Sensor network coverage and data aggregation problem: solutions toward the maximum lifetime

In the coverage problem, an optimal solution is proposed for the maximum lifetime sensor scheduling problem, which could find the upper bound of a sensor network\u27s lifetime. This research reveals the relationship between the degree of redundancy in sensor deployment and achievable extension on network lifetime, which can be a useful guide for practical sensor network design --Introduction, page 4

Cross-layer design through joint routing and link allocation in wireless sensor networks

Both energy and bandwidth are scarce resources in sensor networks. In the past, the energy efficient routing problem has been extensively studied in efforts to maximize sensor network lifetimes, but the link bandwidth has been optimistically assumed to be abundant. Because energy constraint affects how data should be routed, link bandwidth affects not only the routing topology, but also the allowed data rate on each link, which in turn affects the lifetime. Previous research that focus on energy efficient operations in sensor networks with the sole objective of maximizing network lifetime only consider the energy constraint ignoring the bandwidth constraint. This thesis shows how infeasible these solutions can be when bandwidth does present a constraint. It provides a new mathematical model that address both energy and bandwidth constraints and proposes two efficient heuristics for routing and rate allocation. Simulation results show that these heuristics provide more feasible routing solutions than previous work, and significantly improve throughput. A method of assigning the time slot based on the given link rates is presented. The cross layer design approach improves channel utility significantly and completely solves the hidden terminal and exposed terminal problems --Abstract, page iii

Cross-layer design for network performance optimization in wireless networks

In this dissertation, I use mathematical optimization approach to solve the complex network problems. Paper l and paper 2 first show that ignoring the bandwidth constraint can lead to infeasible routing solutions. A sufficient condition on link bandwidth is proposed that makes a routing solution feasible, and then a mathematical optimization model based on this sufficient condition is provided. Simulation results show that joint optimization models can provide more feasible routing solutions and provide significant improvement on throughput and lifetime. In paper 3 and paper 4, an interference model is proposed and a transmission scheduling scheme is presented to minimize the end-to-end delay. This scheduling scheme is designed based on integer linear programming and involves interference modeling. Using this schedule, there are no conflicting transmissions at any time. Through simulation, it shows that the proposed link scheduling scheme can significantly reduce end-to-end latency. Since to compute the maximum throughput is an NP-hard problem, efficient heuristics are presented in Paper 5 that use sufficient conditions instead of the computationally-expensive-to-get optimal condition to capture the mutual conflict relation in a collision domain. Both one-way transmission and two-way transmission are considered. Simulation results show that the proposed algorithms improve network throughput and reduce energy consumption, with significant improvement over previous work on both aspects. Paper 6 studies the complicated tradeoff relation among multiple factors that affect the sensor network lifetime and proposes an adaptive multi-hop clustering algorithm. It realizes the best tradeoff among multiple factors and outperforms others that do not. It is adaptive in the sense the clustering topology changes over time in order to have the maximum lifetime --Abstract, page iv

Energy-efficient Data Gathering Algorithm in Sensor Networks with Partial Aggregation

In sensor networks, data aggregation at intermediate nodes can significantly reduce redundant data and reduce communication load. However, there are scenarios where data aggregation is restricted. In this paper, we study the problem of building an energy-efficient tree structure that can be used for both aggregate data and non-aggregate data. Such a tree provides a transition between the optimal solutions for both aggregate data and for non-aggregate data. A single parameter can be used to control the transition. We proposed a new algorithm Balanced Aggregation Tree (BAT) for tree construction and also suggested how to determine the value of the control parameter for the highest energy efficiency of a given network