Orthogonal time frequency space (OTFS) modulation has been proposed recently as a new waveform in the context of doubly-selective multi-path channels. This article proposes a novel pilot design that improves OTFS's spectral efficiency (SE) while reducing its peak-to-average power ratio (PAPR). Instead of adopting an embedded data-orthogonal pilot for channel estimation, our scheme relies on Chu sequences superimposed to data symbols. We optimize the construction by investigating the best energy split between pilot and data symbols. Two equalizers, and an iterative channel estimation and equalization procedure are considered. We present extensive numerical results of relevant performance metrics, including the normalized mean squared error of the estimator, bit error rate, PAPR and SE. Our results show that, while the embedded pilot scheme estimates the channel more accurately, our approach yields a better tradeoff by achieving much higher spectral efficiency and lower PAPR.

Secure and private communications can be made possible by operating at different levels of the communication stack. One possibility is to work directly at the physical layer, with physical layer security (PLS) techniques. In this work, we propose a PLS-based approach towards secure and private communications, in particular by exploiting reconfigurable intelligent surfaces (RISs). By properly tuning the configuration of the RISs it is possible to enable communication with a set of intended receivers while making the signal unintelligible in nearby areas. This work shows the effectiveness of the approach, while also highlighting its limitations and the challenges that need to be tackled for realizing it in practice.

To protect underwater acoustic communications from interception, the exchange of encryption keys is necessary. Since underwater devices can be compromised, generating keys on site is a better option than predefined keys. In this paper, we present a solution that utilizes the characteristics of the underwater acoustic channel impulse response (CIR), which is highly variable in both space and time, while ensuring consistency between the communicating nodes (Alice and Bob). To compensate for temporal variations, the key is calculated from the CIR feature’s distribution parameters, while the hard key is determined using a K-means strategy. To achieve key agreement, we exploit the long propagation delay in the underwater CIR and let Alice and Bob transmit simultaneously while their packets fly past each other. Due to the channel’s reciprocity, this simultaneous transmission ensures that the same CIR is estimated at both ends of the communication link. Simulation and sea experiment results show that it is possible to extract at least three times as many secret bits as we would with a uniform quantizer. The results show a high matching rate between Alice and Bob and a high Hamming distance to Eve’s key. For reproducibility, we share the CIRs from the sea trials.

The quasi-optical propagation of millimeter-wave (mmWave) signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most algorithms require information on the indoor environment, may entail the collection of large training datasets, or bear an infeasible computational burden for commercial off-the-shelf (COTS) devices. In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment. To relieve training data collection efforts, we resort to an algorithm-supervised approach by bootstrapping the training of our neural network through location estimates obtained from a state-of-the-art localization algorithm. We evaluate our scheme via mmWave measurements from indoor 60-GHz double-directional channel sounding. We process the measurements to yield dominant multipath components, use the corresponding angles to compute ADoA values, and finally obtain location fixes. Results show that the tiny NN achieves sub-meter errors in 74% of the cases, thus performing as good as or even better than the state-of-the-art algorithm, with significantly lower computational complexity.

Networked Music Performances (NMPs), where geographically displaced musicians play together over a data network, represent a challenging application for today’s wireless communications. This is due to the stringent constraints on latency, throughput, and reliability that need to be obeyed in order to achieve a satisfactory quality of experience for the musicians. Slicing is a promising feature of 5G networks in the context of NMP applications, as it makes it possible to isolate the networking and computing resources allocated to NMP devices. However, the use of slicing has been scarcely investigated for the NMP context so far. Moreover, previous works focusing on NMPs over 5G involved up to 4 nodes. To bridge these gaps, we study 5G performance in support of NMPs involving an architecture with 10 nodes, both with and without slicing. Specifically, we focused on the assessment of the sole wireless link, as the measurements can be easily transferred to a realistic NMP architecture involving a wide area network. Our results show that, in the slicing condition, latency slightly increased due to the realistically different computing specifications of the MEC servers compared to those of the Core Network servers, whereas reliability slightly improved as expected.

Underwater acoustic communications are becoming a popular solution for underwater data communications and telemetry, making the authentication of transmitted data a necessity. In this paper, we propose a physical-layer authentication strategy for underwater acoustic networks (UWANs) with mobile devices. Such a scenario is more challenging than classical authentication scenarios in static networks, because the mobility of the receiver and/or transmitter implies that channel conditions slowly change over time. Thus, we cannot rely on the statistics of channel features to be stationary. In our proposed strategy, we assume that the receiver can rely on a set of sensors. We first extract a set of channel features, to be used to track the channel evolution over time. We then develop a long short-term memory (LSTM)-based approach, where at each step the sensors predict future feature values based on a learned model and on previously observed feature values. Next, each sensor computes the prediction error and passes it on to the actual receiver, which makes a decision on the signal authenticity through a generalized likelihood ratio test (GLRT). We model different classes of attacks and test them using simulation data obtained via the Bellhop ray tracing software. Numerical results show that our authentication mechanism successfully distinguishes between legitimate and impersonating transmitters, even when considering challenging attacking scenarios where the attacker can successfully mimic the channels between the legitimate transmitter and the sensors.

