With respect to other routing paradigms, source routing has received comparatively less attention in the underwater acoustic networking domain. The most likely causes of this lack of momentum are the high overhead caused by route discovery and maintenance in typical implementations of the source routing paradigm (e.g., dynamic source routing) in terrestrial radio networks. In this paper, we revert this view and argue that source routing can in fact be a reliable and convenient routing paradigm in underwater networks, when properly implemented and tailored to the peculiarities of underwater acoustic channels. Our scheme, named SUN, successfully recasts the source routing approach by introducing a number of new features, which improve the routing performance especially in the presence of unstable network links and mobile nodes. SUN is scenario-independent by design: this means that it can work in any connected topology, and does not need any side information (such as the node location and depth, or the channel state) in order to operate correctly. We evaluate the performance of SUN by means of simulations using the DESERT Underwater framework. Our results show that SUN correctly manages routing in both static and mobile networks, and that in some scenarios it even achieves better performance than a competing flooding-based approach. We also test the performance of SUN in a thorough experimental campaign involving six nodes and carried out in a lake near Berlin. From these results, we conclude that SUN, and the source routing paradigm in general, are in fact feasible options for general-purpose routing in underwater acoustic networks.

Simulation and experimentation of underwater networks entail many challenges, which for the former are mainly related to the accurate modeling of the channel behavior, while they are typically logistic in nature for the latter. In this article, we present our experience with WOSS and DESERT Underwater, two open source suites address both classes of challenges. The suites build on and extend the capabilities of ns2 and NS-MIRACLE, two widely known software packages for network simulation. WOSS endows NS-MIRACLE with the capability to generate realistic channel patterns by automatically retrieving and processing the environmental boundary conditions that influence such patterns; DESERT Underwater makes it possible to evolve toward at-sea experiments by reusing the same code written for simulations, thereby minimizing the effort required for network deployment and control. Both suites have been widely tested and used in several projects: some examples are provided in this respect, including an account of some experiments carried out in collaboration with the NATO STO Centre for Maritime Research and Experimentation.

DESERT Underwater (short for DEsign, Simulate, Emulate and Realize Test-beds for Underwater network protocols) is a complete set of public C/C++ libraries to support the design and implementation of underwater network protocols. Its creation stems from the will to push the studies on underwater networking beyond simulations. Implementing research solutions on actual devices, in fact, is of key importance to realize a communication and networking architecture that allows heterogeneous nodes to communicate reliably in the underwater environment. In this paper, we first discuss the rationale behind this work, and, then we list and briefly describe all the DESERT Underwater libraries currently implemented. In line with the current trends in underwater networking, our approach makes it possible to reuse the same code prepared for simulations in order to realize underwater network prototypes. We also present some preliminary tests that confirm the feasibility of the proposed solution for the design and evaluation of underwater network protocols. In this perspective, we believe that DESERT Underwater is a useful tool to profitably develop and test real world applications.

In this paper, we consider underwater network prototyping using the network simulation engine NS-Miracle, and investigate different embedded computer boards that can be employed for this task. In particular, we consider two embedded platforms with considerably different capabilities: the PandaBoard (a powerful platform that does not require any cross-compilation effort) and version 5.2 of the NetDCU board, which is much more constrained in terms of computational power, RAM and storage space. After describing the steps required to install NS-Miracle and the DESERT Underwater libraries on board these platforms, we report on the field experiments conducted to test the corresponding prototypes. Our results include a comparison between the two investigated platforms in terms of resources required (e.g., memory occupancy and energy expenditure) and performance in the execution of real-time software (e.g., delays introduced within the simulation framework). We believe that our work represents an interesting step towards the realization of underwater network prototypes made of heterogeneous nodes.

