Towards Securing Peer-to-peer SIP in the MANET Context: Existing Work and Perspectives

The Session Initiation Protocol (SIP) is a key building block of many social applications, including VoIP communication and instant messaging. In its original architecture, SIP heavily relies on servers such as proxies and registrars. Mobile Ad hoc NETworks (MANETs) are networks comprised of mobile devices that communicate over wireless links, such as tactical radio networks or vehicular networks. In such networks, no fixed infrastructure exists and server-based solutions need to be redesigned to work in a peer-to-peer fashion. We survey existing proposals for the implementation of SIP over such MANETs and analyze their security issues. We then discuss potential solutions and their suitability in the MANET context

Accurate Ambient Noise Assessment Using Smartphones

[EN] Nowadays, smartphones have become ubiquitous and one of the main communication resources for human beings. Their widespread adoption was due to the huge technological progress and to the development of multiple useful applications. Their characteristics have also experienced a substantial improvement as they now integrate multiple sensors able to convert the smartphone into a flexible and multi-purpose sensing unit. The combined use of multiple smartphones endowed with several types of sensors gives the possibility to monitor a certain area with fine spatial and temporal granularity, a procedure typically known as crowdsensing. In this paper, we propose using smartphones as environmental noise-sensing units. For this purpose, we focus our study on the sound capture and processing procedure, analyzing the impact of different noise calculation algorithms, as well as in determining their accuracy when compared to a professional noise measurement unit. We analyze different candidate algorithms using different types of smartphones, and we study the most adequate time period and sampling strategy to optimize the data-gathering process. In addition, we perform an experimental study comparing our approach with the results obtained using a professional device. Experimental results show that, if the smartphone application is well tuned, it is possible to measure noise levels with a accuracy degree comparable to professional devices for the entire dynamic range typically supported by microphones embedded in smartphones, i.e., 35 95 dB.This work was partially supported by the “Programa Estatal de Investigación, Desarrollo e InnovaciOn Orientada a Retos de la Sociedad, Proyecto TEC2014-52690-R”, the “Universidad Laica Eloy Alfaro de Manabí” and the “Programa de Becas SENESCYTde la República del Ecuador.”Zamora-Mero, WJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2017). Accurate Ambient Noise Assessment Using Smartphones. Sensors. 17(4):1-18. doi:10.3390/s17040917S118174Noise European Environment Agencyhttp://www.eea.europa.eu/themes/noise/introZannin, P. H. T., Ferreira, A. M. C., & Szeremetta, B. (2006). Evaluation of Noise Pollution in Urban Parks. Environmental Monitoring and Assessment, 118(1-3), 423-433. doi:10.1007/s10661-006-1506-6Kanjo, E. (2009). NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping. Mobile Networks and Applications, 15(4), 562-574. doi:10.1007/s11036-009-0217-yAssessment and management of environmental noise (EU Directive)http://eur-lex.europa.eu/eli/dir/2002/49/ojCommission Directive (EU) 2015/ 996 of 19 May 2015http://eur-lex.europa.eu/eli/dir/2015/996/ojLane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140-150. doi:10.1109/mcom.2010.5560598Ganti, R., Ye, F., & Lei, H. (2011). Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 49(11), 32-39. doi:10.1109/mcom.2011.6069707Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile Crowd Sensing and Computing. ACM Computing Surveys, 48(1), 1-31. doi:10.1145/2794400Maisonneuve, N., Stevens, M., Niessen, M. E., & Steels, L. (2009). NoiseTube: Measuring and mapping noise pollution with mobile phones. Environmental Science and Engineering, 215-228. doi:10.1007/978-3-540-88351-7_16Rana, R., Chou, C. T., Bulusu, N., Kanhere, S., & Hu, W. (2015). Ear-Phone: A context-aware noise mapping using smart phones. Pervasive and Mobile Computing, 17, 1-22. doi:10.1016/j.pmcj.2014.02.001Kardous, C. A., & Shaw, P. B. (2014). Evaluation of smartphone sound measurement applications. The Journal of the Acoustical Society of America, 135(4), EL186-EL192. doi:10.1121/1.4865269Le Prell, C., Nast, D., & Speer, W. (2014). Sound level measurements using smartphone «apps»: Useful or inaccurate? Noise and Health, 16(72), 251. doi:10.4103/1463-1741.140495Sonometer PCE322Ahttp://www.pce-iberica.es/medidor-detalles-tecnicos/instrumento-de-ruido/sonometro-con-logger-de-datos-sl-322.htmKardous, C. A., & Shaw, P. B. (2016). Evaluation of smartphone sound measurement applications (apps) using external microphones—A follow-up study. The Journal of the Acoustical Society of America, 140(4), EL327-EL333. doi:10.1121/1.4964639Zamora, W., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2016). A Survey on Smartphone-Based Crowdsensing Solutions. Mobile Information Systems, 2016, 1-26. doi:10.1155/2016/9681842Electroacoustics—Sound level meters—Part 1: Specificationshttps://webstore.iec.ch/publication/5708Samsung Galaxy S7 edge SM-G935T Complimentary Teardown Report with Additional Commentaryhttp://www.techinsights.com/about-techinsights/overview/blog/samsung-galaxy-s7-edge-teardown

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

A review of urban air pollution monitoring and exposure assessment methods

The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data