A Tutorial of the Mobile Multimedia Wireless Sensor Network (M3WSN) OMNeT++ Framework

In this work, we will give a detailed tutorial instruction about how to use the Mobile Multi-Media Wireless Sensor Networks (M3WSN) simulation framework. The M3WSN framework has been published as a scientific paper in the 6th International Workshop on OMNeT++ (2013) [1]. M3WSN framework enables the multimedia transmission of real video se- quence. Therefore, a set of multimedia algorithms, protocols, and services can be evaluated by using QoE metrics. Moreover, key video-related information, such as frame types, GoP length and intra-frame dependency can be used for creating new assessment and optimization solutions. To support mobility, M3WSN utilizes different mobility traces to enable the understanding of how the network behaves under mobile situations. This tutorial will cover how to install and configure the M3WSN framework, setting and running the experiments, creating mobility and video traces, and how to evaluate the performance of different protocols. The tutorial will be given in an environment of Ubuntu 12.04 LTS and OMNeT++ 4.2.

An OMNeT++ Framework to Evaluate Video Transmission in Mobile Wireless Multimedia Sensor Networks

The development and evaluation of new algorithms and protocols for Wireless Multimedia Sensor Networks (WMSNs) are usually supported by means of a discrete event network simulator, where OMNeT++ is one of the most important ones. However, experiments involving multimedia transmission, video flows with different characteristics, genres, group of pictures lengths, and coding techniques must be evaluated based also on Quality of Experience (QoE) metrics to reflect the user's perception. Such experiments require the evaluation of video-related information, i.e., frame type, received/lost, delay, jitter, decoding errors, as well as inter and intra-frame dependency of received/distorted videos. However, existing OMNeT++ frameworks for WMSNs do not support video transmissions with QoE-awareness, neither a large set of mobility traces to enable evaluations under different multimedia/mobile situations. In this paper, we propose a Mobile MultiMedia Wireless Sensor Network OMNeT++ framework (M3WSN) to support transmission, control and evaluation of real video sequences in mobile WMSNs.

Trends in Human-Centric Multimedia Networking Scenarios

The evolution of wireless access technologies and mobile devices, together with the constant demand for video services, has created new Human-Centric Multimedia Networking (HCMN) scenarios. However, HCMN poses several challenges for content creators and network providers to deliver multimedia data with an acceptable quality level based on the user experience. Moreover, human experience and context, as well as network information play an important role in adapting and optimizing video dissemination. In this paper, we discuss trends to provide video dissemination with Quality of Experience (QoE) support by integrating HCMN with cloud computing approaches. We identified five trends coming from such integration, namely Participatory Sensor Networks, Mobile Cloud Computing formation, QoE assessment, QoE management, and video or network adaptation.

A Link Quality and Geographical-aware Routing Protocol for Video Transmission in Mobile IoT

Wireless mobile sensor networks are enlarging the Internet of Things (IoT) portfolio with a huge number of multimedia services for smart cities. Safety and environmental monitoring multimedia applications will be part of the Smart IoT systems, which aim to reduce emergency response time, while also predicting hazardous events. In these mobile and dynamic (possible disaster) scenarios, opportunistic routing allows routing decisions in a completely distributed manner, by using a hop- by-hop route decision based on protocol-specific characteristics, and a predefined end-to-end path is not a reliable solution. This enables the transmission of video flows of a monitored area/object with Quality of Experience (QoE) support to users, headquarters or IoT platforms. However, existing approaches rely on a single metric to make the candidate selection rule, including link quality or geographic information, which causes a high packet loss rate, and reduces the video perception from the human standpoint. This article proposes a cross-layer Link quality and Geographical-aware Opportunistic routing protocol (LinGO), which is designed for video dissemination in mobile multimedia IoT environments. LinGO improves routing decisions using multiple metrics, including link quality, geographic loca- tion, and energy. The simulation results show the benefits of LinGO compared with well-known routing solutions for video transmission with QoE support in mobile scenarios.

