Power Aware Routing for Sensor Databases

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensor network databases like TinyDB are the dominant architectures to extract and manage data in such networks. Since sensors have significant power constraints (battery life), and high communication costs, design of energy efficient communication algorithms is of great importance. The data flow in a sensor database is very different from data flow in an ordinary network and poses novel challenges in designing efficient routing algorithms. In this work we explore the problem of energy efficient routing for various different types of database queries and show that in general, this problem is NP-complete. We give a constant factor approximation algorithm for one class of query, and for other queries give heuristic algorithms. We evaluate the efficiency of the proposed algorithms by simulation and demonstrate their near optimal performance for various network sizes

A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this pa-per, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the pro-posed scheduling method can better prolong the lifetime of the network in comparison to similar existing method

Medians and Beyond: New Aggregation Techniques for Sensor Networks

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets