1. World Forum on Internet of Things
Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications
Muhammad Intizar Ali
World Forum on Internet of Things
Reston US
14 December 2016

2. Problem Statement High variability of data in the agri-food domain.
Data combination and Information fusion are not easy.
Integration of data to existing platforms is not easy either.
Description of data is very poor – lack of semantics.
High volumes of data – low availability of big data real-time analytics tools.
Big data needs to be harnessed!

3. Motivation
Precision agriculture improves productivity increasing yields and profitability, reducing the impact on the environment.
The vision of smart farming - real-time data gathering, processing and analysis, as well as automation technologies on the farming procedures.
Big agri-data availability.
Need for large-scale frameworks that harness sensor/automation technologies and data analytics, to help farmers protect their crops/livestock and improve overall production.
Internet of Things (IoT) is a perfect match for smart farming due to its highly interoperable, scalable, pervasive and open nature.

4. Potential Benefits of IoT in Agriculture
Seamless connectivity and advanced interoperability by means of open standards.
Easier collaboration among actors, components, devices, processes and platforms.
Reduced risk of vendor lock-in.
Interoperability between machinery and sensing/automation systems from different companies, easier adapted to the existing farm’s smart system
Easier data exchange among different, heterogeneous components.
Increased automation with less effort by employing internet standards.
Seamless integration of sensing data with existing farm management apps.
Smart farming technologies combined easily with available relevant web services such as weather forecasting for better planning and decision making.
Possibility of more personalized feedback and services to the farmers, by integrating information also from their personal phones and calendars.

5. Agri-IoT: Concept A highly customizable online platform for
IoT- based innovative data analytics solutions,
performing reasoning based on real- time
streams of data coming from a variety of sources.
– Sensors and sensory systems deployed in the smart farm.
– Surveillance cameras deployed in various farm locations.
– Hyperspectral images from unmanned aerial vehicles.
– Online weather forecasting/prediction services.
– Online social media streams
– Information, warning and alerts from relevant governmental organizations
– Regulations from international recognizable farming standards.
– Online social media streams such as tweets from Twitter and posts from Facebook, for fast identification of events (e.g. hazards, earthquakes, floods).
– Information, warning and alerts from relevant governmental organizations, such as the Ministry of Agriculture
Regulations from international recognizable farming standards such as Reg. EU 834/2007 for Organic Products and Global G.A.P

6. Agri-IoT: Architecture
Multi-layered architecture
Lower level (device, communication planes)
Intermediate layers (data, data analytics)
Higher layers (application, end user planes)

7. Agri-IoT: Architecture
Each software component acts as a single entity, with its own open API.
Flexible distributed architecture.
Different applications can integrate different components from different layers based on their specific needs.
Plug and play components according to particular agricultural applications’ requirements.
AgriPulse can integrate, manipulate and process a huge variety of streaming data in a flexible and extensible way, using standardized methods for data acquisition following IoT principles employing semantics.

8. Agri-IoT: Main Components
Data wrapper: (source: CityPulse)
Device manager: (source: FIWARE IoT Back-end)
Discovery module: (source: FIWARE IoT Edge)
Data aggregation: (source: IoT-A)
Data federation: (source: CityPulse)
Event detection: (source: CityPulse)
Real-time adaptive reasoning: (source: CityPulse)
External agent: (source: Agri-IoT)
Dashboard: (source: ThingSpeak, freeboard)
Mobile Apps: (source: MapYourMeal, FoodLoop)
Knowledge base: (source: OpenIoT)
Data wrapper: (source: CityPulse) offers a generic way to describe characteristics of sensors using sensory meta- data, containing general information about the data stream. A semantic annotation module enables to annotate the parsed sensory data.
Device manager: (source: FIWARE IoT Back-end) automatically manages IoT devices, removing the need for human operators, providing the necessary tools for autonomic management processes to enforce decisions at a later stage. Manages device identity and authorization, considers reliability of data streams (e.g. real-time checking if values fall into specific limits) and fault recovery.
Discovery module: (source: FIWARE IoT Edge) ensures scalable registration and discovery of IoT devices and services in real-time, in a plug and play way. These devices can be either located at the same physical space (e.g. inside the farm) or remotely, accessed through the internet/web.
Data aggregation: (source: IoT-A) deals with large volumes of data using time series analysis and data compression techniques to reduce the size of raw sensory observations delivered by the data wrappers.
Data federation: (source: CityPulse) answers users’ queries, e.g. the amount of fertilizer needed to apply over some area. This component first finds relevant streams according to the requirements specified in the request. Then, it translates users’ requests into RDF Stream Processing (RSP) queries and evaluates the queries to obtain results. As IoT-based smart farming involves fast changing data from real-world sensors and online services, as well as real-time processing and analytics based on semantics, we argue that RSP is an appropriate technology to be employed. RDF-based reasoning is supported through CSPARQL and CQELS, which are RDF query languages managing continuous data streams.
Event detection: (source: CityPulse) provides tools for processing annotated and aggregated data streams to obtain farm events, such as need for irrigation, sick animals or pest identification in crops.
Real-time adaptive reasoning: (source: CityPulse) takes into account farmer’s preferences and dynamic contextual farm-related information (represented by real-time events), in order to provide optimal decision support in real-time.
Provides reliable, accurate and fast decision support to the farmer, based on farm’s conditions as measured by the smart sensing technology available.
External agent: (source: Agri-IoT in-house developed) addresses interoperability, device heterogeneity, data handling and protocol adaptation. Plays an important role for virtualising objects, services, methods and processes, considering user’s identity and authorization.
Dashboard: (source: ThingSpeak, freeboard) provides immediate and intuitive visual access to the results of process- ing and analysis of data and events.
Mobile Apps: (source: MapYourMeal, FoodLoop) are built on top of the other components, similarly to the dash- board, and use their APIs to offer various services to their mobile users, either to the farmers for real-time information and fast decision making, or to the consumers and transport agents at the sales points for more transparency.
Knowledge base: (source: OpenIoT) provides service metadata for sensor/data stream discovery.

