space and user segment. A simulation of the architecture has been implemented in Matlab and performances and test results are shown. The paper concludes with suggestions of optimal parameters for an hypothetical implementation, explaining the future research steps.

CARMEN: a cognitive networking testbed on android OS devices

Wireless devices running the Android operating system offer a novel opportunity to study network behaviors, and to observe and modify, in real time, key networking parameters. This opens up an unprecedented opportunity to study, test, and evaluate the performance of techniques operating at different layers of the protocol stack and adopting the cognitive networking paradigm. In this article, we describe our novel IEEE 802.11 mesh network testbed that integrates Android-based devices. The aim is to build a flexible testbed to observe in-stack and out-of-stack parameters of interest that, with some modifications, can be used to test networking techniques in both civilian and emergency scenarios. We provide the implementation details to create an ad hoc network among these inexpensive commercial devices, and specify how to observe and modify the networking parameters at different layers of the protocol stack. We also run some standard protocols on the network, and we obtain some repeatable results, which are similar to other results from the literature and confirm our testbed as a valid tool for ad hoc network performance evaluation.

Discovery, localization and recognition of smart objects by a mobile robot

This paper presents a robotic system that exploits Wireless Sensor Network (WSN) technologies for implementing an ambient intelligence scenario. We address the problems of robot object discovery, localization, and recognition in a fully distributed way. We propose to embed some memory, some computational power, and some communication capability in the objects, by attaching a WSN mote to each object.We called the union of an object and of a mote, a smart object. The robot does not have any information on the number nor on the kind of objects in the environment. The robot discovers the objects through the radio frequency communication provided by the WSN motes. The robot roughly locates the motes by performing a range-only SLAM algorithm based on the RSSI-range measurements. A more precise localization and recognition step is performed by processing images acquired by the camera installed on the robot and matching the descriptors extracted from these images with those transmitted by the motes. Experiments with eight smart objects in a cluttered office environment with many dummy objects are reported. The robot was able to correctly locate the motes, to navigate toward them and to correctly recognize the smart objects.

On the Exploitation of the Android OS for the Design of a Wireless Mesh Network Testbed

Wireless devices running the Android operating system offer a novel opportunity to study network behaviors and to observe and modify in real time key networking parameters. This opens up an unprecedented opportunity to study, test and evaluate the performance of techniques operating at different layers of the protocol stack and adopting the cognitive networking paradigm. In this paper, we describe our novel IEEE 802.11 mesh network testbed that integrates Android based devices. The aim is to build a flexible testbed to observe in-stack and out-stack parameters of interest, that can be used to test many networking techniques in both civilian and tactical and hostile scenarios. We provide the implementation details to create an ad hoc network among these inexpensive commercial devices, and specify how to observe and modify the networking parameters at different layers of the protocol stack. Through some examples we show the stability of the network and discuss the time evolution of some parameters of interest.

RAZOR: A Compression and Classification Solution for the Internet of Things

The Internet of Things is expected to increase the amount of data produced and exchanged in the network, due to the huge number of smart objects that will interact with one another. The related information management and transmission costs are increasing and becoming an almost unbearable burden, due to the unprecedented number of data sources and the intrinsic vastness and variety of the datasets. In this paper, we propose RAZOR, a novel lightweight algorithm for data compression and classification, which is expected to alleviate both aspects by leveraging the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. In particular, RAZOR leverages the concept of motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way, it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion within acceptable bounds and allowing for simple lightweight distributed classification. In addition, RAZOR is designed to keep the computational complexity low, in order to allow its implementation in the most constrained devices. The paper provides results about the algorithm configuration and a performance comparison against state-of-the-art signal processing techniques.

Autonomous robot exploration in smart environments exploiting wireless sensors and visual features

This paper presents a complete solution for the integration of robots and wireless sensor networks in an ambient intelligence scenario. The basic idea consists in shifting from the paradigm of a very skilled robot interacting with standard objects to a simpler robot able to communicate with smart objects, i.e., objects capable of interacting among themselves and with the robots. A smart object is a standard item equipped with a wireless sensor node (or mote) that provides sensing, communication, and computational capabilities. The mote’s memory is preloaded with object information, as name, size, and visual descriptors of the object. In this paper, we will show how the orthogonal advantages of wireless sensor network technology and of mobile robots can be synergically combined in our approach. We detail the design and the implementation of the interaction of the robot with the smart objects in the environment. Our approach encompasses three main phases: (a) discovery, the robot discovers the smart objects in the area by using wireless communication; (b) mapping, the robot moving in the environment roughly maps the objects in space using wireless communication; (c) recognition, the robot recognizes and precisely locates the smart object of interest by requiring the object to transmit its visual appearance. Hence, the robot matches this appearance with its visual perception and reach the object for fine-grain interaction. Experimental validation for each of the three phases in a real environment is presented.

Improving Internet of Things communications through compression and classification

The amount of data produced and exchanged in the Internet of Things is continuously increasing. The associated management costs for information transmission and classification are becoming an almost unbearable burden due to the unprecedented number of data sources and the intrinsic vastness of the dataset. In this paper, we propose a novel lightweight approach capable of alleviating both aspects by leveraging on the advantages offered by classification methods to optimize communications and by enhancing information transmission to simplify data classification. In particular, we propose to adopt Motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion rate within acceptable bounds and allowing for simple lightweight distributed classification and anomaly detection techniques. We elaborate about data representation and motif extraction methods for constrained devices, proposing a simple and effective solution for the problem. We validate our approach with an extensive simulation campaign thoroughly spanning the system parameter set. This work paves the road ahead for the realization of a universal signal processor for constrained devices in the Internet of Things, which will be capable of appropriately handling any given data while at the same time increasing communication efficiency.

Estimating the Number of Receiving Nodes in 802.11 Networks via Machine Learning Techniques

Nowadays, most mobile devices are equipped with multiple wireless interfaces, causing an emerging research interest in device to device (D2D) communication: the idea behind the D2D paradigm is to exploit the proper interface to directly communicate with another user, without traversing any network infrastructure. A first issue related to this paradigm is the need for a coordinator, called controller, able to decide when activating a D2D connection is appropriate and to manage such connection. In this view, the paradigm of Software Defined Networking (SDN) can be exploited both to handle the data flows among the devices and to interact directly with every device. This work is focused on a scenario where a device is selected by the SDN controller, in order to become the master node of a WiFi-Direct network. The remaining nodes, called clients, can exchange data with other nodes through the master. The objective is to infer, through different Machine Learning approaches, the number of nodes actively involved in receiving data, exploiting only the information available at the client side and without modifying any standard communication protocol. The information about the number of client nodes is crucial when, e.g., a user desires a precise prediction of the transmission estimated time of arrival (ETA) while downloading a file.