Ana Aguiar

Towards Efficient Mobile M2M Communications: Survey and Open Challenges

Machine-to-Machine (M2M) communications enable networked devices and services to exchange information and perform actions seamlessly without the need for human intervention. They are viewed as a key enabler of the Internet of Things (IoT) and ubiquitous applications, like mobile healthcare, telemetry, or intelligent transport systems. We survey existing work on mobile M2M communications, we identify open challenges that have a direct impact on performance and resource usage efficiency, especially the impact on energy efficiency, and we review techniques to improve communications. We review the ETSI standard and application protocols, and draw considerations on the impact of their use in constrained mobile devices. Nowadays, smartphones are equipped with a wide range of embedded sensors, with varied local and wide area connectivity capabilities, and thus they offer a unique opportunity to serve as mobile gateways for other more constrained devices with local connectivity. At the same time, they can gather context data about users and environment from the embedded sensors. These capabilities may be crucial for mobile M2M applications. Finally, in this paper, we consider a scenario where smartphones are used as gateways that collect and aggregate data from sensors in a cellular network. We conclude that, in order for their use to the feasible in terms of a normal depletion time of a smartphones battery, it is a good advice to maximize the collection of data necessary to be transmitted from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways.

Experimental Characterization and Modeling of RF Jamming Attacks on VANETs

In this paper, we evaluate the performance of 802.11p-based vehicular communications in the presence of radio frequency (RF) jamming attacks. Specifically, we characterize the transmission success rate of a car-to-car link subject to constant, periodic, and reactive RF jamming. First, we conduct extensive measurements in an anechoic chamber, where we study the benefits of built-in techniques for interference mitigation. In addition, we identify that the periodic transmission of preamble-like jamming signals can hinder successful communication despite being up to five orders of magnitude weaker than the signal of interest. We further provide the rationale behind this remarkably high jamming effectiveness. Additionally, we quantify the impact of reaction delay and interference signal length on the effectiveness of the reactive jammer. Then, by means of outdoor measurements, we evaluate the suitability of using the indoor measurements as a model to characterize the performance of car-to-car communications in the presence of RF jamming. Finally, we conduct outdoor measurements emulating a vehicular platoon and study the threats that RF jamming poses to this vehicular ad hoc network (VANET) application. We observe that constant, periodic, but also reactive jamming can hinder communication over large propagation areas, which would threaten road safety.

In VANETs we trust?: characterizing RF jamming in vehicular networks

In this paper we study the impact of RF jamming on 802.11p car-to-car communications. We build a jammer on a software defined radio and implement constant, reactive and pilot jamming patterns, whose effectiveness is first measured in an anechoic chamber. We perform extensive experiments in two relevant outdoor scenarios, namely a straight road in an open space as well as a dense building scenario with a crossroad and characterize the performance of 802.11p communications under the impact of constant and reactive RF jamming. The constant jammer is able to dramatically disrupt communication regardless of the scenario. The reactive jammer exhibits a low impact in scenarios with reduced line-of-sight as its jamming success greatly depends on the relative position of the nodes. It is, however, very effective in open-space scenarios. In general, we observe that RF jamming can cause large communication-blind areas. Under these conditions, critical safety applications would simply fail in their purpose of timely warning dissemination.

A mobile sensing architecture for massive urban scanning

Mobile Sensor Networks based on connected vehicles and smart phones are poised to become key enablers in the development of sustainable and intelligent transportation systems in urban environments. By gathering and processing massive amounts of data in real-time, this form of information and communication infrastructure can be instrumental towards improving traffic flow, reducing carbon emissions and promoting multi-modal mobility and enhanced coordination among public transit systems. We propose a system architecture for a Massive Multi-Sensor Urban Scanner capable of acquiring large quantities of real-time information from a vast variety of sources and sending the data to a back-end data processing cloud using multiple communication interfaces. Requirements, technical challenges, design choices and first results are explained in detail based on a prototype that is currently being deployed in Porto, Portugal.

Bi-directional WLAN channel measurements in different mobility scenarios

The interest in the behaviour of the wireless channel increases as WLAN access points become more widespread. In order to enhance the performance of existing applications in wireless environments, especially when mobility is present, knowledge about channel behaviour is of great importance. Nevertheless, very few experimental results have been documented and evaluated. We designed and realised a measurement campaign to investigate channel characteristics in different mobility conditions and different environments. Special attention is paid to the correlation between both channel directions. This is particularly interesting for channel-adaptive mechanisms; the strength of this correlation is important for the use of such techniques without the need for additional signalling or feedback of channel state information. We present the results of this measurement campaign. We evaluate the channel behaviour in various scenarios typical for WLAN. The linear time dependences within the same direction and between the opposite directions are calculated and compared. Finally we studied the usefulness of the results for channel prediction.

