Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of this dissertation is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure.
We propose the first structure-free data aggregation technique that achieves high efficiency. Based on this technique, we propose two semi-structured approaches
to support scalability. We conduct large scale simulations and real experiments on a testbed to validate our design. The results show that our protocols can perform similar to an optimum structured approach which has global knowledge of the event and the network.
In addition to conserving energy through efficient data aggregation, renewable energy sources are required for sensor networks to support everlasting monitoring services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing data services without interruptions caused by battery runouts is non-trivial. Moreover, most environment monitoring applications require data collection from all nodes at a steady rate. The objective is to design a solution for fair and high throughput data extraction from all nodes in the network in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node in the network, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 158 sensor nodes to validate the distributed algorithm.