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

We describe a biomimicking interception scheme tailored to Underwater Acoustic Communications (UAC), which aims at separating authentic and biomimicking signals. Our interceptor leverages the expected stability of the biomimicking sources, as opposed to vocalizations by marine fauna, which are expected to move fast and rapidly change orientation. Consequently, the channel impulse response (CIR) of the link between a receiver and a biomimicking source is expected to be much more stable than those corresponding to actual vocalizations. We quantify this stability by testing the randomness of the representation of the CIRs. The latter are represented by two similarity metrics: the cross-correlation and the sample entropy between adjacent CIRs features. We offered two interception measures: 1) testing the similarity between a Gaussian distribution and the distribution of the similarity measures using the Kullback–Leibler divergence (KLD) criteria for quantification, and 2) the minimum number of clusters to effectively segment the similarity measures as a point cloud. Results from simulations for artificial signals mimicking dolphin whistles and the outcomes of a lake trial demonstrate the effectiveness of our biomimicking interceptor.

The availability of high-performance embedded audio systems, along with high-bandwidth and low-latency connectivity options provided by 5G networks, is enabling the Internet of Musical Things (IoMusT) paradigm. A central component of this paradigm is represented by Networked Music Performances (NMPs), where geographically displaced musicians play together over the network in real time. However, to date, IoMusT deployments over 5G networks remain scarce, and very limited statistical results are available on the actual latency and reliability of 5G networks for IoMusT and NMP scenarios. In this paper, we present a private 5G IoMusT deployment and analyze its performance when supporting NMPs. Our IoMusT system is composed of up to four nodes and includes different background traffic conditions. We focused on the assessment of the sole wireless link, as the measurements can be easily transferred to a realistic NMP architecture involving a WAN by compounding them with those of the WAN. Our results show that latency increases with the number of nodes and with the presence of background traffic, whereas the reliability did not vary with the complexity of the conditions. For all tested scenarios, the average measured latency was below 24 ms (including a jitter buffer of 10.66 ms), whereas packet losses occurred with a probability of less than 0.01. However, irregular spikes were found for all latency and reliability metrics, which can significantly reduce the quality of service perceived by the users of NMP applications. Finally, packet loss and latency resulted to be uncorrelated, which suggests that they have different root causes.

Considering the specific quality-of-service requirements of Internet of Musical Things (IoMusT) deployments, 5G networks are deemed as a key enabling technology to support IoMusT applications as needed, e.g., in networked music performance (NMP) settings. Slicing, in particular, is a promising feature of 5G networks, as it could help dedicate specific resources to NMP traffic, so as to schedule NMP communications with higher priority and thus guarantee swift delivery delays and higher reliability. Moreover, handling NMP traffic via a slice residing on a geographically closer multi-access edge computing (MEC) server would help significantly reduce WAN transit times for NMP traffic. In this paper, we conduct the first tests with slicing in 5G networks supporting IoMusT applications (to the best of our knowledge). We conduct our tests by decoupling WAN transit times from the radio access performance (evaluated in terms of latency, error rate, number of lost packets, and maximum length of a packet loss burst). Our results show that slicing, even when introducing slight extra delays due to UPF implementation or hardware specifications, is a potentially excellent choice to save on WAN transit times for NMP traffic while delivering the same reliability as the operator’s core network equipment.

We propose to employ multiple tiny neural network (NN) models to localize a mobile client in large or complex indoor environments. These models are trained in a self-supervised fashion by exploiting the location labels obtained from a geometry-based bootstrapping localization algorithm, thus relieving the burden of training data collection. We further propose a scheme to switch to the right NN model in order to keep localizing a mobile client accurately as it moves through the indoor space.

