The Automated Street Light Measurement System (ASLMS) (see Part 2) is, in its crudest form, an in-vehicle data acquisition system that outputs geospatial lighting statistics. In its more complex form, the ASLMS comprises:
a data fusion engine that integrates data from multi-modality sensors into a consistent, accurate, and useful representation, and
a data analytics engine that examines data with the purpose of drawing conclusions about that information
When we built ASLMS, the Internet-of-Things (IoT) was merely a shadow and the above was really just a data acquisition box. Today, new buzzwords are created simply to confuse the masses. Our advice is to stay grounded, keep to the fundamentals and try not be misled by marketing spiel.
When we started designing and building Litesense, IoT became a force to be reckoned with and we embraced the technological disruptions. Our transition from ASLMS to Litesense was prudent (see Figure 1) while trying our best to adhere to the expand/contract pattern (The rule is that you never change existing objects all at once. Instead, divide the changes into reversible steps.) Each transition was designed to be a viable product and this is illustrated nicely in Figure 2.
Litesense, like its predecessors, can be used at normal vehicular speeds while collecting and transmitting wirelessly high granularity sensor data. This meant that the platform needed to be performant and reliable (see Figure 3) across all modules required of an end-to-end streaming system. Fast forward two years, tons of learning and less number of sleepless nights, Litesense was commissioned (see Figures 4 and 5).
By adhering to the Rule of Modularity (see 17 Unix Rules), we have taken the liberty to promote the Litesense IoT platform into the Cosmiqo IoT platform.
Through Litesense, the Cosmiqo team (see Figure 6) has gained a deep understanding of sensing platforms and analytics engines. Till the next project.