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Energy efficient data aggregation in wireless sensor networks


Wireless sensor networks (WSNs) form a critical interface between physical and digital worlds by converting physical qualities into measurements which can be used for a wide-ranging spectrum of applications. In the past, these WSNs have been application-specific and exposed only to a limited set of users. Going forward, WSNs will no longer be specialized networks. The new emerging IoT will run multiple applications that have diverse delay requirements for generated and received measurements. In this thesis, we propose a power management framework that operates on the sensing platforms which have multiple power managers. For example, sensors are controlled by sensor controller and battery is managed by battery manager in order to minimize the unnecessary energy consumption. The proposed framework integrates these different power management approaches and optimizes their interactions to achieve optimality in terms of energy efficiency. Proposed approach saves 20\% to 60\% of energy consumption compared to the state of art approaches.

In addition, we propose an optimal transmission manager that supports multiple applications in single-hop wireless sensor networks. We formulate the problem with Markov decision process model and dynamically adjust transmission instances based on random delay requirements of buffered measurements. Then, we propose a distributed transmission manager that leverages the optimal transmission manager to operate in multi-hop WSNs. We implement both transmission managers in ns3 simulator and compare with other state of the art designs. The results show that the proposed transmission manager consumes on average 148.3% less energy than the state of the art approaches while on average having 14.1% fewer measurements that expire.

Lastly, we propose adaptive information dissemination protocol in order to provide information to users in vicinity. Sensors estimate users moving speed and adjust information provision interval in order to save energy. The results show that our proposed approach decrease in power consumption by a factor of 2x to 8x in a single sensor, and 2x to 16x in 10 node sensor network, when compared to the state of art approaches.

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