With the growing use of underwater acoustic communications and the recent adoption of standards in this field, it is becoming increasingly important to secure messages against eavesdroppers. In this paper, we focus on a physical-layer security solution to generate sequences of random bits (keys) between two devices (Alice and Bob) belonging to an underwater acoustic network (UWAN); the key must remain secret to a passive eavesdropper (Eve) not belonging to the UWAN. Our method is based on measuring the propagation delay of the underwater acoustic channel over multiple hops of the UWAN: this harvests the randomness in the UWAN topology and turns the slow sound propagation in water into an advantage against eavesdropping. Our key generation protocol includes a route discovery handshake, whereby all UWAN devices at intermediate hops accumulate their message processing delays. This enables Alice and Bob to compute the actual propagation delays along each route and to map such information to a sequence of bits. Finally, from these bit sequences, Alice and Bob obtain a secret key. We analyze the performance of the protocol theoretically and assess it via extensive simulations and field experiments.

Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches to angle-based localization typically require to know the map of the environment and the location of the access points. Thus, several works have resorted to automated learning in order to infer a device’s location from the properties of received mmWave signals. However, collecting training data for such models is a significant burden. In this work, we propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. Therefore, it is amenable for implementation in resource-constrained hardware, and needs fewer training samples to converge. We also propose to relieve training data collection efforts by retrieving (inherently imperfect) location estimates from geometry-based mmWave localization algorithms. Even in this case, our results show that the proposed neural networks perform as good as or better than state-of-the-art algorithms.

The advent of multi access edge computing~(MEC) will enable latency-critical applications such as cooperative adaptive cruise control (also known as platooning) to be hosted at the edge of the network. MEC-based platooning will leverage the coverage of the cellular infrastructure to enable inter-vehicular communications, potentially overcoming crucial problems of vehicular ad-hoc networks~(VANETs) such as non-trivial packet loss rates. However, MEC-based platooning will require the controller to be migrated to the most suitable positions at the network edge, in order to maintain low-latency connections as the platoon moves. In this paper, we propose a context-awareQ -Learning algorithm that carries out such migrations only as often as is necessary, and thereby reduces the additional delays implicit in application migration across MEC hosts. When compared to the state-of-the-art approach named FollowME, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, as well as a reduced statistical dispersion.

As a growing number of service and application providers choose cloud networks to deliver their services on a software-as-a-service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet service level agreements (SLAs). At the same time, they should guard against over-provisioning, which limits their capacity to accommodate more tenants. To this end, we propose Shortterm memory Q-Learning pRovisioning (SQLR, pronounced as “scaler”), a system employing a customized variant of the modelfree reinforcement learning algorithm. It can reuse contextual knowledge learned from one workload to optimize the number of virtual machines (resources) allocated to serve other workload patterns. With minimal overhead, SQLR achieves comparable results to systems where resources are unconstrained. Our experiments show that we can reduce the amount of provisioned resources by about 20% with less than 1% overall service unavailability (due to blocking), while delivering similar response times to those of an over-provisioned system.

Future millimeter-wave networks will support very high densities of devices and access points. This vastly increases the overhead required for access point selection and beam training. Fortunately, the quasi-optical properties of millimeter-wave channels make location-based network optimization a highly promising technique to reduce control overhead in such millimeter-wave WLANs. In this paper, we extract channel state information from off-the-shelf routers, we use it to design a high accuracy location system, and then show how location information enables the optimization of network operations. The resulting scheme, named LEAP, can predict blockage, optimize access point association, and select the most suitable antenna beam patterns while significantly reducing the beam training overhead. We show that compared to standard state-of-the-art 802.11ad systems, LEAP's location driven management greatly improves network performance and link stability.

Millimeter-wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. Thus, the communicating devices need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes an efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this paper, we discuss the above-mentioned challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at a lower frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

With the growing use of underwater acoustic communications (UWAC) for both industrial and military operations, there is a need to ensure communication security. A particular challenge is represented by underwater acoustic networks (UWANs), which are often left unattended over long periods of time. Currently, due to the physical and performance limitations, the UWAC packets rarely include encryption, leaving the UWAN exposed to external attacks faking legitimate messages. In this paper, we propose a new algorithm for message authentication in a UWAN setting. We begin by observing that, due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one. Taking this into account, our scheme relies on trusted nodes that independently help a sink node in the authentication process. For each incoming packet, the sink fuses beliefs evaluated by the trusted nodes to reach an authentication decision. These beliefs are based on the estimated statistics of the channel parameters, which are chosen to be the most sensitive to the transmitter-receiver displacement. Our simulation results show accurate identification of an attacker's packet. We also report results from a sea experiment demonstrating the effectiveness of our approach.

