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Smart irrigation automation for Stevens campus — monitoring, controlling, and optimizing water delivery through IoT sensors and intelligent scheduling.
💧 Save Water🔔 Leak Alerts📊 Usage Data🔗 Campus Integration
What We Are Controlling
Rain Bird System Architecture
Not one product — a modular system of components our hub must control
🎛
Controller (What We Replace)
Rain Bird's ESP-ME3 is the brain — it runs schedules and opens valves. Our Raspberry Pi hub replaces this scheduling logic entirely, sending commands via the LNK2 WiFi module instead of the manual timer.
LNK2 local APIWe own the logic
🚰
Valves (What We Switch)
One solenoid valve per zone, each running on a 24VAC signal. Our Raspberry Pi triggers these through relay channels connected to the ESP-ME3 — one relay output per irrigation zone.
24VAC signal1 relay per zone
📐
Zones (What We Schedule)
Each zone covers a distinct area and runs independently. Our automation assigns each zone its own watering window, moisture threshold, and sensor data — replacing the static timer schedule with dynamic logic.
Dynamic schedulingSensor-driven
🔗
Automation Control Chain
Soil & Rain Sensors
→
Raspberry Pi Hub + Logic
→
LNK2 Local HTTP API
→
ESP-ME3 Controller
→
Solenoid Valves Zones A – D
System Architecture · EWB-SIT 2026
Smart System Challenges
Rain Bird Smart System Weaknesses
Automation-specific problems our design must solve
📡
Tech Dependency
▸Rain Bird's smart features require reliable WiFi + app availability + cloud uptime at all times
▸Connectivity failure breaks automation entirely — adds failure points that simpler systems avoid
▸Their system can't alert Facilities when timers malfunction — Dan confirmed this is a current problem
Cloud-dependentSilent failures
🧠
Steep Learning Curve
▸Smart controllers + zoning logic are not intuitive for facilities staff
▸Troubleshooting requires technical knowledge most maintenance staff don't have
▸For us as engineers: a learning opportunity. For ongoing operations: a burden we must design around
Design for non-technical users
✅
Our Solution: Local-First
▸No cloud dependency — Raspberry Pi runs all logic locally, works through WiFi outages
▸Active alert system — Email, SMS, and Slack notifications when something fails (what Dan asked for directly)
▸Touchscreen on-site — Facilities staff can see system status and override without any app or internet
Local-firstActive alertsOffline capable
🌐
Our Solution: Simple Dashboard
▸Web dashboard — intuitive, any browser, no app install required for Facilities staff
▸Visual zone map — shows what's running, sensor readings, and upcoming schedules at a glance
▸Remote override — Dan or Kurt can start/stop zones from anywhere without calling maintenance
Browser-basedRemote accessNo training needed
Smart System Challenges · EWB-SIT 2026
Our Argument
Why Our System Is Better
The technical case for a custom-engineered automation approach
🎯
Optimized for Stevens' Conditions
Rain Bird designs for generic landscapes. Our system is engineered for Stevens' exact soil type, slope, pressure, and plant mix — watering windows based on real sensor data, not a national average template.
Site-specificSensor-drivenNot generic
⬇
Reduced Complexity = Fewer Failures
Fewer zones, simpler piping, fewer valves than a Rain Bird over-spec'd system. More components = more failure points. Our lean design means less to break and easier maintenance.
We model flow rates, calculate pressure losses, and optimize layouts — actual hydraulics and systems engineering, not just an installation job.
HydraulicsSystems design
🔧
Upgrade Flexibility
Add sensors, swap emitter types, extend to more zones. Our open architecture means no vendor lock-in — ever.
Add sensors laterNo lock-in
📚
Learning + Resume Value
Real-world IoT networking, embedded systems, hydraulics, and water conservation engineering — directly applicable to environmental and systems engineering careers.
IoT systemsWater conservation
🏆
Bottom line: Rain Bird is proven but designed for broad use cases with contractor dependency built in. Our custom automation is cheaper, simpler, and more technically optimized for Stevens — and directly solves what Dan asked for: active alerts, system visibility, and remote control.