The interest for underwater network applications is increasing in both academia and industry, often translating into joint efforts to implement research solutions on actual devices. Not only does this activity help researchers validate theoretic and simulation results via experimentation: it may also guide the design of reliable prototypes and, eventually, of commercial products, by pinpointing practical issues that cannot be easily observed in simulations. Recently, [1], [2] and [3] propose the idea of reusing the same software already written by researchers for simulation purposes in real world experiments. In particular, the well known network simulator ns2 [4] and its extension NS-Miracle [5] are the basis upon which both the SUNSET framework [6] and the DESERT Underwater libraries [7] have been developed. These two tools are now freely available to the research community to move from network simulations to actual prototypes. However, experimenting in the real world implies technical and logistic issues typically ignored in network simulations. First of all, ns2/NS-Miracle simulations are performed by means of a single script that, once fed with the desired input parameters, creates and controls all network nodes in a centralized fashion; in network prototypes involving SUNSET or DESERT, instead, the nodes are controlled by different scripts, one per node. To perform an experiment, such scripts must be started almost simultaneously on all nodes. Furthermore, depending on the experiment, it may be necessary to run different scripts several times (or the same script with different input parameters) in a given order. If a cabled or remote connection to all nodes is available, this can be easily achieved. Unfortunately, it is often impossible to access all the nodes of an underwater network after its deployment, especially in networks of large size. In fact, for logistic reasons, it may be necessary to have such nodes as buoys or Autonomous Underwater Vehicles (AUVs) run on battery power, and to leave them in the water during the sea trials (see Figs. 1 and 2). Hence, alternative means of remote control must be employed. In this work, we demonstrate a solution to this issue, that can be employed when direct access to at least one node is available throughout the experimental campaign. In detail, we implemented a Master/Slave mechanism for distributing experiment management commands. Our solution is based on two entities: a Master process (which continuously runs on the accessible node, referred to as the Master node hereon, see Fig. 3) and a Slave process (running on board all the remaining nodes). Through the Master, a human operator can: 1) check the network connectivity; 2) set a route for control packets (this control route must mandatorily end on the Master node, and can include either all other nodes or only part of them);

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples (sub-sampling) at the Data Collection Point (DCP). To this end, we propose a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is very general as it does not require ad-hoc parameter tuning by the user and is able to self-adapt to unpredictable changes in the signal statistics. A feedback control loop is accounted for to estimate, in an on-line fashion, the signal reconstruction error and to react accordingly in order to keep such error bounded. For the actual recovery of the sub-sampled signal, our framework accommodates diverse interpolation techniques, by framing them into the same general algorithm. As a further original contribution of this paper, we integrate a novel signal interpolation method based on Compressive Sensing (CS) into our framework. Specifically, this technique exploits Principal Component Analysis (PCA) for on-line learning of the signal statistics and CS for recovering the sub-sampled signal through convex optimization. Also, we perform an extensive validation of the proposed framework when used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. Besides validating our framework, these results have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs. Finally, we note that although SCoRe1 is proposed for WSNs, it can be readily applied to other types of network infrastructures that require the approximation of large and distributed signals datasets showing spatial and/or temporal correlation.

In this paper we present a performance evaluation and feasibility test of SUN, a routing protocol for underwater networks inspired to Dynamic Source Routing (DSR), to which it adds several features that improve its behavior in underwater environments. The evaluation has been performed with real devices, and has been made possible through a collaboration between the Department of Information Engineering (DEI) of the University of Padova, Italy and EvoLogics GmbH, Germany. In detail, the idea put in practice in this work is to command real hardware, i.e., the S2C acoustic modems of EvoLogics, by means of the ns2/NS-Miracle engine developed and extensively used primarily by research institutions. This approach favors code reuse and speeds up the realization of flexible and easily modifiable network prototypes. Our results show that SUN can deal with typical network issues such as the disconnection of a node and the appearance of additional nodes, and that it copes well with dynamic topology changes.

