The objective of this dissertation is to investigate Device-to-Device content dissemination protocols for maximizing the economic gain of dissemination for given combinations of commercial and network parameters. We pose this as a gain-aware multicast Delay Tolerant Network (DTN) routing problem in a Social Wireless Network (SWN) setting. Commercial parameters in this framework include the revenue out of selling a product, discount, rebate, content expiry time, and a content-specific... Show moreThe objective of this dissertation is to investigate Device-to-Device content dissemination protocols for maximizing the economic gain of dissemination for given combinations of commercial and network parameters. We pose this as a gain-aware multicast Delay Tolerant Network (DTN) routing problem in a Social Wireless Network (SWN) setting. Commercial parameters in this framework include the revenue out of selling a product, discount, rebate, content expiry time, and a content-specific redemption function. These parameters are inputs provided by a content generator. Network parameters are decided based on a Social Wireless Network’s characteristics such as consumers’ interest profiles and their social bindings summarized in their mobility profiles. We explore two solution approaches. First, we investigate stochastic design methods for gain-aware DTN protocol design including prediction-based dissemination mechanisms. We develop a predictive gain utility which is used for deciding relative importance of an immediate peer in comparison with potential future peers. We propose two versions of the utility; the first one, Predictive Gain Utility Routing-Individual (PGUR-I) uses node-specific information, and the second one, Predictive Gain Utility Routing-Aggregated (PGUR-A) implements a more scalable version by probabilistically aggregating node interaction information across consumers with similar consumption interests. In the second approach, we explore learning mechanisms including evolutionary learning and reinforcement learning for synthetizing communication protocols. We explore evolutionary learning, specifically, evolving state machines for protocol synthesis. A specific network protocol is coded as a genotype and its resulting state machine behavior manifests in the form of the corresponding phenotype, leading to specific performance. Using the developed framework, we explore the evolutionary design of Medium Access Control (MAC) protocols including ALOHA, S-ALOHA and Carrier Sensing Multiple Access (CSMA). Finally, we leverage Reinforcement Learning (RL), which enables nodes to gather local information and make efficient routing decisions in runtime. Show less