Assessment of a robust opportunistic routing for video transmission in dynamic topologies

Mobile multimedia ad hoc services run on dynamic topologies due to node mobility or failures and wireless channel impairments. A robust routing service must adapt to topology changes with the aim of recovering or maintaining the video quality level and reducing the impact of the user's experience. In those scenarios, beacon-less Opportunistic Routing (OR) increases the robustness by supporting routing decisions in a completely distributed manner based on protocol-specific characteristics. However, the existing beacon-less OR approaches do not efficiently combine multiple metrics for forwarding selection, which cause higher packet loss rate, and consequently reduce the video quality level. In this paper, we assess the robustness and reliability of our recently developed OR protocol under node failures, called cross-layer Link quality and Geographical-aware OR protocol (LinGO). Simulation results show that LinGO achieves multimedia dissemination with QoE support and robustness in scenarios with dynamic topologies.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

Cervical soft tissue imaging using a mobile CBCT scanner with a flat panel detector in comparison with corresponding CT and MRI data sets

OBJECTIVE: The aim of this study was to evaluate soft tissue image quality of a mobile cone-beam computed tomography (CBCT) scanner with an integrated flat-panel detector. STUDY DESIGN: Eight fresh human cadavers were used in this study. For evaluation of soft tissue visualization, CBCT data sets and corresponding computed tomography (CT) and magnetic resonance imaging (MRI) data sets were acquired. Evaluation was performed with the help of 10 defined cervical anatomical structures. RESULTS: The statistical analysis of the scoring results of 3 examiners revealed the CBCT images to be of inferior quality regarding the visualization of most of the predefined structures. Visualization without a significant difference was found regarding the demarcation of the vertebral bodies and the pyramidal cartilages, the arteriosclerosis of the carotids (compared with CT), and the laryngeal skeleton (compared with MRI). Regarding arteriosclerosis of the carotids compared with MRI, CBCT proved to be superior. CONCLUSIONS: The integration of a flat-panel detector improves soft tissue visualization using a mobile CBCT scanner.

Demo Abstract: Disseminating WMSN Data by Using Social Network and Web

For smart cities applications, a key requirement is to disseminate data collected from both scalar and multimedia wireless sensor networks to thousands of end-users. Furthermore, the information must be delivered to non-specialist users in a simple, intuitive and transparent manner. In this context, we present Sensor4Cities, a user-friendly tool that enables data dissemination to large audiences, by using using social networks, or/and web pages. The user can request and receive monitored information by using social networks, e.g., Twitter and Facebook, due to their popularity, user-friendly interfaces and easy dissemination. Additionally, the user can collect or share information from smart cities services, by using web pages, which also include a mobile version for smartphones. Finally, the tool could be configured to periodically monitor the environmental conditions, specific behaviors or abnormal events, and notify users in an asynchronous manner. Sensor4Cities improves the data delivery for individuals or groups of users of smart cities applications and encourages the development of new user-friendly services.

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

Source of funding in experimental studies of mobile phone use on health: Update of systematic review

A previous review showed that among 59 studies published in 1995–2005, industry-funded studies were least likely to report effects of controlled exposure to mobile phone radiation on health-related outcomes. We updated literature searches in 2005–2009 and extracted data on funding, conflicts of interest and results. Of 75 additional studies 12% were industry-funded, 44% had public and 19% mixed funding; funding was unclear in 25%. Previous findings were confirmed: industry-sponsored studies were least likely to report results suggesting effects. Interestingly, the proportion of studies indicating effects declined in 1995–2009, regardless of funding source. Source of funding and conflicts of interest are important in this field of research.

Predictors and overestimation of recalled mobile phone use among children and adolescents

A growing body of literature addresses possible health effects of mobile phone use in children and adolescents by relying on the study participants' retrospective reconstruction of mobile phone use. In this study, we used data from the international case-control study CEFALO to compare self-reported with objectively operator-recorded mobile phone use. The aim of the study was to assess predictors of level of mobile phone use as well as factors that are associated with overestimating own mobile phone use. For cumulative number and duration of calls as well as for time since first subscription we calculated the ratio of self-reported to operator-recorded mobile phone use. We used multiple linear regression models to assess possible predictors of the average number and duration of calls per day and logistic regression models to assess possible predictors of overestimation. The cumulative number and duration of calls as well as the time since first subscription of mobile phones were overestimated on average by the study participants. Likelihood to overestimate number and duration of calls was not significantly different for controls compared to cases (OR=1.1, 95%-CI: 0.5 to 2.5 and OR=1.9, 95%-CI: 0.85 to 4.3, respectively). However, likelihood to overestimate was associated with other health related factors such as age and sex. As a consequence, such factors act as confounders in studies relying solely on self-reported mobile phone use and have to be considered in the analysis.

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.

Analysis Methodology for Flow-level Evaluation of a Hybrid Mobile-Sensor Network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.