The following figure shows my typical research methodology for projects like road traffic monitoring or human mobility measurements. There is a component of (i) sensing or gathering data on the phenomenon of interest, (ii) analysis of that data to gather insights, (iii) communication among sensors for coordination, and to the Cloud for transferring data and/or inferences and (iv) deployment of system prototypes, that implement the first three components, for long term evaluation. Communications can also be encrypted, if the application needs privacy.
Since what sensor or analysis methods will work in a given scenario is unknown, I typically use an iterative designing, experimentation and re-designing process. Projects start with offline analysis, where candidate methods are evaluated for accuracy and efficiency. The best method identified is implemented into a working prototype to do in-situ analysis, for demonstration and deployment. Finally, long term insights are gathered from deployments, for temporal analysis of the phenomenon under consideration.
Thus I need three kinds of collaborators in my projects -- (i) technical experts: since I combine techniques from different areas of Computer Science (CS), like embedded systems, mobile computing, wireless networks, computer vision, applied machine learning, deep learning, operating systems and cryptography, I need experts to explain my necessity and learn from them the relevant topics in a particular area of CS, (ii) domain experts: since the problems I work on are at the intersection of Information Technology and society, I need help from goverment and private organizations to deploy my prototypes, like traffic control authorities for deploying my road sensors or shopping mall and airport authorities for human mobility measurements, (iii) co-workers: a typical system project is large and working as a team helps in faster progress. All my projects have been in teams with undergradute, Masters and PhD students and interns, where I have contributed 50-95% in terms of actual hands-on work.