Millimeter-wave (mm-wave) location systems not only provide accurate positioning for location-based services but can also help optimize network operations, for example, through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mm-wave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference of arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mm-wave access points are known. Moreover, they are designed to be lightweight so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mm-wave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

The very large bandwidth available in the 60 GHz band allows, in principle, to design highly accurate positioning systems. Integrating such systems with standard protocols (e.g., IEEE 802.11ad) is crucial for the deployment of location-based services, but it is also challenging and limits the design choices. Another key problem is that consumer-grade 60 GHz hardware only provides coarse channel state information, and has highly irregular beam shapes due to its cost-efficient design. In this paper, we explore the location accuracy that can be achieved using such hardware, without modifying the 802.11ad standard. We consider a typical 802.11ad indoor network with multiple access points (APs). Each AP collects the coarse signal-to-noise ratio of the directional beacons that clients transmit periodically. Given the irregular beam shapes, the challenge is to relate each beacon to a set of transmission angles that allows to triangulate a user. We design a location system based on particle filters along with linear programming and Fourier analysis. We implement and evaluate our algorithm on commercial off-the-shelf 802.11ad hardware in an office scenario with mobile human blockage. Despite the strong limitations of the hardware, our system operates in real-time and achieves sub-meter accuracy in 70% of the cases.

Millimeter wave (mmWave) communications are an essential component of 5G-and-beyond ultra-dense Gbit/s wireless networks, but also pose significant challenges related to the communication environment. Especially beam-training and tracking, device association, and fast handovers for highly directional mmWave links may potentially incur a high overhead. At the same time, such mechanisms would benefit greatly from accurate knowledge about the environment and device locations that can be provided through simultaneous localization and mapping (SLAM) algorithms. In this paper we tackle the above issues by proposing CLAM, a distributed mmWave SLAM algorithm that works with no initial information about the network deployment or the environment, and achieves low computational complexity thanks to a fundamental reformulation of the angle-differences-of-arrival mm Wave anchor location estimation problem. All information required by CLAM is collected by a mmWave device thanks to beam training and tracking mechanisms inherent to mm Wave networks, at no additional overhead. Our results show that CLAM achieves submeter accuracy in the great majority of cases. These results are validated via an extensive experimental measurement campaign carried out with 60-GHz mmWave hardware.

While acoustic communications are still considered the most prominent technology to communicate under water, other technologies are being developed based, e.g., on optical and radio-frequency electromagnetic waves. Each technology has its own advantages and drawbacks: for example, acoustic signals achieve long communication ranges at order-of-kbit/s rates, whereas optical signals offer order-of-Mbit/s transmission rates, but only over short ranges. Such diversity can be leveraged by multimodal systems, which integrate different technologies and provide the intelligence required to decide which one should be used at any given time. In this paper, we address a fundamental part of this intelligence by proposing optimal multimodal routing (OMR), a novel routing protocol for underwater networks of multimodal nodes. OMR makes distributed decisions about the flow in each link and over each technology, at any given time, in order to advance a packet toward its destination; in doing so, it prevents bottlenecks and allocates resources fairly to different nodes. We analyze the performance of OMR via simulations and in a field experiment. The results show that OMR successfully leverages all technologies to deliver data, even in the presence of imperfect topology information. To permit the reproduction of our results, we share our simulation code.

Device localization is a highly important functionality for a range of applications. It is particularly beneficial in mmWave networks, where it can be used to reduce the beam training overhead and anticipate handovers between access points. In this paper, we present JADE, an algorithm that estimates the location of a mobile user in an indoor space without any knowledge about the surrounding environment (floor plan, location of walls and presence of reflective surfaces) or about the location and number of access points available therein. JADE leverages the beam procedure used in pre-standard and commercial mmWave equipment to estimate the angle-of-arrival of multipath components of the signal sent by visible access points. This information is then employed to localize the mobile user, estimate the position of access points and finally form a map of the environment. No radar-like ranging operations are required for this. Our results demonstrate that JADE can localize a user with sub-meter accuracy in the broad majority of the cases, and that it even outperforms localization algorithms that require full knowledge of the environment and access point positions.

We consider a multihop network where a source node must reliably deliver a set of data packets to a given destination node. To do so, the source applies a fountain code and floods the encoded packets through the network, until they reach their destination or are lost in the process. We model the probability that the destination can recover the original transmissions from the received coded packets as a function of the network topology and of the code redundancy, and show that our analytical results predict the outcome of simulations very well. These results are employed to design distributed forwarding policies that achieve a good tradeoff between the success probability and the total number of transmissions required to advance a packet toward the destination. We finally develop in detail the case where intermediate relays can inject additional redundancy in the network, provided that they have successfully decoded the source packets.

