Energy e ciency is a critical constraint in wireless sensor networks. Wireless sensor networks (WSNs) consist of a large number of battery-powered sensor nodes, connected to each other and equipped with low-power transmission radios. Usually, the sensor nodes closer to the sink are more likely to become overloaded and subject to draining their battery faster than the nodes farther away, creating a funneling e ect. The use of a mobile device as a sink node to perform data gathering is a well known solution to balance the energy consumption in the entire network. To address this problem, in this work we consider the use of an UAV as a mobile sink. An unmanned aircraft vehicle (UAV) is an aircraft without a human pilot on-board, popularly known as a Drone. In this thesis, besides the use of the UAV as a mobile sink node, we propose an UAV-aided algorithm for data gathering in wireless sensor networks, called Humming- bird. Our distributed algorithm is energy-e cient. Rather than using an arbitrary path, the UAV implements an approximation algorithm to solve the well-known NP- Hard problem, the Traveling Salesman Problem (or TSP), to setup the trajectory of node points to visit for data gathering. In our approach, both the path planning and the data gathering are performed by the UAV, and this is seamlessly integrated with sensor data reporting. The results, using ns-3 network simulator show that our algorithm improves the network lifetime compared to regular (non-UAV) data gathering, especially for data intensive applications. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Zhou Yangfan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 102-112). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction and Background Study --- p.1 / Chapter 1.1 --- Wireless Sensor Networks --- p.1 / Chapter 1.1.1 --- Wireless Integrated Network Sensors --- p.1 / Chapter 1.1.2 --- Main Challenge of In-situ Sensing with Sensor Nodes: Limited Energy Resource --- p.3 / Chapter 1.1.3 --- Networking the Sensor Nodes --- p.4 / Chapter 1.2 --- Applications of Wireless Sensor Networks --- p.4 / Chapter 1.3 --- Characteristics of Wireless Sensor Networks: A Summary --- p.6 / Chapter 1.4 --- Energy-Efficient and Reliable Wireless Sensor Networks --- p.9 / Chapter 2 --- PORT: A Price-Oriented Reliable Transport Protocol --- p.12 / Chapter 2.1 --- Reliable Sensor-to-Sink Data Communications in Wireless Sensor Networks --- p.14 / Chapter 2.2 --- Related Work --- p.17 / Chapter 2.3 --- Protocol Requirements --- p.20 / Chapter 2.4 --- Design Considerations --- p.25 / Chapter 2.4.1 --- The concept of node price --- p.25 / Chapter 2.4.2 --- Link-loss rate estimation --- p.28 / Chapter 2.4.3 --- Routing scheme --- p.29 / Chapter 2.5 --- Protocol Description --- p.31 / Chapter 2.5.1 --- Task initialization --- p.31 / Chapter 2.5.2 --- Feedback of newly desired source reporting rates --- p.32 / Chapter 2.5.3 --- Feedback of wireless communication condition --- p.32 / Chapter 2.5.4 --- Fault tolerance and scalability considerations --- p.33 / Chapter 2.6 --- Protocol Evaluation: A Case Study --- p.34 / Chapter 2.6.1 --- Simulation model --- p.34 / Chapter 2.6.2 --- Energy consumption comparison --- p.36 / Chapter 2.6.3 --- The impact of reporting sensors' uncertainty distribution --- p.39 / Chapter 2.7 --- Conclusion --- p.40 / Chapter 3 --- Setting Up Energy-Efficient Paths --- p.41 / Chapter 3.1 --- Transmitter Power Setting for Energy-Efficient Sensor-to-Sink Data Communications --- p.46 / Chapter 3.1.1 --- "Network, communication, and energy consumption models" --- p.46 / Chapter 3.1.2 --- Transmitter power setting problem for energy-efficient sensor-to-sink data communications --- p.49 / Chapter 3.2 --- Setting Up the Transmitter Power Levels for Sensor-to-Sink Traffic --- p.51 / Chapter 3.2.1 --- BOU: the basic algorithm --- p.52 / Chapter 3.2.2 --- Packet implosion of BOU: the challenge --- p.53 / Chapter 3.2.3 --- Determining the waiting time before broadcasting --- p.56 / Chapter 3.2.4 --- BOU-WA: