If you can dream it, we can make it.
A list of publications that we have contributed to
The Internet of Things (IoT) age is upon us. As we look to build larger networks with more devices connected to the Internet, the need for lightweight protocols that minimize the use of both energy and computation gain popularity. One such protocol is Message Queue Telemetry Transport (MQTT). Since its introduction in 1999, it has slowly increased in use cases and gained a huge spike in popularity since it was used in the popular messaging application Facebook Messenger. In our previous works, we focused on adding geolocation to MQTT, to help modernize the protocol into the IoT age. In this paper, we build off our previous work on MQTTg and build an IoT Android Application that can pull geolocation information from the Operating System. We then use the geolocation data to create geofences to help further tailor the use cases of MQTTg.
Green communications is the practice of selecting energy efficient communications, networking technologies and products. This process is followed by minimizing resource use whenever possible in all branches of communications. In this day and age, green communication is vital to the footprint we leave on this planet as we move into a completely digital age. One such communication tool is Message Queue Transport Telemetry or MQTT which is an open source publisher/subscriber standard for M2M (Machine to Machine) communication. It is well known for its low energy and bandwidth footprint and thus makes it highly suitable for Green Internet of Things (IoT) messaging situations where power usage is at a premium or in mobile devices such as phones, embedded computers or microcontrollers. It is a perfect tool for the green communication age upon us and more specifically Green IoT. One problem however with the original MQTT protocol is that it is lacking the ability to broadcast geolocation. In today's age of IoT however, it has become more pertinent to have geolocation as part of the protocol. In this paper, we add geolocation to the MQTT protocol and offer a revised version, which we call MQTTg. We describe the protocol here and show where we are able to embed geolocation successfully. We also offer a early glimpse into an Android OS application we are developing for Open Source use.
Message Queue Transport Telemetry or MQTT is an open source publisher/subscriber standard for M2M (Machine to Machine) communication. This makes it highly suitable for Internet of Things (IoT) messaging situations where power usage is at a premium or in mobile devices such as phones, embedded computers or microcontrollers. In its original state, MQTT is lacking the ability to broadcast geolocation as part of the protocol. In today's age of IoT however, it has become more pertinent to have geolocation as part of the protocol. In this paper, we add geolocation to the MQTT protocol and offer a revised version, which we call MQTT-G. We describe the protocol here and show where we were able to embedd geolocation successfully.
Sensor networks are recently rapidly growing research area in wireless communications and distributed networks. A sensor network is a densely deployed wireless network of small, low cost sensors, which can be used in various applications like health, environmental monitoring, military, natural disaster relief, and finally gathering and sensing information in inhospitable locations to name a few. In this paper, we focus on one specific type of sensor network called MQTT, which stands for Message Queue Transport Telemetry. MQTT is an open source publisher/subscriber standard for M2M (Machine to Machine) communication. This makes it highly suitable for Internet of Things (IoT) messaging situations where power usage is at a premium or in mobile devices such as phones, embedded computers or microcontrollers. In its original state, MQTT is lacking the ability to broadcast geolocation as part of the protocol itself. In today’s age of IoT however, it has become more pertinent to have geolocat ion as part of the protocol. In this paper, we add geolocation to the MQTT protocol and offer a revised version, which we call MQTT-G. We describe the protocol here and show where we were able to embed geolocation successfully.
Accurate, high-resolution fire behavior prediction is a critical component of fire management decision-making before and during fires. Prometheus is a deterministic fire growth simulation model that was developed to help fire managers to understand the probable consequences of their decisions. It uses spatial input data on topography (slope, aspect, and elevation), fuel types, and weather to simulate fire growth by applying Huygens’ principle of wave propagation to the rate-of-spread predictions from the Canadian Forest Fire Behavior Prediction System of the Canadian Forest Fire Danger Rating System. This approach produces detailed fire perimeters at user-specified display time step intervals. Each active vertex along the perimeter has corresponding fire behavior output. Exported fire perimeters are compatible with geographic information systems. Additionally, three interpolation techniques are available to produce optional raster fire behavior outputs. This report documents the structure of the Prometheus model and an assessment of its performance. The report includes a general discussion of approaches to the modeling of fire growth simulation and explains the vector propagation technique used in Prometheus. The limitations and assumptions of applying the model, as well as the most appropriate directions for future research, are also discussed.
This report presents a brief review of fire simulation models in general, and on their various approaches to untangling in particular. We describe the mathematical foundations for our new method and give a brief overview of the programming issues that arise in implementation. Finally, we report the results obtained by the new model on a test database of fires maintained by Prometheus developers. Good performance is obtained both in terms of run times and in the ability of the code to produce realistic fire fronts without operator intervention.
The PROMETHEUS model is a spatially explicit, deterministic fire growth model, praised for being beneficial in various aspects of fire management. Our goal is to build on this success, applying statistical smoothing to alleviate some computational difficulties and to increase accuracy; a pleasant by-product is the opportunity to introduce stochasticity to the model using a residual-based block bootstrap.
This work describes a computer based technique for simulating the spread of wildland fire for heterogeneous fuel and meteorological conditions. The mathematical model is in the form of a pair of partial differential equations, and can model fuels whose fire perimeter for homogeneous conditions is any given shape, such as ellipses, double ellipses, lemniscates etc. Provided the fire does not attempt to burn into an already burnt out region, then the differential equations are easily solved and a simple method of solution is presented. To identify regions that are internal to the fire perimeter an algorithm that uses the turning number of appoint in the plane relative to the fuel perimeter is used. The algorithm is found to be reliable, and allows for the simulation of highly complex fire scenarios in a reasonable time. Ex- ample simulations are presented that involve variations in fuel, barriers, wind direction changes and multiple fires.