International Journal of Computer and Applications

A review on monitoring and advanced control strategies for precision irrigation

The sensor nodes have limited sensing, computation, communication capabilities and are mostly operated by batteries in a harsh environment with non-replenishable power sources. These restrictions make the sensor network prone to failures because most of the energy is spent on data transmission, sensing, and computing. Many applications such as habitat monitoring, military surveillance and forest fire detection expect the sensor nodes to last for a long time because they operate human unattended. Therefore, the major challenges in designing a wireless sensor network (WSN) are energy conservation, reducing data transmission delay and improving the network lifetime. In this context, data aggregation is an intelligent technique used in WSN, wherein the data from disparate sources are accumulated at intermediate nodes, thereby reducing the number of packets to be sent to the sink. Literature study shows that various routing algorithms are used to perform data aggregation based on the network topology. In order to provide an improved performance amongst the existing, a routing algorithm called cluster-chain mobile agent routing (CCMAR) is proposed in this work. It makes full use of the advantages of both low energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor information systems (PEGASIS). CCMAR divides the WSN into a few clusters and runs in two phases.The proposed system is simulated and evaluated for the performance metrics such as energy consumption, transmission delay and network lifetime. The results demonstrate that the proposed CCMAR outperforms LEACH, PEGASIS and other similar routing algorithm, energy efficient cluster-chain based protocol.

Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

In wireless sensor networks (WSN), data aggregation using soft computing methods is a challenging issue because of the security factors. When a node is compromised, it is easy for an adversary to inject false data and mislead the aggregator to accept false readings. Therefore there is a need for secure data aggregation. Although sufficient works on the survey of data aggregation in WSNs are done, it seems less satisfactory in terms of maintaining a secured data aggregation, and measuring accurate values. This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques. Here, classification of protocols is done according to the soft computing technique as: fuzzy logic, swarm intelligence, genetic algorithm and neural networks. Accuracy, energy consumption, cost reduction and security measures are the metrics used for the classification. Finally, the authors provide a comparative study of all aggregation techniques.

Survey of data aggregation techniques using soft computing in wireless sensor networks

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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Intelligent Irrigation Control System by Employing Wireless Sensor Networks

The data aggregation is most important in Wireless Sensor Networks (WSN) due to constraint of resources. There is lot of data redundancy in WSN due to dense deployment. So, it is necessary to minimize the data redundancy by adopting suitable aggregation techniques. To resolve this problem, Support Vector Machine (SVM) based Data Redundancy Elimination for Data Aggregation in WSN (SDRE) has been proposed in this work. First, we build aggregation tree for the given size of the sensor network. Then, SVM method was applied on tree to eliminate the redundant data. The Locality Sensitive Hashing (LSH) is used minimize the data redundancy and to eliminate the false data based on the similarity. The LSH codes are sent to the aggregation supervisor node. The aggregation supervisor finds sensor nodes that have the same data and selects only one sensor node among them to send actual data. The benefit of this approach is it minimizes the redundancy and to eliminate the false data to improve performance of WSN. The performance of proposed approach is measured using the network parameters such as Delay, Energy, Packet drops and Overheads. The SDRE perform better in all the scenarios for different size network and varying data rate.

SVM based data redundancy elimination for data aggregation in Wireless Sensor Networks

Recent advances in technology and wireless sensor networks made new trends to emerge in agriculture sector. One such new trend is Precision Agriculture(PA). This paper presents an automatic Irrigation Controller for precision agriculture. The irrigation controller regulates the desired moisture level in agricultural soil by making the irrigation pump on or off based on the sensor readings. This controller efficiently monitors moisture level in soil and proves efficient in both dry and heavy rainfall conditions.

Wireless Sensor Network for Precision Agriculture

Wireless Sensor Network has limited battery power and computational power, leads to increased complexity A better data aggregation framework on wireless sensor networks gives an efficient battery power and improved lifetime This can be achieved by making the framework as middleware for aggregating data, measured by a number of nodes within a network In this paper the performance of TAG in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks is presented

Effect of data aggregation on wireless sensor network performance

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Prakashgoud Patil

Papers

Intelligent Irrigation Control System by Employing Wireless Sensor Networks

SVM based data redundancy elimination for data aggregation in Wireless Sensor Networks

Wireless Sensor Network for Precision Agriculture

Effect of data aggregation on wireless sensor network performance

Energy Efficient Aggregation with Divergent Sink Placement for Wireless Sensor Networks