Impact of Position Errors on Path Loss Model Estimation for Device-to-Device Channels

Many wireless applications require a propagation model that describes the attenuation of the transmitted signal as a function of the distance between devices. Such channel models are derived commonly from signal strength measurements, and assume that the true distances between wireless terminals are known. In practice, however, the true distances may be unavailable or difficult to obtain, for instance in mobile scenarios or in the absence of line-of-sight. These conditions typically occur in forested environments, urban areas, etc. This paper addresses the problem of path loss model parameter estimation in presence of erroneous distance measurements, such as the ones derived from the GPS positions. We provide a model for the uncertainties, and study the impact of distance errors on the estimation of a log-distance channel model. Our main conclusion is that the path loss model can be estimated with a reasonable accuracy from unreliable distances, provided that the measurements are taken at distances beyond a few standard deviations of the GPS positioning error. In case the maximum communication range does not allow such large distances, we provide a method to correct the erroneous channel model. Real-world measurements are used in order to validate our approach.

Channel Prediction Heuristics for Adaptive Modulation in WLAN

Channel-adaptive techniques increase the efficiency of wireless communications, as they are efficient in coping with the quality variation of the wireless channels. However, channel-adaptive mechanisms require a prediction of the future behaviour of the channel. Heuristics are an important alternative to more complex predictors in WLAN scenarios. In this paper we use WLAN measurement traces for the simulative evaluation of the influence of channel prediction errors in the performance of a threshold-based adaptive modulation scheme, considering also the case of delayed channel feedback. The results show that, as long as the necessary prediction horizon does not exceed 2 ms, assuming the channel to stay constant leads to less than 15% capacity loss compared to the case when perfect channel prediction is used. When farther prediction horizons are needed, the moving average of the received signal should be used for channel prediction.

Mining geographic data for fuel consumption estimation

Mobility is one of the greatest contributors to the personal carbon footprint and to pollution and noise in urban areas. Still, these factors are not yet easily quantifiable in personal or urban scale, e.g. impact of each car trip or areas most exposed to CO2 emissions. In this article, we propose an innovative solution for estimating fuel consumption and emissions leveraging the opportunities generated by the ubiquitous availability of mobile devices. We collect a large data set of GPS and fuel consumption data crowd-sourced by volunteer participants with an Android mobile application that logs the smartphones embedded GPS data and gathers vehicle data using an external On-Board Diagnostics (OBD) device. This data is used to develop a model that estimates the instantaneous fuel consumption from the smartphones GPS data alone, using the OBD data as ground truth. We use speed, acceleration and steepness as predictor variables to train polynomial models with and without cross-product terms. With the best general model (trained and tested on all participant vehicles), we obtain an average residual standard deviation of 1.58 l/100km for average consumption on 1min intervals. For individual models (trained and tested on each participant vehicle), we obtain an average residual standard deviation of 1.43 l/100km. The average fuel consumption for the used data set was 6.7 l/100km.

IoT interoperability for actuating applications through standardised M2M communications

Smartphones, with vast connectivity and sensing capabilities, are the natural choice to serve as gateways for physiologic sensors and body area networks. Machine-to-Machine (M2M) middleware standards are driving the emergence of Internet of Things (IoT) applications by providing autonomic interoperability. However, standards remain opaque and difficult to interpret, throttling implementation. In this work, we report the design and development of an ETSI M2M Gateway (GW) on a mobile device, instantiated in a smartphone. We describe our reasoning in interpreting the standard for the varied implementation challenges faced. Further, we develop libraries to ease the deployment of IoT applications using the ETSI M2M ecosystem with reduced development costs. Finally, we validated the implementation with a mobile e-health pilot with 10 participants during 3 weeks.

Speech stress assessment using physiological and psychological measures

Emotional stress is commonly experienced while speaking in public, producing changes to the various speech productions subsystems, affecting the speech signal in predictable ways and being easily conveyed to listeners. Speech stress indicators, however, are typically studied under laboratory settings, allowing little generalization to real life settings. To bridge this gap, we propose an interdisciplinary approach to assess speech stress during public speaking events, based on a platform that records speech simultaneously annotated with physiological and psychological measures. This approach enables the collection of a large corpus of annotated speech in ecological settings, i.e. in objectively stressing situations. We also propose and implement a methodology to assess listeners evaluation of stress including psychologists, and overall public. The platform has been in use for the past 5 months, and we have collected 13 complete samples after the initial iterative development procedure. Preliminary results indicate that the proposed user-friendly platform is an accurate and robust method to collect annotated speech under ecological settings that can be processed to obtain speech stress indicators. The findings will be used primarily in the design of computer and mobile assisted voice coaching applications, but the outreach extends to mobile emotion sensing for individuals and crowds.