an approximation approach --- p.60 / Chapter 3.3 --- Simulation Results --- p.62 / Chapter 3.3.1 --- The comparisons of BOU and BOU-WA --- p.63 / Chapter 3.3.2 --- The approximation of BOU-WA --- p.65 / Chapter 3.4 --- Related Work --- p.67 / Chapter 3.5 --- Conclusion Remarks and Future Work --- p.69 / Chapter 4 --- Solving the Sensor-Grouping Problem --- p.71 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- The Normalized Minimum Distance i:A Point-Distribution Index --- p.74 / Chapter 4.3 --- The Sensor-Grouping Problem --- p.77 / Chapter 4.3.1 --- Problem Formulation --- p.80 / Chapter 4.3.2 --- A General Sensing Model --- p.81 / Chapter 4.4 --- Maximizing-i Node-Deduction Algorithm for Sensor-Grouping Problem --- p.84 / Chapter 4.4.1 --- Maximizing-i Node-Deduction Algorithm --- p.84 / Chapter 4.4.2 --- Incremental Coverage Quality Algorithm: A Benchmark for MIND --- p.86 / Chapter 4.5 --- Simulation Results --- p.87 / Chapter 4.5.1 --- Number of Groups Formed by MIND and ICQA --- p.88 / Chapter 4.5.2 --- The Performance of the Resulting Groups --- p.89 / Chapter 4.6 --- Conclusion --- p.90 / Chapter 5 --- Conclusion --- p.92 / Chapter A --- List of Research Conducted --- p.96 / Chapter B --- Algorithms in Chapter 3 and Chapter 4 --- p.98 / Bibliography --- p.102

This work starts from the proposition that it is beneficial to conserve communication energy in Wireless Sensor Networks (WSNs). For WSNs there is an added incentive for energy-efficient communication. The power supply of a sensor is often finite and small. Replenishing the power may be impractical and is likely to be costly. Wireless Sensor Networks are an important area of research. Data about the physical environment may be collected from hostile or friendly environments. Data is then transmitted to a destination without the need for communication cables. There are power and resource constraints upon WSNs, in addition WSN networks are often application specific. Different applications will often have different requirements. Further, WSNs are a shared medium system. The features of the MAC (Medium Access Control) protocol together with the application behaviour shape the communication states of the node. As each of these states have different power requirements the MAC protocol impacts upon the operation and power consumption efficiency. This work focuses on the development of an energy conservation protocol for WSNs where direct communication between sources and a base station is feasible. Whilst the multi-hop approach has been regarded as the underlying communication paradigm in WSNs, there are some scenarios where direct communication is applicable and a significant amount of communication energy can be saved. The Power & Reliability Aware Protocol has been developed. Its main objectives are to provide efficient data communication by means of energy conservation without sacrificing required reliability. This has been achieved by using direct communication, adaptive power adaptation and intelligent scheduling. The results of simulations illustrate the significance of communication energy and adaptive transmission. The relationship between Received Signal Strength Indicator (RSSI) and Packet Reception Rate (PRR) metrics is established and used to identify when power adaptation is required. The experimental results demonstrate an optimal region where lower power can be used without further reduction in the PRR. Communication delays depend upon the packet size whilst two-way propagation delay is very small. Accurate scheduling is achieved through monitoring the clock drift. A set of experiments were carried out to study benefits of direct vs. multi-hop communication. Significant transmitting current can be conserved if the direct communication is used. PoRAP is compared to Sensor-MAC (S-MAC), Berkeley-MAC (B-MAC) and Carrier Sense Multiple Access (CSMA). Parameter settings used in the Great Duck Island (GDI) a production habitat monitoring WSNs were applied. PoRAP consumes the least amount of energy.