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.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Passive wireless sensor systems can recognize activites of daily living

The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Visualizing the Simulation of 3-D Underwater Sensor Networks

The majority of sensor network research deals with land-based networks, which are essentially two-dimensional, and thus the majority of simulation and animation tools also only handle such networks. Underwater sensor networks on the other hand, are essentially 3D networks because the depth at which a sensor node is located needs to be considered as well. Due to that additional dimension, specialized tools need to be used when conducting simulations for experimentation. The School of Engineering’s Underwater Sensor Network (UWSN) lab is conducting research on underwater sensor networks and requires simulation tools for 3D networks. The lab has extended NS-2, a widely used network simulator, so that it can simulate three-dimensional networks. However, NAM, a widely used network animator, currently only supports two-dimensional networks and no extensions have been implemented to give it three-dimensional capabilities. In this project, we develop a network visualization tool that functions similarly to NAM but is able to render network environments in full 3-D. It is able to take as input a NS-2 trace file (the same file taken as input by NAM), create the environment, position the sensor nodes, and animate the events of the simulation. Further, the visualization tool is easy to use, especially friendly to NAM users, as it is designed to follow the interfaces and functions similar to NAM. So far, the development has fulfilled the basic functionality. Future work includes fully functional capabilities for visualization and much improved user interfaces.

Evaluation of elevator shafts as a pathway for fungal spores and particles to enter a hospital housing immuno-compromised patients

Background. It is estimated that hospitals spend between 28 and 33 billion dollars per year as a result of hospital-acquired infections. (Scott, 2009) The costs continue to rise despite the guidance and controls provided by hospital infection control staff to reduce patient exposures to fungal spores and other infectious agents. With all processes and controls in place, the vented elevator shaft represents an unprotected opening from the top of the building to the lower floors. The hypothesis for this prospective study is that there is a positive correlation between the number of Penicillium/Aspergillus-like spores, Cladosporium, ascospores, basidiospores in spores/m3 as individual spore categories found in the hoistway vent of an elevator shaft and the levels of the same spores, sampled near-simultaneously in the outdoor intake of the elevator shaft. Specific aims of this study include determining if external Penicillium/Aspergillus-like spores are entering the healthcare facility via the elevator shaft and hoistway vents. Additional aims include determining levels of Penicillium/Aspergillus-like spores outdoors, in the elevator shafts, and indoors in areas possibly affected by elevator shaft air; and, finally, to evaluate whether any effect is observed due to the installation of a hoistway vent damper, installed serendipitously during this study. ^ Methods. Between April 2010 and September 2010, a total of 3,521 air samples were collected, including 363 spore trap samples analyzed microscopically for seven spore types, and polymerase chain reaction analyses on 254 air samples. 2178 particle count measurements, 363 temperature readings and 363 relative humidity readings were also obtained from 7 different locations potentially related to the path of air travel inside and near a centrally-located and representative elevator shaft. ^ Results. Mean Penicillium/Aspergillus-like spore values were higher outside the building (530 spores/m3 of air) than inside the hoistway (22.8 spores/m3) during the six month study. Mean values inside the hospital were lower than outside throughout the study, ranging from 15 to 73 spores/m3 of air. Mean Penicillium/Aspergillus-like spore counts inside the hoistway decreased from 40.1 spores/m3 of air to 9 spores/m3 of air following the installation of a back draft damper between the outside air and the elevator shaft. Comparison of samples collected outside the building and inside the hoistway vent prior to installing the damper indicated a strong positive correlation (Spearman's Rho=0.8008, p=0.0001). The similar comparison following the damper installation indicated a moderate non-significant inverse correlation (Spearman's rho = −0.2795, p=0.1347). ^ Conclusion. Elevator shafts are one pathway for mold spores to enter a healthcare facility. A significant correlation was detected between spores and particle counts inside the hoistway and outside prior to changes in the ventilation system. The insertion of the back draft damper appeared to lower the spore counts inside the hoistway and inside the building. The mold spore counts in air outside the study building were higher in the period following the damper installation while the levels inside the hoistway and hospital decreased. Cladosporium and Penicillium/Aspergillus -like spores provided a method for evaluating indoor air quality as a natural tracer from outside the building to inside the building. Ascospores and basidiospores were not a valuable tracer due to low levels of detection during this study. ^ Installation of a back draft damper provides additional protection for the indoor environment of a hospital or healthcare facility, including in particular patients who may be immunocompromised. Current design standards and references do not require the installation of a back draft damper, but evaluation of adding language to relevant building codes should be considered. The data indicate a reduction in levels of Penicillium/Aspergillus -like spores, particle counts and a reduction in relative humidity inside of the elevator shaft after damper installation.^

Multi-sensor global compilation of mid-Holocene sea surface temperatures, based on Mg/Ca and alkenone palaeothermometry and reconstructions obtained using planktonic foraminifera and organic-walled dinoflagellate cyst

We present and examine a multi-sensor global compilation of mid-Holocene (MH) sea surface temperatures (SST), based on Mg/Ca and alkenone palaeothermometry and reconstructions obtained using planktonic foraminifera and organic-walled dinoflagellate cyst census counts. We assess the uncertainties originating from using different methodologies and evaluate the potential of MH SST reconstructions as a benchmark for climate-model simulations. The comparison between different analytical approaches (time frame, baseline climate) shows the choice of time window for the MH has a negligible effect on the reconstructed SST pattern, but the choice of baseline climate affects both the magnitude and spatial pattern of the reconstructed SSTs. Comparison of the SST reconstructions made using different sensors shows significant discrepancies at a regional scale, with uncertainties often exceeding the reconstructed SST anomaly. Apparent patterns in SST may largely be a reflection of the use of different sensors in different regions. Overall, the uncertainties associated with the SST reconstructions are generally larger than the MH anomalies. Thus, the SST data currently available cannot serve as a target for benchmarking model simulations.

Near-bottom zooplankton aggregations in Kongsfjorden with link to images from the optical zooplankton sensor MOKI

Near-bottom zooplankton communities have rarely been studied despite numerous reports of high zooplankton concentrations, probably due to methodological constraints. In Kongsfjorden, Svalbard, the near-bottom layer was studied for the first time by combining daytime deployments of a remotely operated vehicle (ROV), the optical zooplankton sensor moored on-sight key species investigation (MOKI), and Tucker trawl sampling. ROV data from the fjord entrance and the inner fjord showed high near-bottom abundances of euphausiids with a mean concentration of 17.3 ± 3.5 n/100 m**3. With the MOKI system, we observed varying numbers of euphausiids, amphipods, chaetognaths, and copepods on the seafloor at six stations. Light-induced zooplankton swarms reached densities in the order of 90,000 (euphausiids), 120,000 (amphipods), and 470,000 ind/m**3 (chaetognaths), whereas older copepodids of Calanus hyperboreus and C. glacialis did not respond to light. They were abundant at the seafloor and 5 m above and showed maximum abundance of 65,000 ind/m**3. Tucker trawl data provided an overview of the seasonal vertical distribution of euphausiids. The most abundant species Thysanoessa inermis reached near-bottom concentrations of 270 ind/m**3. Regional distribution was neither related to depth nor to location in the fjord. The taxa observed were all part of the pelagic community. Our observations suggest the presence of near-bottom macrozooplankton also in other regions and challenge the current view of bentho-pelagic coupling. Neglecting this community may cause severe underestimates of the stock of elagic zooplankton, especially predatory species, which link secondary production with higher trophic levels.