Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.

Does the use of mobile devices (tablets and smartphones) affect survey quality and choice behaviour in web surveys?

Web surveys are becoming increasingly popular in survey research including stated preference surveys. Compared with face-to-face, telephone and mail surveys, web surveys may contain a different and new source of measurement error and bias: the type of device that respondents use to answer the survey questions. This is the first study that tests whether the use of mobile devices, tablets or smartphones, affects survey characteristics and stated preferences in a web-based choice experiment. The web survey on expanding renewable energy production in Germany was carried out with 3182 respondents, of which 12% used a mobile device. Propensity score matching is used to account for selection bias in the use of mobile devices for survey completion. We find that mobile device users spent more time than desktop/laptop users to answer the survey. Yet, desktop/laptop users and mobile device users do not differ in acquiescence tendency as an indicator of extreme response patterns. For mobile device users only, we find a negative correlation between screen size and interview length and a positive correlation between screen size and acquiescence tendency. In the choice experiment data, we do not find significant differences in the tendency to choose the status quo option and scale between both subsamples. However, some of the estimates of implicit prices differ, albeit not in a unidirectional fashion. Model results for mobile device users indicate a U-shaped relationship between error variance and screen size. Together, the results suggest that using mobile devices is not detrimental to survey quality.

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.

Mobile phone use and brain tumors in children and adolescents: a multicenter case-control study

It has been hypothesized that children and adolescents might be more vulnerable to possible health effects from mobile phone exposure than adults. We investigated whether mobile phone use is associated with brain tumor risk among children and adolescents.

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.

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.

Surveillance and simulation of bovine spongiform encephalopathy and scrapie in small ruminants in Switzerland

BACKGROUND: After bovine spongiform encephalopathy (BSE) emerged in European cattle livestock in 1986 a fundamental question was whether the agent established also in the small ruminants' population. In Switzerland transmissible spongiform encephalopathies (TSEs) in small ruminants have been monitored since 1990. While in the most recent TSE cases a BSE infection could be excluded, for historical cases techniques to discriminate scrapie from BSE had not been available at the time of diagnosis and thus their status remained unclear. We herein applied state-of-the-art techniques to retrospectively classify these animals and to re-analyze the affected flocks for secondary cases. These results were the basis for models, simulating the course of TSEs over a period of 70 years. The aim was to come to a statistically based overall assessment of the TSE situation in the domestic small ruminant population in Switzerland. RESULTS: In sum 16 TSE cases were identified in small ruminants in Switzerland since 1981, of which eight were atypical and six were classical scrapie. In two animals retrospective analysis did not allow any further classification due to the lack of appropriate tissue samples. We found no evidence for an infection with the BSE agent in the cases under investigation. In none of the affected flocks, secondary cases were identified. A Bayesian prevalence calculation resulted in most likely estimates of one case of BSE, five cases of classical scrapie and 21 cases of atypical scrapie per 100'000 small ruminants. According to our models none of the TSEs is considered to cause a broader epidemic in Switzerland. In a closed population, they are rather expected to fade out in the next decades or, in case of a sporadic origin, may remain at a very low level. CONCLUSIONS: In summary, these data indicate that despite a significant epidemic of BSE in cattle, there is no evidence that BSE established in the small ruminant population in Switzerland. Classical and atypical scrapie both occur at a very low level and are not expected to escalate into an epidemic. In this situation the extent of TSE surveillance in small ruminants requires reevaluation based on cost-benefit analysis.

Three-dimensional printing of models for preoperative planning and simulation of transcatheter valve replacement

In this study, we show the use of three-dimensional printing models for preoperative planning of transcatheter valve replacement in a patient with an extreme porcelain aorta. A 70-year-old man with severe aortic stenosis and a porcelain aorta was referred to our center for transcatheter aortic valve replacement. Unfortunately, the patient died after the procedure because of a potential ischemic event. Therefore, we decided to fabricate three-dimensional models to evaluate the potential effects of these constructs for previous surgical planning and simulation of the transcatheter valve replacement.

Mobile Cloud Networking: Mobile Network, Compute, and Storage as One Service On-Demand

The Future Communication Architecture for Mobile Cloud Services: Mobile Cloud Networking (MCN) is a EU FP7 Large-scale Integrating Project (IP) funded by the European Commission. MCN project was launched in November 2012 for the period of 36 month. In total top-tier 19 partners from industry and academia commit to jointly establish the vision of Mobile Cloud Networking, to develop a fully cloud-based mobile communication and application platform.