We present a spatial reuse resource allocation scheme for underwater acoustic networks that organizes communications so as to avoid destructive collisions. One prime source of collisions in underwater acoustic networks is the so called near-far effect, where a node located farther from the receiver is jammed by a closer node. While common practice considers such a situation a challenge, in this paper we consider it a resource, and use it to increase the network throughput of spatial-reuse time-division multiple access. Our algorithm serves two types of communications: (1) contention-free and (2) opportunistic. Our objective is to maximize the time slot allocation, while guaranteeing a minimum per-node packet transmission rate. The result is an increase in the number of contention-free packets received, and a decrease in the scheduling delay of opportunistic packets. Numerical results show that, at a slight cost in terms of fairness, our scheduling solutions achieve higher throughput and lower transmission delay than benchmark spatial-reuse scheduling protocols. These results are verified in a field experiment conducted in the Garda Lake, Italy, where we demonstrated our solution using off-the-shelf acoustic modems. To allow the reproducibility of our results, we publish the implementation of our proposed algorithm.

Wireless transmissions in water are mostly carried out via long-range (but low-rate) underwater acoustic communications, or short-range (but high-rate) underwater optical communications. In this paper, we are interested in finding out whether a statistical relationship exists between underwater acoustics and optics. Besides the theoretical interest of such a relationship, predicting the quality of the optical link through acoustics is also relevant in the context of a multimodal system with both acoustics and optics. Our study is based on a large data set acquired during the NATO ALOMEX 2015 expedition. During this experiment, we simultaneously measured several characteristics of the acoustic and optical links at multiple locations, reflecting a diversity of sea environments. Our results show a strong correlation between the properties of the acoustic link and the reliability of optical communications. This correlation makes it possible to predict the state of the underwater optical link at a certain depth and range. Due to the complexity of the acoustic and optical channels, we could not find the source of this correlation. This paper is, therefore, aimed to stimulate a theoretical study of the mutual properties of underwater acoustic and optical communication links. For reproducibility, we share the processed data from the experiment.

In this paper, we target single-anchor localization schemes for millimeter wave (MMW) systems. The schemes are designed to be lightweight, so that even computationally-constrained devices can support them. We identify the main propagation properties of MMW signals that have an impact on localization and design three algorithms that exploit these, namely a triangulation-validation procedure, an angle difference-of-arrival approach, and a scheme based on location fingerprinting. We evaluate the algorithms by means of simulations, and draw conclusions on their robustness. We then validate our results via measurements involving commercial pre- standard 60- GHz MMW hardware. Our experiments confirm that, by relying only on a single anchor and without requiring complex signal processing at the receiver, the algorithms can localize a node with high probability, and in many cases with sub-meter accuracy. We conclude by discussing how these algorithms complement each other in terms of robustness and localization success probability.

This paper presents ALBA-R, a protocol for convergecasting in wireless sensor networks. ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The protocol is localized and distributed, and adapts efficiently to varying traffic and node deployments. Through extensive ns2-based simulations, we show that ALBA-R significantly outperforms other convergecasting protocols and solutions for dealing with connectivity holes, especially in critical traffic conditions and low-density networks. The performance of ALBA-R is also evaluated through experiments in an outdoor testbed of TinyOS motes. Our results show that ALBA-R is an energy-efficient protocol that achieves remarkable performance in terms of packet delivery ratio and end-to-end latency in different scenarios, thus being suitable for real network deployments.

In this paper, we introduce Underwater Selective Repeat (USR), a Selective Repeat Automatic Repeat reQuest (SR-ARQ) mechanism for multiuser underwater acoustic networks. Our scheme exploits the typically large round-trip time (RTT) of underwater acoustic links to interlace the transmission of data and acknowledgment (ACK) packets, such that the transmitter never starts sending data packets when it should receive ACKs. No specific synchronization mechanism is required to do so. It is shown that the timing of point-to-point communications can be adjusted to optimize the performance of multiuser networks of a given size. Moreover, it is shown that the proposed strategy can be made robust to mobility, hence to time-varying RTTs. We provide detailed simulation results that assess the performance of USR as a function of the protocol parameters, both in static and in mobile networks. Based on these results, we propose an adaptive version of USR, whereby a node can modify its behavior (e.g., it can pack data transmissions more tightly or more loosely within one RTT) by reacting to packet errors induced by multiple-access interference.

We propose a theoretical framework to evaluate the expected throughput of underwater networks over an ensemble of node topologies and propagation environments. The analysis is based on the assumptions that the transmitters are spatially distributed according to a Poisson point process, and that the channel follows a Rayleigh fading distribution, with a mean that is determined by spreading loss and frequency-dependent absorption. We evaluate the probability of a successful transmission, i.e., the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a given threshold, and determine the maximum network throughput density over the transmitter density and the operating frequency. The theoretical results are validated using a realistic underwater channel simulator based on ray tracing. It is demonstrated that, for a number of practical scenarios, the theoretical and simulated throughput match provided that the spreading-loss exponent is appropriately fitted to the simulation scenario. Overall, the proposed framework provides easy-to-obtain network throughput results, which can be used as a complement or an alternative to time-costly, deployment-dependent network simulations.