My research activity focused on the field of heterogeneous wireless networks and has been particularly inspired by the problem of sensing a city-wide environment through a large scale, partially distributed, mobile and low cost network (possibly composed of mobile phones or similar user’s equipment). In my PhD thesis I have been guided by the grand vision of a two tier architecture which integrates existing cellular systems with different types of distributed networks (these could be mixtures of ad hoc, sensor networks and soon). In fact, a fully distributed infrastructure alone would be inappropriate when the network is very large in size and highly populated (e.g., urban area networks). In such a case, the network organization itself would be energy draining and probably impractical. On the other hand, a cellular system alone does not have the flexibility and the instruments to get a fine grained view of all the data generated within such a network. This envisioned scenario, besides featuring a number of mobile phones, also consists of a mixture of embedded devices, which are expected to have on-board radio and sensing capabilities. Nowadays technology makes us more and more able to control the environment we are in through motion sensors, GPS, health care devices, microphones and video-cameras. Wireless Sensor Networks (WSNs), for instance, are infrastructures made of small devices (nodes) equipped with “intelligent sensors” able to sense their surroundings for, e.g., light, temperature, humidity and/or pollution. Therefore, mobile phones as well as other network elements, including base stations, routers and access points hosting diverse wireless and wired technologies, can cooperate to accomplish a common task like the detection of a fire or the monitoring of a physical phenomenon. Exploiting the fact that cell phones are becoming a communication hub in our daily life, we can foresee the integration of standard cellular systems with overlayed distributed networks such as WSNs. The ultimate goal of this is to “connect” everything has some communication capability, possibly providing self-configurability and self-adaptability of the network. We note that current cellular networks already implement some of these features: user positions, to a certain extent, can be tracked already and services can be provided based on contextual information. As a matter of fact, we are depicting a Delay Tolerant Network (DTN) scenario, where heterogeneous, sparse and/or mobile wireless networks communicate with each other, but where, due to the inherent nature of the infrastructure itself, no continuous connectivity can be assumed. The above grand vision entails quite a few challenges, and during my research activity I have been focusing on the following ones: 1) the design of reconstruction algorithms that from a subset of the data (i.e., from the collection of the sensor readings from a small fraction of nodes) are able to reconstruct with high accuracy the data monitored over the entire sensor field (these algorithms allow for scalability of the system as they decrease the number of data packets to collect for a given accuracy goal); 2) the design of cooperative networking protocols, where cooperation is utilized to reach a common goal such as the detection of a fire or/and to increase the network performance in terms of optimization of given performance metrics, e.g., energy consumption, delivery time, delivery probability. Concerning the first point, my study explores the capabilities of Compressive Sensing (CS), a technique that has been proved to be very effective for the compression and recovery of correlated signals, with the objective of designing and implementing a system for the efficient acquisition of large data sets in distributed (sensor) networks. The goal of this system is to reconstruct large signals through the collection of the smallest number of samples that will keep the reconstruction quality above a minimum target level. The steps of my research activity can be summarized as follows: 1.a) assess the applicability and potential benefits of CS in networking applications; 1.b) provide a sound theoretical justification of the effectiveness of CS recovery when coupled with Principal Component Analysis (PCA) along with a characterization of the optimality of the reconstruction process as a function of the statistics of the input signal; 1.c) design an algorithm for signal reconstruction based on CS and validating the proposed method through Matlab simulations as well as real signal traces. For the second point, my work has been centered around distributed optimization methods whose objective is that of optimizing network wide (global) performance metrics. In detail, in the investigated scenario nodes collaborate to minimize the sum of local objective functions, which in general depend on global variables such as the network protocol parameters or actions taken by all the nodes in the network. In the case where the local objective functions are convex, it is possible to adopt a framework that relies on local subgradient methods and consensus algorithms to average the information from each node, while granting convergence towards global optimal solutions. However, existing convergence results for this framework can only be applied in the case of synchronous operations of the nodes and mobility models without memory. My research addresses and solves these issues, and its fundamental steps were: 2.a) the extension of the convergence results to the optimal solution for a more general class of mobility models; 2.b) the application of distributed sub-gradient methods under asynchronous operations; 2.c) the presentation of a possible networking scenario to validate the analysis, showing the effectiveness of the considered distributed optimization technique. The outcomes of my research are useful tools for the optimization of practical network protocols and provide recommendations for the design of the integrated communication and sensing system that we have envisioned above.

