A Mobile Ad Hoc Network (MANET) adopts a decentralized communication architecture which relies on cooperation among nodes at each layer of the protocol stack. Its reliance on cooperation for success and survival makes the ad hoc network particularly sensitive to variations in node behavior. Specifically, for functions such as routing, nodes which are limited in their resources may be unwilling to cooperate in forwarding for other nodes. Such selfish behavior leads to degradation in the performance of the network and possibly, in the extreme case, a complete cessation of operations. Consequently it is important to devise solutions to encourage resource-constrained nodes to cooperate. Incentive schemes have been proposed to induce selfish nodes to cooperate. Though many of the proposed schemes in the literature are payment-based, nodes can be incentivized to cooperate by adopting policies which are non-monetary in nature, but rather are based on the threat of retaliation for non-cooperating nodes. These policies, for which there is little formal analysis in the existing literature on node cooperation, are based on observed node behavior. We refer to them as behavior-based incentives. In this work, we analyze the effectiveness of behavior-based incentives in inducing nodes to cooperate. To determine whether an incentive scheme is effective in fostering cooperation we develop a game-theoretic model. Adopting a repeated game model, we show that nodes may agree to cooperate in sharing their resources and forward packets, even if they perceive a cost in doing so. This happens as the nodes recognize that refusing to cooperate will result in similar behavior by others, which ultimately would compromise the viability of the network as a whole. A major shortcoming in the analysis done in past works is the lack of consideration of practical constraints imposed by an ad hoc environment. One such example is the assumption that a node, when making decisions about whether to cooperate, has perfect knowledge of every other node's actions. In a distributed setting this is impractical. In our work, we analyze behavior-based incentives by incorporating such practical considerations as imperfect monitoring into our game-theoretic models. In modeling the problem as a game of imperfect public monitoring (nodes observe a common public signal that reflects the actions of other nodes in the network) we show that, under the assumption of first order stochastic dominance of the public signal, the grim trigger strategy leads to an equilibrium for nodes to cooperate. Even though a trigger-based strategy like grim-trigger is effective in deterring selfish behavior it is too harsh in its implementation. In addition, the availability of a common public signal in a distributed setting is rather limited. We, therefore, consider nodes that individually monitor the behavior of other nodes in the network and keep this information private. Note that this independent monitoring of behavior is error prone as a result of slow switching between transmit and promiscuous modes of operation, collisions and congestion due to the wireless medium, or incorrect feedback from peers. We propose a probability-based strategy that induces nodes to cooperate under such a setting. We analyze the strategy using repeated games with imperfect private monitoring and show it to be robust to errors in monitoring others" actions. Nodes achieve a near-optimal payoff at equilibrium when adopting this strategy. This work also characterizes the effects of a behavior-based incentive, applied to induce cooperation, on topology control in ad hoc networks. Our work is among the first to consider selfish behavior in the context of topology control. We create topologies based on a holistic view of energy consumption " energy consumed in forwarding packets as well as in maintaining links. Our main results from this work are to show that: (a) a simple forwarding policy induces nodes to cooperate and leads to reliable paths in the generated topology, (b) the resulting topologies are well-connected, energy-efficient and exhibit characteristics similar to those in small-world networks. / Ph. D.

This thesis looks into the economics of crowdsourcing using game theoretic modeling. The art of aggregating information and expertise from a diverse population has been in practice since a long time. The Internet and the revolution in communication and computational technologies have made this task easier and given birth to a new era of online resource aggregation, which is now popularly referred to as crowdsourcing. Two important features of this aggregation technique are: (a) crowdsourcing is always human driven, hence the participants are rational and intelligent, and they have a payoff function that they aim to maximize, and (b) the participants are connected over a social network which helps to reach out to a large set of individuals. To understand the behavior and the outcome of such a strategic crowd, we need to understand the economics of a crowdsourcing network. In this thesis, we have considered the following three major facets of the strategic crowdsourcing problem. (i) Elicitation of the true qualities of the crowd workers: As the crowd is often unstructured and unknown to the designer, it is important to ensure if the crowdsourced job is indeed performed at the highest quality, and this requires elicitation of the true qualities which are typically the participants' private information. (ii) Resource critical task execution ensuring the authenticity of both the information and the identity of the participants: Due to the diverse geographical, cultural, socio-economic reasons, crowdsourcing entails certain manipulations that are unusual in the classical theory. The design has to be robust enough to handle fake identities or incorrect information provided by the crowd while performing crowdsourcing contests. (iii) Improving the productive output of the crowdsourcing network: As the designer's goal is to maximize a certain measurable output of the crowdsourcing system, an interesting question is how one can design the incentive scheme and/or the network so that the system performs at an optimal level taking into account the strategic nature of the individuals. In the thesis, we design novel mechanisms to solve the problems above using game theoretic modeling. Our investigation helps in understanding certain limits of achievability, and provides design protocols in order to make crowdsourcing more reliable, effective, and productive.