ABSTRACT In this paper, we extend previous work on geographic random forwarding (GeRaF) by considering a more efficient paradigm whereby on/off cycles are exploited and every node can participate to packet relaying, as long as it wakes up during any handshake procedure. The protocol is fully distributed, as routing decisions are made hop by hop based on geographic metrics and using a joint channel access/routing procedure, like in the first version of GeRaF. In addition, a previously defined analytical model for GeRaF is used to analyze the protocol, after extending it to cover the new channel access and relay selection procedure. The model is employed to find optimal choices for the parameters of the protocol, before comparing analytical results with ns2 simulations. Results confirm that the geographic paradigm remains a very good solution for energy‐constrained, density‐constrained, and latency‐constrained networks, and that small yet very effective protocol optimizations can achieve remarkable performance improvements. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we derive fundamental performance limits of underwater (UW) networks via an analysis of the average behavior of random deployments. In particular, we consider an UW network that consists of a Poisson point process of transmitters, each with a receiver at a given distance. The link channel model accounts for path-loss, frequency-dependent absorption, and Rayleigh fading. We evaluate the probability of a successful transmission, defined as the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a predetermined threshold, over different network and channel realizations. We then determine the network throughput density and the transmission capacity, defined as the maximum throughput density such that a constraint on the success probability is satisfied. The dependence of these metrics on the operating frequency and other system parameters is quantified through the proposed framework.

In this paper, we evaluate the performance of a Medium Access Control (MAC) protocol for MIMO ad hoc networks under imperfect channel estimation. To this end, we also present an analysis of channel estimation errors using correlator-based and Minimum Mean-Square Error (MMSE) channel estimators. Unlike similar works, we specifically focus on a scenario where the presence of several simultaneous, symbol-asynchronous signals makes the problem more complicated than in traditional channel estimation. In particular, we show that there is direct dependence of the channel estimation error on the instantaneous channel matrix. The model we propose in this paper makes it possible to quickly evaluate the performance of channel estimation schemes as a function of the system parameters. In this light, we include the effect of channel estimation errors in an ad hoc networking protocol simulator and thoroughly evaluate their impact. Our results show that there exists a significant interplay between the performance of MAC protocols for MIMO networks and the accuracy of channel estimation. Moreover, we show that interesting tradeoffs arise between MAC- and physical level-related parameters.

Abstract In this paper we present TinyNET, a modular framework allowing development and quick integration of protocols and applications for Wireless Sensor Networks (WSNs) in TinyOS. The motivation behind TinyNET is two‐fold: on one hand it allows to adopt a divide‐and‐conquer approach in the development of any TinyOS application; on the other hand it provides a flexible administration of network protocols. As a sample development using TinyNET, we consider an environmental monitoring application, and test it over a floor‐wide WSN testbed. Data are converge‐casted toward a sink node, which gathers all data collected by the sensors. Routing toward the sink is achieved by means of a hop count (HC) based algorithm. Our framework also integrates support for the 6LowPAN standard (providing, e.g., per‐sensor queries and pings). Thanks to TinyNET, s these messages will make transparent use of the underlying network protocols. Also, TinyNET transparently manages the network components and related messages, allowing different applications to share the same network stack; furthermore, it translates TinyOS interfaces so that any previously developed application can be easily ported. These features make it possible to have a global vision over any application, as well as to focus on each of its separate components. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper we present TinyNET, a modular framework that allows development and quick integration of protocols and applications for Wireless Sensor Networks in TinyOS. As a sample experience of software development using TinyNET, we employ an environmental monitoring application. We organize our network testbed in a converge-casting topology, where the sink gathers the data collected by all sensors. Routing toward the sink is achieved based on a hop-count based algorithm. Our framework also integrates support for the 6LowPAN standard, which becomes therefore integrated with other network components and allows, e.g., to query single sensors within the network using ping messages. Thanks to TinyNET, these messages will make transparent use of the underlying network protocols. The main advantages yielded by TinyNET are found in the modularity of network components, in specific code that transparently manages the interfaces among different modules and translates standard TinyOS interfaces into new TinyNET ones, and in the readily available infrastructure to multiplex different applications over the same network stack. This allows a global vision of the system as well as the chance to focus on the design of separate components.

