The project requirements are
Elias, Andy Rosales, et al. "Where's the Bear?-Automating Wildlife Image Processing Using IoT and Edge Cloud Systems." Internet-of-Things Design and Implementation (IoTDI), 2017 IEEE/ACM Second International Conference on. IEEE, 2017.
This work can easily be extended in several ways. First, the original system only identified images with a single species. Identifying images with multiple species (which are rare but useful) would be a great improvement. Secondly, the ecologists who operate the camera traps would like to use them to count (e.g. for population estimates). They would REALLY like counts separated by age group (e.g. youngsters versus adults). The original system does no counting. Thirdly (and this is probably hard) the ecologists would like to know if it is possible to identify individual animals. IN addition, we would like to understrand whether there is a relationship (temporal or spatial) between image capture and environmental conditions (meteorlogical, seasonal, drought, etc.) In partiocular, to what degree is it possible to predict when an image will be taken of a given species? The authors and collaborators as well as various data sets are available in the area as resources for this project. You can find out more here but you will need a UCSB NetID to access the images.
Nanoclimate forecasting: One big area of interest for IoT and cloud is agriculture (as evidenced by Microsoft's Farmbeats project). Estimating meteorlogical conditions at a fine-grained level is turning out to be an important capability that IoT for agriculture can provide. For example, agricultural engineers and scientists believe that it is possible to use highly localized temperature and humidity measurements to optimize crop management (e.g. frost prevention, differential irrigation scheduling, etc.) However it is often infeasible to instrument growing areas with densely distributed sensors. Doing so often carries a large infrastructure cost (both in terms of installation and maintenance) as well as the potential for interfering with farm operations. Thus nanoclimate sensing and forecasting requires the heavy use of analytics to make inferences and predictions.
For example, at one orchard in the Central Valley of California, the growers would like to use a data from few carefully placed temperature sensors with the plethora of mesoscale and microclimate meterological data to make fine-grained inferebces and predictions of temperature and humidity at meter scale.
Another example project would be to try and determine the specific sets of data and data-fusion analytics that can infer the temperature in an arbitrary square meter of the orchard. For example, knowing the temperature at one location, the prevailing wind, and the solar radiocity, it is possible to infer the temperature at another location near by. How accurately can this inference be made? Where should sensors be placed? What is the minimum sensor to error ratio that is possible?
Forecasting (predicting a future temperature value) is another imporrtant area that is related to the inference problem. For frost prevention, for example, an inference is sufficient to allow the system to send an alert when frost is immenent, but it would be better to predict that it will occur several hours into the future.
All of the above are also true for inferring and forecasting humidity at the nanoclimate level. Our group has access to an instrumented orchard and historical sensor data to support this project. In addition, this paper describes some early attempts at nanoclimate temperature inferences using internal CPU temperatures as explanatory variables.
Both of these examples are intended to stimulate your imagination about what is possible (although they are both availabnle as projects for this class as well). What is key, though, is that the solution is an "end-to-end" solution -- one that addresses a real-world problem using a combination of infrastructure for cloud/edge/IoT and analytics. To succeed, you must often devlop a novel technology or amalgamate a set of existing technologies in an entirely new way.
For example, we have developed a multi-scale, distributed Functions as a Service (FaaS) infrastructure called CSPOT
CSPOT: A Serverless Platform of Things
CSPOT make several new innoivations. First, it defines a common, universal "append-only" storage abstraction for FaaS programs. This abstraction is simple enough to be implementable at the microcontroler level, yet powerful enough to function as the main storage abstraction for IoT applications at the edge and in the cloud. Secondly, it uses an append-only log as its runtime system so that it leaves behind a record of causal dependency between computations. Thus, by definition it is possible to recover causal execution chains in highly scalable deployments. Thirdly, CSPOT functions are very low latency -- two orders of magnitude faster than comparable AWS or Microsoft technologies. CSPOT will soon be released as open source. There are a number of new technological advances that it could enable including
Additionally, you are free to use any other cloud platform (e.g. in the free tier) to which you can gain access. Unfortunately, we do not have class credits from the public cloud vendors for this class.