Why Our System Wins · EWB-SIT 2026
Network Layer
IoT Communication Options
Three protocols evaluated for connecting sensors to the central hub across campus infrastructure.
📡
LoRa / LoRaWAN
15–20 km range, low power. Penetrates walls, buildings, and underground structures — ideal for buried pipe sensors.
★ Recommended
🕸
Zigbee
Mesh networking, up to 65,000 nodes. Self-healing, low power. Shorter range but scales well for dense deployments.
Scalable
📶
Standard Wi-Fi
Leverages existing Stevens campus network. Simpler setup but dependent on coverage near mechanical rooms.
Coverage Dependent
Hardware Strategy
Sensor Design Options
Two approaches: building sensors in-house or purchasing pre-made solutions.
Factor
🔧 DIY / In-House
📦 Pre-Made
Cost
Lower — ESP32 + Arduino (~$5–25)
Higher — Seeed / Iskrasonic ($30–200+)
Customization
Fully adaptable to our use case
Fixed feature set, limited modification
Power
Solar panel integration possible
Proprietary batteries or wired power
Setup
Higher effort — code + hardware assembly
Lower — plug-and-play protocols
Reliability
Depends on build quality and testing
Commercially validated, warranty support
Sensing Layer
Sensor Types & Use Cases
Four core sensor types power intelligent irrigation decision-making.
🌱
Soil Moisture
Measures water content per zone. Drives watering decisions — prevents over and under-irrigation.
Primary driver
🔧
Leak Detection
Monitors pipe integrity in real time. Alerts Facilities instantly when a break is detected.
Facilities alert
🌡
Temp & Humidity
Tracks ambient conditions to calculate optimal water volume for current weather.
Weather-aware
☀
Sunlight Intensity
Determines best watering window — after dark — to maximize absorption and minimize evaporation.
Timing optimizer
⚡API Alternative: Temp, humidity & sunlight data can use the National Weather Service free API — cheaper but internet-dependent and less locally accurate.
Control Layer
Control System & User Interface
A Raspberry Pi central hub collects sensor data and controls irrigation valves via relay switches.
🖥 Raspberry Pi Hub
Creates a mesh network polling all sensors. Saves readings to a local database. Connects via Ethernet or Wi-Fi for remote access.
⚡ Relay Control
GPIO-connected relays interface with existing sprinkler valves. Separate microcontroller fallback if no on-site internet.
💾 Local Database
All sensor readings logged locally. Enables historical water usage analysis and full offline operation during outages.
📟 Touchscreen / 🌐 Dashboard
On-site touchscreen needs no internet. Web dashboard allows Facilities to adjust schedules and get live alerts remotely.
System Architecture
Control System Map
Water Today
142 gal
↓ 18% vs avg
Avg Soil Moisture
67%
Optimal range
Temperature
58°F
Humidity 72%
System Status
Active
No leak alerts
Active sprinkler
Idle sprinkler
Soil sensor
Light sensor
Leak sensor
Alerting
Alert System Options
When the system detects a leak or malfunction, it automatically notifies the right people.
📧
Email
Automated alerts to predefined recipients with a dashboard link for instant status viewing.
💬
SMS
Text notification to a predefined number. Fastest way to reach Facilities without requiring app access.
🗨
Slack / Discord
Posts to a dedicated channel. Ideal for EWB team monitoring without individual email chains.
🔗Any alert can include a dashboard link for live sensor readings — configurable as a public read-only view for Facilities staff.
Action Items
Next Steps & Open Questions
Key decisions to resolve before end of Phase 2 — April 29, 2026.
1
Decide communication protocol — LoRa vs. Zigbee vs. Wi-Fi based on coverage at Morton/Kidde mechanical room.
2
Finalize sensor approach — DIY (ESP32 + Arduino) vs. pre-made vs. hybrid strategy per sensor type.
3
Confirm Wi-Fi availability — Ask Dan/Kurt whether mechanical room near Morton/Kidde has usable Wi-Fi.
4
Determine flow rate — Measure or request water source flow rate at Morton/Kidde for design team sizing.
5
Sync with Irrigation Design Team — Coordinate valve interfaces with Claire, Athena, Kylie & Leilani before April 8.