In this paper, we propose an analytical technique to evaluate the statistics of the channel estimation error in a simple multi-user ad hoc networking scenario. This problem is very relevant in situations where advanced PHY techniques are used (e.g., MIMO or interference cancellation) and channel state information may be needed. The presence of several simultaneous and non-orthogonal signals makes the problems significantly more complicated than in traditional channel estimation. In particular, there is direct dependence of the channel estimation error on the instantaneous channel matrix. The proposed model makes it possible to quickly evaluate the performance of channel estimation schemes as a function of the system parameters. In this light, we include the effect of channel estimation errors in an ad hoc networking protocol simulator and thoroughly evaluate their impact. Our results show that there exists a significant interplay between the performance of MAC protocols for MIMO networks and the accuracy of channel estimation. Moreover, we show that relevant tradeoffs arise between MAC- and PHY-level parameters which lead to the definition of design guidelines.

Interest in underwater acoustic networks has grown rapidly with the desire to monitor the large portion of the world covered by oceans. Fundamental differences between underwater acoustic propagation and terrestrial radio propagation may call for new criteria for the design of networking protocols. In this paper, we focus on some of these fundamental differences, including attenuation and noise, propagation delays, and the dependence of usable bandwidth and transmit power on distance (which has not been extensively considered before in protocol design studies). Furthermore, the relationship between the energy consumptions of acoustic modems in various modes (i.e., transmit, receive, and idle) is different than that of their terrestrial radio counterparts, which also impacts the design of energy-efficient protocols. The main contribution of this work is an in-depth analysis of the impacts of these unique relationships. We present insights that are useful in guiding both protocol design and network deployment. We design a class of energy-efficient routing protocols for underwater sensor networks based on the insights gained in our analysis. These protocols are tested in a number of relevant network scenarios, and shown to significantly outperform other commonly used routing strategies and to provide near optimal total path energy consumption. Finally, we implement in ns2 a detailed model of the underwater acoustic channel, and study the performance of routing choices when used with a simple MAC protocol and a realistic PHY model, with special regard to such issues as interference and medium access.

In this paper, we present a novel MAC approach for ad hoc networks in which Multiple Input-Multiple Output (MIMO) techniques are used at the physical level (PHY). The use of MIMO in PHY point-to-point as well as multiuser communications has been extensively studied in the recent literature. Here we go one step further, extending the approach to also include protocol design for decentralized and infrastructureless ad hoc networks. First, we study the impact of MIMO on packet transmission in an ad hoc network setting. Then, following a crossiquestlayer design paradigm, we deploy a distributed access control protocol and characterize its performance, with the aim to improve spatial reuse and convey more information on a single ad hoc link. We also explore the interaction between the Medium Access Control (MAC) and PHY layers, and use this knowledge to implement proper policies for distributed traffic control and robustness against interference. Important tradeoffs that arise when managing radio resources are highlighted, and extensive simulation results are presented and discussed.

Geographic forwarding in wireless sensor networks (WSN) has long suffered from the problem of bypassing "dead ends," i.e., those areas in the network where no node can be found in the direction of the data collection point (the sink). Solutions have been proposed to this problem, that rely on geometric techniques leading to the planarization of the network topology graph. In this paper, a novel method alternative to planarization is proposed, termed ALBA-R, that successfully routes packets to the sink transparently to dead ends. ALBA-R combines nodal duty cycles (awake/asleep schedules), channel access and geographic routing in a cross-layer fashion. Dead ends are dealt with by enhancing geographic routing with a mechanism that is distributed, localized and capable of routing packets around connectivity holes. An extensive set of simulations is provided, that demonstrates that ALBA-R is scalable, generates negligible overhead, and outperforms similar solutions with respect to all the metrics of interest investigated, especially in sparse topologies, notoriously the toughest benchmark for geographic routing protocols.

In this paper, we consider a wireless sensor network (WSN) where packets are forwarded to a central collector node (the sink). As all network traffic converges to this point, congestion builds up. We deal with this problem by assuming that the nodes around the sink are allowed to use a different, more efficient protocol for advancing data, namely a method designed to yield more throughput and reduce collisions, at the expense of some coordination efforts. We carry out extensive simulations, and compare our solution (which is composed of two protocols, one for the sink and its neighbors and one for the rest of the network) to a simpler collision avoidance approach used throughout the whole network. This allows to highlight the tradeoff between coordinating and interfacing two protocols (which requires better design, but achieves higher effectiveness) and using just one protocol (simpler, but more prone to congestion and losses). We show how our two-protocol solution outperforms the simpler one.

