Multisensory Agricultural Monitoring Platform

Transcription

1 Getting crop infestation knowledge data from central disease server or distributed servers Make a call and follow instructions Web Server (PC Debian GNU/Linux) Get required disease infomation and Store Call information Distributed Servers Multi modal PBX/Voice server Database Server (PC Debian GNU/Linux) Get Crop Status via SMS and Voice Call SMS Gateway Multisensory Agricultural Monitoring Platform PI: Ketan Rajawat (EE) Co-PI: Rajesh Hegde (EE)

2 Goals Agricultural practice in India is characterized by a large pool of local knowledge Communities thrive on local wisdom about dealing with local environment, infestation, etc. However: central planners often miss this data Bridge the gap between: technical knowledge & longitudinal farm experience Goal: agricultural monitoring, data collection, presentation, trend mapping, visualization

3 Objective Multi-sensor agricultural monitoring and information dissemination platform ZigBee/WiFi gateway WiFi communication range Sensor nodes Data Mule s movement path Data Mule (Farmer) ZigBee/GPRS gateway Cell phone network coverage

4 Deliverables Identification and deployment of affordable and usable sensors Architecture: mobile data collection agent Backend database + data analytics codebase Web portal with maps & alert services

5 Motivation Enable epidemiological studies at central level Allow data scientists to spot trends General early warning signals on movement of infestations and impact of weather conditions Overall: enhancing P2G and G2P

6 Novel Aspects Energy & bandwidth efficient data collection 1 Novel strategies for distributed storage 2 Succinct & meaningful data visualization 3 Filtering, data cleansing, & outlier rejection Path planning Beyond Predictive models for diseases Real-time query resolution 1. K. Rajawat, A. Cano, and G. B. Giannakis, Network-compressive coding for wireless sensors with correlated data, IEEE Transactions on Wireless Communications, vol. 11, no. 12, pp , Dec Lakshmi J Mohan, Udaya Parampalliy, Ketan Rajawat, and Aaron Harwood, An optimization design framework for repair efficient storage codes in geo-diverse clusters, submitted to IEEE Globecom K. Rajawat and S. Kumar, "Stochastic Multidimensional Scaling," IEEE Transactions on Signal and Information Processing over Networks - Special Issue on Distributed Information Processing in Social Networks, vol , no. 2, pp. June 2017.

7 WP1: Sensing technologies Investigation & testing of sensing technologies Agricultural sensors for use in Indian context Air temperature Air relative humidity Solar radiation Wind velocity Rainfall Soil salinity Soil hydrolic conductivity Leaf wetness Stomatal conductance Soil temperature Soil moisture content Soil ph Soil bulk density Cameras Field testing of low-cost sensors

8 WP2: Architectural issues Design of sensor-gateway data collection Energy efficient + low power protocols Sleep-wake protocols for energy conservation Lab testing of appropriate protocols Implementation on test bed in lab Reliability and stress testing Lifetime maximization Operation under harsh conditions

9 WP3: Data Mule Algorithm design for low-cost data collection Data compression Path planning Distributed storage Implementation of data fetching protocol At WSN lab in EE dept. Ensure robustness to delays and losses Field testing S. Kumar, R. Jain, and K. Rajawat, "Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM," IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 1, pp , Mar

10 WP4: Backend Design Remote database server Pulling data from data mule Pushing queries to sensors through data mule Data analytics Storage, filtering, interpolation, prediction, outlier rejection, data cleansing, denoising Visualization & Mapping Real-time algorithms for query processing

11 WP5: Web Portal Software specifications for web portal Multimodal information dissemination tools Text & map from backend Automated audio text messages Design & testing User-aided testing WP6: Integration & field testing

12 Deliverables Year 1: Investigation and field testing of agricultural monitoring sensors Year 2: Implementation and testing of various routing, data collection, data analytics, and visualization algorithms Year 3: Field testing of the routing and data collection algorithms Year 4: Backend ready, allowing collection, storage, and processing of data Year 5: Web portal online and ready for deployment SWP Year 1 Year 2 Year 3 Year 4 Year

13 Specific Technology outcome at Y1 We expect to have completed the following at the end of the first year: Comparative analysis of agricultural sensors available in the market and their usability in the Indian context (field testing) Various algorithms for routing and data collection (SWP2.1 and SWP3.1) Thank You