The ever increasing requirements of data sensing applications result in the usage of IoT networks. These networks are often used for efficient data transfer. Wireless sensors are incorporated in the IoT networks to reduce the deployment and maintenance costs. Designing an energy efficient data aggregation method for sensor equipped IoT to process skyline query, is one of the most critical problems. In this paper, we propose two approximation algorithms to process the skyline query in wireless sensor networks. These two algorithms are uniform sampling-based approximate skyline query and Bernoulli sampling-based approximate skyline query. Solid theoretical proofs are provided to confirm that the proposed algorithms can yield the required query results. Experiments conducted on actual datasets show that the two proposed algorithms have high performance in terms of energy consumption compared to the simple distributed algorithm.

As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.