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Fast growing technology of wireless sensor networks (WSN) is an advantage to the agriculture sector. Modern advances in the development of WSN offer new trends like Precision Agriculture (PA). This paper presents Fuzzy Logic Based Intelligent Irrigation Control System by employing WSN for PA. In the proposed system the irrigation controller normalizes the desired moisture level in the agricultural soil by controlling the water flow of the irrigation pump based on the sensor readings, by switching the pump between ON and OFF states. For the efficient utilization of water in the agricultural irrigation, the proposed methodology offers a system equipped with soil moisture sensors, temperature sensors, precise irrigation equipments, computer-controlled devices, and an intelligent controller using fuzzy logic approach for irrigation of agricultural fields, which simulates or emulates the human being's intelligence. This leads to effective utilization of various resources like water and electricity and hence becomes a cost effective system for the expected yield. The fuzzy controller designed in this paper efficiently monitors moisture level in soil, leaf wetness, temperature, humidity, and other essential parameters and also controls the irrigation intelligently. The results show that the application is correct and reasonable and enables user to precisely acquire the crop water requirement information. The system can be effectively applied to PA applications such as water-saving agriculture areas, for example, the croplands, the nursery gardens and the greenhouses.

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In wireless sensor networks data aggregation is a very important one and at the same time the aggregation should be energy efficient and a lesser amount of delay. To solve the problem we propose Delay Efficient Distributed Data Aggregation (DEDA) Scheduling Algorithm for Wireless Sensor Network (WSN) to handle the delay and energy tradeoff in the process of aggregation using the timeout concept. In this paper, first we are building the aggregated tree. After building the aggregation tree the Distributed Data Aggregation Scheduling algorithm to achieve the optimized energy efficiency and delay aware data aggregation. Decision Making Unit (DMU) has been used to handle energy and delay trade off. The DMU will control the tradeoff between energy and delay by generating the timeout values. The main advantage of this approach is to achieve the ideal energy consumption by limiting a number of redundant and unnecessary responses from the sensor nodes.