Data Aggregation Methods for IoT Routing Protocols A Review Focusing on Energy Optimization in Precision Agriculture
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Abstract
Agriculture productivity can be enhanced by IoT- enabled real-time monitoring of weather and soil parameters. Increased volume of sensor data demands a significant amount of memory and power. It also overloads the network, making real-time parameter monitoring very difficult. A large volume of sensor data reduces the lifetime and latency of the network, decreasing the overall throughput. Hence, a reduction in data overload becomes necessary for energy optimization of these energy-constrained sensors. Data aggregation is an effective way of optimizing energy consumption by reducing the volume of redundantly sensed data. Data aggregation helps in designing energy-efficient routing algorithms to transmit information by consuming minimum energy to increase the operational period of the network. This paper surveys different routing algorithms for data aggregation with a focus on energy optimization in precision agriculture. The survey includes IPv6 routing protocol for low-power and lossy networks (RPL) to reduce network overload during data transmission, nature-inspired algorithms for energy-optimized intracluster communication, and energy-efficient compressive sensing (CS) to minimize redundant data by aggregation. It also examines duty-cycling algorithms for reducing average energy consumption by periodically placing sensors into the sleep mode during inactive state to save energy. Different performance benchmarks are evaluated to determine the suitability of the routing algorithms in agriculture.
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