The NAUTILUS (Network Architecture and protocols for Underwater Telerobotics via acoustIc Links in Ubiquitous Sensing, monitoring and explorations) project aims at providing a comprehensive study of the technical issues related to the realization of a complete solution for the network architecture and the communications protocols needed for the tele-operation of underwater robots. When pursuing this goal, the need to implement realistic scenarios for underwater simulations clearly emerges. In this paper, starting from the investigation on the state-of-the-art carried out for the NAUTILUS project, we list the main concepts and parameters that underlie realistic simulations of underwater scenarios. Also, we present and thoroughly discuss the choices made in terms of parameters, network architectures and models for the NAUTILUS project itself. We believe that the information collected in this paper provides a good starting point for the development of a realistic underwater performance evaluation tool.

In this paper we apply distributed sub-gradient methods to optimize global performance in Delay Tolerant Networks (DTNs). These methods rely on simple local node operations and consensus algorithms to average neighbours' information. Existing results for convergence to optimal solutions can only be applied to DTNs in the case of synchronous operation of the nodes and memory-less random meeting processes. In this paper we address both these issues. First, we prove convergence to the optimal solution for a more general class of mobility models. Second, we show that, under a synchronous operations, a direct application of the original sub-gradient method would lead to suboptimal solutions and we propose some adjustments to solve this problem. Further, at the end of the paper, we illustrate a possible DTN application to demonstrate the validity of this optimization approach.

In this paper, we propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real Wireless Sensor Network (WSN) scenarios. We advocate the joint use of CS for the recovery and of Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. The statistical characteristics of the signals are thus exploited to design the sparsification matrix required by CS recovery. In this paper, we represent this framework through a Bayesian Network (BN) and we use Bayesian analysis to infer and approximate the statistical distribution of the principal components. We show that the Laplacian distribution provides an accurate representation of the statistics of the data measured from real WSN testbeds. Therefore, the joint use of CS and PCA for data recovery in real WSNs is legitimate, and is equivalent to Maximum A Posteriori (MAP) recovery.

The main contribution of this paper is the implementation and experimental evaluation of a signal reconstruction framework for Wireless Sensor Networks (WSNs). We design WSN-Control, an architecture to control a WSN from an external server connected to the Internet. Within such architecture, we implement a compression and recovery technique that combines Principal Component Analysis (PCA) and Compressive Sensing (CS) to reconstruct signals with many components from a sensor field through the collection of a relatively small number of samples, i.e., through incomplete representations of the actual signal. Overall, our experimental results show that a careful use of CS recovery is effective and can lead to a fully automated system for data gathering and reconstruction of real world and non-stationary signals in WSNs. In detail, WSN-Control effectively recovers signals showing some temporal and/or spatial correlation, from a relatively small number of samples, even below 20%, keeping the relative reconstruction error smaller than 5 · 10-3. Signals with more irregular and quickly varying statistics are also recovered, even though the reconstruction error becomes highly dependent on the number of collected samples. CS minimization is obtained through the recently proposed NESTA optimization algorithm. Our implementation of CS recovery is available.

Network coding is a technique which is particularly suitable for the dissemination of data in distributed ad hoc networks. The definition of a mathematical model that describes the interactions among nodes and, in particular, their relationship in terms of buffer subspaces is still an open and challenging problem. The contribution of this paper is an analysis of the relationship between the network topology and the subspace overlap among nodes. This analysis can be used to establish criteria for the design of packet combination policies in diverse networking scenarios. Differently from previous studies, we will explicitly take the overlap among subspaces into account through a framework comprising networks with fixed as well as mobile nodes.

In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using compressive sensing (CS) in conjunction with principal component analysis (PCA). Our scheme compresses in a distributed way real world non-stationary signals, recovering them at the data collection point through the online estimation of their spatial/temporal correlation structures. The proposed technique is hereby characterized under the framework of Bayesian estimation, showing under which assumptions it is equivalent to optimal maximum a posteriori (MAP) recovery. As the main contribution of this paper, we proceed with the analysis of data collected by our indoor wireless sensor network (WSN) testbed, proving that these assumptions hold with good accuracy in the considered real world scenarios. This provides empirical evidence of the effectiveness of our approach and proves that CS is a legitimate tool for the recovery of real-world signals in WSNs.

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data.We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs.