Data and energy management in wireless sensor networks

Field: Computer Science (Physical Sciences)

Wireless ad hoc networks of battery–powered microsensors (WSN) are proliferating rapidly and transforming how information is gathered and processed, and how we affect our environment. These networks are envisioned to have thousands of inexpensive sensors with sensing, data processing, and communication components. Sensors typically operate in unattended mode, communicate with one another over short distances and establish multi–hop communication paths to one or more base stations. The limited energy, the large number of sensor nodes, the huge amount of generated sensing data, the unfriendly working environments, and the nature of unpredictable deployment of wireless sensor networks have introduced many interesting challenges. Foremost among them is energy conservation, since it often is cost prohibitive or even infeasible to replenish the energy of the sensors.
In this dissertation, we present our efforts toward energy conservation in wireless sensor networks. We address four data and energy management problems, with the goal of prolonging the network lifetime—the time until the first sensor depletes its energy.
The first problem we consider is that of reducing the transmitted bits by a sensor sending a set of elements to the base station. Since radio communication is the major energy consumer in WSN, it helps save energy by reducing the transmitted bits. We provide a novel method that can substantially reduce the transmitted bits for a set of elements, with its application to gathering network topology and network energy map, two of the network metadata that are critical for network management and maintenance.
The second problem we work on is the problem of identifying data redundancy and its utilization on data gathering. We propose a new idea of using measurements co–occurrence to identify data redundancy in wireless sensor networks, and a novel collaborative data gathering method utilizing co-occurrence that can significantly reduce the communication cost of data gathering, at the cost of few errors when estimating the sensor measurements at the base station.
The third problem we consider in this dissertation is maximum lifetime data gathering with in–network aggregation (MLDA) in wireless sensor networks, that is, maximizing the system lifetime T so that we can perform T rounds of data gathering with in–network aggregation, given the network topology and the available energy of the sensors. The number of aggregation trees used in the solution is required to be small, because the computation resource of sensors is limited. We describe a simple and efficient combinatorial iterative algorithm for finding a near optimal solution that consists of up to n–1 aggregation trees.
The fourth problem we consider is maximum lifetime continuous in–network query processing. This problem has two coupled aspects: (1) the placement of the query operators, variables, and constants to the sensor nodes, and (2) the routing of the values to the appropriate sensor nodes that need them to evaluate the operators. We define the routing aspect of the problem as a Maximum Lifetime Concurrent Flow (MLCF) problem in WSN, and provide a combinatorial iterative algorithm for finding a near optimal solution of small size. Together with our simple and efficient algorithm for addressing the placement aspect, we provide a novel approach that consistently finds optimal solutions to the continuous in–network query processing problem.