Project III
Semester VI · BCA 356 · 3 credits
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chapters done
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The syllabus asks for
- What changes in Project III
- The running example: KhetSense
- The four-layer IoT architecture
- Choosing the sensors and the board
- Requirements as user stories and measurable checks
- Eight reasons sensor projects fail, and the cure
Key points
- A sensor project adds noisy real-world data, batteries, weak networks and costly failures to the concerns of Project II.
- KhetSense is the running example; its objectives are measurable, including an accuracy target (O2) and a budget (O5).
- The four-layer architecture separates sensing, edge processing, cloud storage and the phone app.
- Hardware is chosen from requirements and checked against the budget; the plan is revised with numbers.
- The eight failure causes (calibration, battery, gaps, noise, weather, offline, clocks, desk-only testing) guide Units 2 to 5.
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The syllabus asks for
- From soil to count: the ADC
- Calibration: turning counts into moisture
- Noise and filters
- Messaging: publish-subscribe with retry
- How long does the battery last?
- Eight rules for reliable sensor data
Key points
- The ADC turns a voltage into a count; calibration turns the count into a useful unit and gives a measurable error.
- The simulated calibration reached 0.32 points of mean error on new samples; the uncalibrated vendor index was 36 points off.
- A moving median removed spikes best (0.58 against 1.67 for raw data).
- QoS 1 with ids delivered 96 of 96 readings on a link that lost 30 percent of packets; QoS 0 delivered 64.
- Batching the radio lengthened battery life from 87.8 to 165.1 days in the calculation.
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The syllabus asks for
- The pipeline from message to advice
- Storing readings safely
- Cleaning a series
- Anomaly detection and silent nodes
- Forecasting tomorrow's moisture
- From forecast to advice
- Eight uses of data analysis in KhetSense
Key points
- The pipeline validates, stores once, cleans, analyses and then advises; each step is testable.
- Constraints in the database refused 78 duplicates and the impossible value; 8 to 9 percent of readings were missing and recorded as such.
- The cleaning rules flagged exactly the three true spikes and left the long gap missing.
- Stuck, silent and jumping nodes are detected by three simple rules, with rain used to explain jumps.
- The exponential forecast beat persistence (0.58 against 1.31 points) on a small simulated test, and the rule engine turns it into four plain messages.
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The syllabus asks for
- Choosing the client technology
- The screens
- Offline-first synchronisation
- Showing stale data honestly
- Permissions, privacy and security on the phone
- Testing the client logic
- Eight features of a good field-ready mobile client
Key points
- Native Android with Room and a background worker suits field users best; Flutter or a PWA are alternatives.
- Three screens (home advice, plot detail, alerts) answer three questions, and wireframes come before code.
- Offline-first design needs a queue, retry and idempotence; the simulation stored 6 unique actions and ignored 5 duplicates.
- Stale data must be labelled, and advice must be withheld when data are older than 2 hours.
- Test the logic in code and the screens on a device matrix; turn each fixed bug into a permanent test.
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The syllabus asks for
- The field-test plan
- The report
- The demo: ten minutes, with a plan B
- Honesty, consent and ethics
- Twelve viva questions with short model answers
- An illustrative marking rubric
- Eight habits of teams that finish Project III well
Key points
- A field test turns claims into evidence: the simulated 48-hour log passed four of five objectives and honestly failed O1.
- The failed objective is diagnosed (far node, weak link) and a fix is proposed; hiding it would be dishonest.
- The report map highlights the sensor-specific chapters; the page budget came to 80 pages with 62 percent technical content.
- The demo is rehearsed with a plan B for every live step, including an offline run.
- Honesty, consent, licensing and the law apply to sensor data and photographs as much as to code.
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The paper
TU assessment: 60% final examination set by the university, 40% internal (assignments, lab work, attendance). Confirm the split in your college's notice.