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Terrain Description Table (TDT) for zone B of Figure 3. Table 4

Figure 3 Terrain Description Table (TDT) for zone B of Figure 3. Table 4

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several phases which require distinct conditions depending on the type of grape: agricultural production, harvesting, crushing, fermentation, aging, and packaging. While some phases of wine- making can be supported by modern techniques, the agricultural production consisting of the cultivation of grapes and their surveil- lance is done with traditional techniques by the farmers in order to detect problems - e.g. plagues, humidity, or dryness - and ideal conditions - e.g. the Ph or sugar level. The remainder of this paper is organized as follows. Section 2 describes the economic importance of the winemaking process as a reference sector of the Spanish economy and it discusses both WSNs and other alternative monitoring strategies. Section 3 presents the related work with regard to solutions for agricultural monitoring and decision making. In Section 4 we collect the general requirements for agricultural monitoring based on WSNs. Section 5 describes our proposal for a novel methodology designed to support applications development with the purpose of improving the agri- cultural production. Section 6 underscores our main contributions. We conclude in Section 7 by summarizing our work.
several phases which require distinct conditions depending on the type of grape: agricultural production, harvesting, crushing, fermentation, aging, and packaging. While some phases of wine- making can be supported by modern techniques, the agricultural production consisting of the cultivation of grapes and their surveil- lance is done with traditional techniques by the farmers in order to detect problems - e.g. plagues, humidity, or dryness - and ideal conditions - e.g. the Ph or sugar level. The remainder of this paper is organized as follows. Section 2 describes the economic importance of the winemaking process as a reference sector of the Spanish economy and it discusses both WSNs and other alternative monitoring strategies. Section 3 presents the related work with regard to solutions for agricultural monitoring and decision making. In Section 4 we collect the general requirements for agricultural monitoring based on WSNs. Section 5 describes our proposal for a novel methodology designed to support applications development with the purpose of improving the agri- cultural production. Section 6 underscores our main contributions. We conclude in Section 7 by summarizing our work.
Classification of Grape Monitoring based on the Romer design space. Table 1 environment and crop conditions in oder to improve the quality of the crops. can be obtained quickly by measuring electro-magnetic radiation reflected from farmland and collected by orbiting satellites. The spectrum of this radiation can reveal the properties of the soil, moisture level, the quantity of crop being grown, and the levels of chlorophyll and minerals in the crops. This solution is generally less expensive than the analysis of crops typically performed in laboratories.
Classification of Grape Monitoring based on the Romer design space. Table 1 environment and crop conditions in oder to improve the quality of the crops. can be obtained quickly by measuring electro-magnetic radiation reflected from farmland and collected by orbiting satellites. The spectrum of this radiation can reveal the properties of the soil, moisture level, the quantity of crop being grown, and the levels of chlorophyll and minerals in the crops. This solution is generally less expensive than the analysis of crops typically performed in laboratories.
WSN-based agricultural monitoring projects. Table 2 4. Analysis of agricultural monitoring applications process. Open challenges in precision vineyards were reviewed by Arno et al. (2009). The term vineyard computing was introduced by Burrell et al. (2004), which in his work uses ethnographic methods to identify the priorities of people working in vineyards in order to exploit the use of sensor networks in agriculture.
WSN-based agricultural monitoring projects. Table 2 4. Analysis of agricultural monitoring applications process. Open challenges in precision vineyards were reviewed by Arno et al. (2009). The term vineyard computing was introduced by Burrell et al. (2004), which in his work uses ethnographic methods to identify the priorities of people working in vineyards in order to exploit the use of sensor networks in agriculture.
Fig. 2. The phases of the methodology and their corresponding outcomes.
Fig. 2. The phases of the methodology and their corresponding outcomes.
Fig. 3. A parcel with three different zones — A, B, and C - classified based on geographic and weather maps.
Fig. 3. A parcel with three different zones — A, B, and C - classified based on geographic and weather maps.
Fig. 4. On the left, a terrain of 600 x 450 m divided in three different zones of interest: A, B and C. On the right, the network architecture design for every zone, identifyins the amount of nodes required and their physical location.
Fig. 4. On the left, a terrain of 600 x 450 m divided in three different zones of interest: A, B and C. On the right, the network architecture design for every zone, identifyins the amount of nodes required and their physical location.
Fig. 5. A wireless sensor network using a tree topology. The output of this step is a table which describes the sensor net- work design. We name this table the Sensor Network Architecture TAble (SeNTA); SeNTA will record the assignments of the monitor-
Fig. 5. A wireless sensor network using a tree topology. The output of this step is a table which describes the sensor net- work design. We name this table the Sensor Network Architecture TAble (SeNTA); SeNTA will record the assignments of the monitor-
Fig. 6. State transition diagram of generic monitoring applications (time-driven and exception-driven model).
Fig. 6. State transition diagram of generic monitoring applications (time-driven and exception-driven model).
Fig. 7. Estimation of node lifetime using a routing protocol without energy saving (on the left) and with energy awareness strategy (on the right) for different battery capacities and sampling frequencies.
Fig. 7. Estimation of node lifetime using a routing protocol without energy saving (on the left) and with energy awareness strategy (on the right) for different battery capacities and sampling frequencies.
Fig. 9. View of Sensonic where the Sensitive graphics can be added and removed. For preventive maintenance SeNTA is used to compute the MTTF (or expected lifetime) for each element. With this information, Although sensor networks are optimized to maximize their life- time and intended to work in an autonomous fashion, they do require maintenance tasks, which are performed concurrently with the exploitation phase. The inputs of the maintenance phase are the SeNTA, the RAT, and the deployment and operation manual. The output is a maintenance manual that will be updated by WSN supervisors. This manual provides the description of all the main- tenance tasks that need to be performed, in a similar way that the
Fig. 9. View of Sensonic where the Sensitive graphics can be added and removed. For preventive maintenance SeNTA is used to compute the MTTF (or expected lifetime) for each element. With this information, Although sensor networks are optimized to maximize their life- time and intended to work in an autonomous fashion, they do require maintenance tasks, which are performed concurrently with the exploitation phase. The inputs of the maintenance phase are the SeNTA, the RAT, and the deployment and operation manual. The output is a maintenance manual that will be updated by WSN supervisors. This manual provides the description of all the main- tenance tasks that need to be performed, in a similar way that the
Fig. 8. Sensitive: it is represented the temperature read from several sensors (iden- tified by sensor node id) during a logical period of time (given by sequence number).
Fig. 8. Sensitive: it is represented the temperature read from several sensors (iden- tified by sensor node id) during a logical period of time (given by sequence number).
Fig. 10. Representation for the battery state of motes on zone B.
Fig. 10. Representation for the battery state of motes on zone B.
Related topics:
EngineeringPrecision AgricultureWater SavingDevelopment MethodologyReal Time Decision Making
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