In this paper, we consider a MAC protocol for ad hoc networks where nodes are equipped with multiple antennas, and communications are spatially multiplexed using the Bell labs LAyered space time (BLAST) system. The contribution of this paper is twofold. First, we introduce two different analytical models aimed at predicting the propagation of detection errors within the BLAST receiver, the first based on a Gaussian approximation of the detection errors, and the second on the weighed enumeration of error configurations. A simplification of the latter, with lower complexity, is also obtained and compared to the original model. We then use these analytical tools to assess the performance of a cross–layer MAC protocol, and compare it with fully detailed simulations. Since the analytical tools replace the simulation of the physical layer, the proposed semianalytical approach is much faster than the bit-by-bit simulations. Numerous results are provided for the network performance assessment, showing that our semianalytical approach is able to predict network behavior with very good accuracy, but much lower complexity.

In this paper, we address the impact of different access strategies in ad hoc networks with multiple antennas and MIMO communications. We employ a cross-layer designed MAC protocol that allows both for multiple simultaneous access to the radio medium and for proper exploitation of multiuser detection at the receiver for interference cancellation purposes. Still, the network is subject to early deadlock if no access strategy is employed that reduces transmission persistency. To this aim, we study two types of exponential backoff, namely node- wise and destination-wise, and combine them with a form of cooperative agreement on who takes the role of transmitter or receiver. The impact of all schemes is assessed and a comparison is pursued, focusing on typical network metrics (such as throughput and transmission delay among others) and showing when and why one technique performs better than the others.

In this paper, we consider a layered space-time multiuser detection technique and propose an analytical approximation for its performance. Our work is useful in two different stages of network design. On one hand, the approximation may be used to evaluate the bit and packet error performance of communications among a group of terminals making use of multiuser detection. On the other hand, it can also be seen as a very valuable tool from the networking point of view, as it may help in designing radio access control protocols based on multiuser detection, as analytical formulas are very fast to evaluate, in contrast to bit-level simulations that may need a long time to complete. Thus, an analytical formulation is important, because it helps discriminating among different protocol alternatives, speeding up considerably the protocol design phase and the development of new radio access policies for multiuser networks.

In this work, we analyze the effects of a distributed transmission coordination scheme that is particularly suited for ad hoc networks with the ability to exploit spatially--multiplexed communications over MIMO links. Following previous work where we discussed the performance of this kind of networks and deployed a fast yet reliable approximation for physical layer behavior, we now employ this knowledge to analyze the performance of a different access scheme, meant to outperform previous results by the use of distributed coordination of transmissions and receptions without adding any further redundancy in communications. Furthermore, we analyze the effect of tuning two parameters on the overall network performance.

We consider the application of spatial multiplexing to ad hoc networks where nodes have multiple antennas. At the physical level, we suppose that layered space-time multiuser detection (LAST-MUD) is applied to separate multiple streams arriving at the receiver simultaneously. Our contributions here consist first in the reproduction of the multiuser detection process performance by a simple analysis, where we also specify when the analytical results are expected to be accurate and why. Second, we use this approximation to perform a cross-layer design of a MIMO ad hoc network where physical layer and medium access control strategies are integrated to maximize the network throughput. We finally corroborate our conclusions, by comparing analysis with simulation results both at the link and the network level

In this paper, we shed some light on the implications that using a recently proposed layered space-time multiuser detection technique has on MAC protocol design for ad hoc networks with multiple antennas. From this point of view, our work relates to both physical layer and network layer studies. In fact, on the one hand physical layer aspects are important to characterize the behavior at the receiver, especially in terms of bit and packet error rates, but are rarely considered in conjunction with networking scenarios; on the other hand, networking aspects are typically studied using drastically simplified physical layer models that, while allowing easier networking analysis, are often too restrictive or unrealistic. Also, this disconnects between physical and networking layer studies may severely limit the possibilities for cross-layer optimization, which appears to be the right approach for efficient wireless network design. Our paper is then an effort to establish some connection between the "physical" and "network" approaches, highlighting some interesting capabilities that ad hoc networks with multiple antennas are endowed by the use of multiuser detection

In this paper, we propose a novel technique for the provisioning of multicast streaming flows in 3G W-CDMA cellular systems. Our focus here is on the transmission of a downlink multicast streaming flow to the interested users in a 3G cell. For error control, we propose packet-based forward error correction (FEC) and with the transmission of a certain amount of redundancy over parallel common channels. In practice, we exploit the temporal diversity over multiple channels to strengthen the FEC scheme thereby increasing the QoS. To increase error resilience, in every channel we exploit well-known packet-based Reed Solomon like coding techniques. Moreover, appropriate time shifts of the information sent across the parallel common channels are also introduced to increase the robustness against error bursts. The performance evaluation is carried out through an analytical framework. The obtained results show the substantial benefits deriving from the usage of a multiple channel transmission technique.