9. Semantic Annotation
Data streams annotated using lightweight information models developed on top of well- known models, such as SSN and OWL-S
Related to agriculture, use of AGROVOC, the Agricultural Ontology Service (AOS) and AgOnt.
RDF Stream Processing (RSP) techniques (e.g. CSPARQL and CQELS) to easily process heterogeneous data streams.
SPARQL-like query languages to evaluate query patterns over static/dynamic knowledge.

10. Evaluation Scenario A: Fertility management of dairy cows
A farm has 1,000 cows.
Each cow with a wearable sensor attached, measuring periodically its heat status
Observations (annotated with SSN) are sent to a dedicated sensor data stream every second.
To monitor cow fertility, farmers need to be notified about the heat of cows which are 18 to 30 months old.
Dynamic CQELS/CSPARQL queries to consider in real-time the cows’ fertility potential.

11. Evaluation Scenario A: Fertility management of dairy cows
The results show that, although CQELS query performance has dropped significantly when handling over 700 queries/sensor streams, it is in general better at handling multiple queries than CSPARQL, in terms of query latency and memory consumption. In our experiment, CSPARQL failed to handle more than 500 queries stably, i.e., it may stop producing results when handling a larger number of queries.
What is –m? In the experiment above, fertility detection task is actually carried out in two steps: a static discovery step to identify relevant streams and then a dynamic query step. The use of ontologies improves the interoperability for both static and dynamic information. Moreover, by leveraging RSP the static discovery process, which is a query on stream metadata, can be integrated within the dynamic stream query. Hence, the first step can be skipped and we can evaluate the queries directly over a merged sensor stream (i.e. instead of subscribing to individual sensor streams, we listen to all sensor updates and merge them into a single stream).
Using a merged query reduces system complexity by com- bining static discovery with dynamic monitoring. However, an overhead is introduced by subscribing to all, including irrelevant, sensor streams. CSPARQL copes very well with the merged queries.
CQELS Performance
CSPARQL Performance

12. Evaluation Scenario B: Soil fertility for crop cultivation
Soil composition is an indicator for measuring the quality and conditions of the soil
We considered a set of soil sensors measuring soil index (for soil composition), salinity and moisture.
Farmers are using Agri-IoT to know when it is a good time to cultivate.
Whenever half of the total sensors deployed in the same land (assuming a heterogeneous land in edaphological terms) indicate conditions appropriate for cultivation, then a notification is delivered to the farmers.
Only tested merged queries because of the need of aggregating functions
Time window of 10 seconds, sensor update frequency of 1.0 Hz
Soil composition (phosphorus, potassium and magnesium), salinity, and moisture levels.
Experimented sensor frequencies are relatively high, to put the system under stress. In a realistic scenario of monitoring soil fertility, frequencies could be in the range of few hours up to some days.

13. Evaluation Scenario B: Soil fertility for crop cultivation
Only showing results for CSPARQL, since CQELS was not able to stably handle queries after minutes involving more than 150 sensors.
Higher sensor frequency introduces more latency and likelihood of causing unstable states for the CSPARQL engine, without significant effect on memory consumption.
CSPARQL Latency
CSPARQL Memory

14. Conclusion
Agri-IoT offers satisfactory performance even in demanding scenarios with large numbers of simultaneous data streams and sensors.
Can be used in medium-to-large farms ( sensors deployed at the field) for performing real-time stream processing and reasoning, based on IoT and semantic web technologies
Can help farmers in decision making and fast reactions to events happening.
Can integrate data streams coming from varied, heterogeneous data streams.
Semantic technologies and web linked data can bring increased data and software interoperability.
CQELS has performed better in terms of query latency and memory consumption, but CSPARQL is more scalable.
Limitations of Agri-IoT include dynamicity, autonomy and full adaptability to heterogeneity.

15. Future Work
On-demand discovery, integration and complex event processing could become optimized to provide robust real-time analytic solutions over heterogeneous data streams originating from agricultural sensors.
Agri-IoT flexible and adaptable to every farming scenario.
True plug and play support of heterogeneous sensors, information and solutions.
Fully involve semantic web technologies for data integration, stream processing and reasoning, including mechanisms of complex event service reusability.
Improve RSP performance for concurrent queries, high-stream rates and large triple window sizes (e.g. by realizing a distributed RSP engine), using the experimental results of this paper as baseline.
Improve existing agricultural ontologies and semantic models
Develop a complete knowledge representation framework in the form of linked data for IoT streams

16. Muhammad Intizar Ali World Forum on Internet of Things
Thank you!
Muhammad Intizar Ali
World Forum on Internet of Things