Large scale message dissemination is important in our daily life. Typical examples include emergency notifications and advertisements. However, traditional approaches for large scale message dissemination, e.g., the Internet and cellular networks often incur high communication and maintenance costs. With the proliferation of mobile handsets and their powerful communication technologies, e.g., Wi-Fi and Bluetooth, large scale message dissemination becomes feasible and affordable. We can envision such a mechanism originating at a source, e.g., a base station, and propagating via interactions among human forwarders' mobile handsets. This creates much opportunity for large scale message dissemination; however, it also introduces new challenges. The message source might deliver irrelevant messages to receivers, irritating them and wasting time. Therefore, the first key challenge is to ensure the efficiency of message delivery in such mobile opportunistic networks. Generally, large scale message dissemination needs to involve many participants. Some might not forward the message unless appropriate incentives are provided. Hence, the second key challenge is proving such incentives for message forwarding. In this dissertation, we will study the two challenges. Our aim is to provide efficient message delivery and message forwarding incentives in large scale message dissemination in these networks.
We first study efficient message delivery in large scale message dissemination. To efficiently deliver messages in mobile opportunistic networks, the source can send location-specific messages to interested persons. Hence, a key enabling technology is object localization. We present a technique called EV-Loc for accurate localization based on electronic and visual signals. Given an object's electronic identifier, we aim to accurately localize the object with the help of visual signals. In order to achieve this, we propose an E-V match engine that can accurately and efficiently correspond an object's electronic and visual signals. Following the correspondence, we use visual localization to precisely estimate the given object's position. We also consider practical situations, e.g., missing or indistinct electronic and visual signals, and devise schemes to eliminate their impacts.
We then study the incentives for message forwarding in large scale message dissemination. We propose an incentive-driven and privacy-preserving forwarding algorithm for large scale message dissemination in mobile opportunistic networks. To encourage forwarding behaviors, we distribute incentives among forwarders and maintain the forwarder list. In our forwarding algorithm, we rely on a probabilistic one-ownership mechanism to record the list in order to achieve message brevity and privacy preservation. More specifically, only one hop of forwarder information, instead of the complete list, is recorded, and the information is updated probabilistically following two ownership flipping models: One-Flip and Always-Flip. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free message exchange. Our approach can serve as a general framework for facilitating message dissemination in mobile opportunistic networks, where incentives and privacy protection are both essential.
|Commitee:||Qin, Feng, Zheng, Yuan F.|
|School:||The Ohio State University|
|Department:||Computer Science and Engineering|
|School Location:||United States -- Ohio|
|Source:||DAI-B 78/11(E), Dissertation Abstracts International|
|Keywords:||Advertisements, Emergency notifications, Message dissemination|
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