Case study
StreetSide — Intelligent Waste Management
A more efficient way to pick up trash. Work in progress.
- Timeline
- Mar 2026 — Present
- Stack
- DevOps, Front-End Development, Project Management, Algorithm Deployment
- 30%Projected savings from efficiency
- 12+Hours spent figuring out Firebase, Particle, and hosting
- 8+Marketplace sessions in front of a tough crowd (and our professor)
Most trash routes are static. That comes from a lack of information into each dumpster. But what if you knew exactly what was happening in each dumpster on your route?
Most senior design classes are a test of what you’ve learned. They’re essentially asking you to make something that demonstrates the things you’ve learned in your classes. Ours was a startup incubator.
My team and I bounced around a few ideas, even making a miniature barebones prototype for one. Then one day, they came to me and said “We have a great idea, we should drop the one we’re currently going with,” about ten minutes before our meeting with our professor to discuss our project.
They then proceeded to explain to me an interesting problem, one that most people don’t even think about: most trash routes are fixed. This means for every route, regardless of how full its dumpsters actually are, garbage trucks will always stop at the same stops. That’s inefficient, and as an ISE, that’s unacceptable.
The Pitch and the Plan
So what if we could make something to fix that? What if we could collect information about exactly how full each dumpster is, then only route trucks there when the dumpsters are above a certain fill level?
That’s pretty much the pitch we led with. Our professor thought it was interesting, so we set out to figure out how to present it to everyone and build a plan for the next ~8 months. Through the process, we had several questions we needed to answer. How much is this data worth to waste management companies? How far does the scope extend? Would it be smarter to give them data and let them route it themselves, or set up the routing for them?
Despite that, we knew we had a great idea. It was a proper ISE project, and the value proposition was easy to explain: Optimizing routes to save money. But we had to figure out what kind of company we wanted to build: is this a routing product, a sensor company, a data company, or some combination of the three?
The Engineering
There are two pieces to this: the hardware and the software. The hardware consists of an IoT sensor that’s clipped to a dumpster. Using an ultrasonic sensor (think sonar), the device can capture fill level of its dumpster and send it to the cloud. But getting the reading was only the first part. We had more questions to answer. How will we get the battery to last long enough that the device doesn’t become more trouble than it’s worth? How will we make sure it can survive the elements? Things like this steered exactly how frequently we collected readings and forced us to decide between ultra-accurate and good enough to function for a long time. They changed our enclosure unit four times to make sure our sensor kit could survive garbage juice among other things.
On the software side, this is where the sensor connects to a broader network. Once it sends data to the cloud, we’re able to use the information from each sensor (AKA each dumpster) to create a network of coordinates that the dumpsters are located at.
That’s the core engineering of StreetSide. The rest is about making it useful. Once you know where every dumpster is and how full it is, you can start setting fill thresholds for each dumpster to know the exact point at which you should pick up from that dumpster. And once we have that, we can start making a routing algorithm to optimally hit each point, minimizing the total number of trips and saving fuel and other costs.
Beyond that, if each device is tied to a specific fleet, you can use the same algorithms but only use the data from specific dumpsters so companies can route according to only the dumpsters they service.
Today, Tomorrow, and Later
Like I said, this is a work in progress. The groundwork has been laid and we’re able to get data from our sensors. This means we can start putting the pieces together, tying the hardware to the software, starting up the data pipeline, and turning this from prototype to product.
Where we go from there is really endless. When readings start coming in and showing up on a map, that’s when you start looking at your user base. How do you make it a usable product? We’re designing a user-friendly interface so people can make the most of our data. But our big question is still something we’re thinking about. What exactly are we selling? For the purpose of a project, it’s cool to have elements of all three. But if we were truly going to the market, as our professor wants us to believe, there’s more customer discovery that needs to be done. And along with making a cool product, we need to do one more thing: make a sellable product. When we do, we think you’re gonna love it.
Most senior design classes are a test of what you’ve learned. They’re essentially asking you to make something that demonstrates the things you’ve learned in your classes. Ours was a startup incubator.
My team and I bounced around a few ideas, even making a miniature barebones prototype for one. Then one day, they came to me and said “We have a great idea, we should drop the one we’re currently going with,” about ten minutes before our meeting with our professor to discuss our project.
They then proceeded to explain to me an interesting problem, one that most people don’t even think about: most trash routes are fixed. This means for every route, regardless of how full its dumpsters actually are, garbage trucks will always stop at the same stops. That’s inefficient, and as an ISE, that’s unacceptable.
The Pitch and the Plan
So what if we could make something to fix that? What if we could collect information about exactly how full each dumpster is, then only route trucks there when the dumpsters are above a certain fill level?
That’s pretty much the pitch we led with. Our professor thought it was interesting, so we set out to figure out how to present it to everyone and build a plan for the next ~8 months. Through the process, we had several questions we needed to answer. How much is this data worth to waste management companies? How far does the scope extend? Would it be smarter to give them data and let them route it themselves, or set up the routing for them?
Despite that, we knew we had a great idea. It was a proper ISE project, and the value proposition was easy to explain: Optimizing routes to save money. But we had to figure out what kind of company we wanted to build: is this a routing product, a sensor company, a data company, or some combination of the three?
The Engineering
There are two pieces to this: the hardware and the software. The hardware consists of an IoT sensor that’s clipped to a dumpster. Using an ultrasonic sensor (think sonar), the device can capture fill level of its dumpster and send it to the cloud. But getting the reading was only the first part. We had more questions to answer. How will we get the battery to last long enough that the device doesn’t become more trouble than it’s worth? How will we make sure it can survive the elements? Things like this steered exactly how frequently we collected readings and forced us to decide between ultra-accurate and good enough to function for a long time. They changed our enclosure unit four times to make sure our sensor kit could survive garbage juice among other things.
On the software side, this is where the sensor connects to a broader network. Once it sends data to the cloud, we’re able to use the information from each sensor (AKA each dumpster) to create a network of coordinates that the dumpsters are located at.
That’s the core engineering of StreetSide. The rest is about making it useful. Once you know where every dumpster is and how full it is, you can start setting fill thresholds for each dumpster to know the exact point at which you should pick up from that dumpster. And once we have that, we can start making a routing algorithm to optimally hit each point, minimizing the total number of trips and saving fuel and other costs.
Beyond that, if each device is tied to a specific fleet, you can use the same algorithms but only use the data from specific dumpsters so companies can route according to only the dumpsters they service.
Today, Tomorrow, and Later
Like I said, this is a work in progress. The groundwork has been laid and we’re able to get data from our sensors. This means we can start putting the pieces together, tying the hardware to the software, starting up the data pipeline, and turning this from prototype to product.
Where we go from there is really endless. When readings start coming in and showing up on a map, that’s when you start looking at your user base. How do you make it a usable product? We’re designing a user-friendly interface so people can make the most of our data. But our big question is still something we’re thinking about. What exactly are we selling? For the purpose of a project, it’s cool to have elements of all three. But if we were truly going to the market, as our professor wants us to believe, there’s more customer discovery that needs to be done. And along with making a cool product, we need to do one more thing: make a sellable product. When we do, we think you’re gonna love it.
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This section is still under construction. Please try again in a few weeks.