39 research outputs found
Systems-compatible Incentives
Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests.
In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible.
The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants
Cooperative Internet access using heterogeneous wireless networks
Ph.DDOCTOR OF PHILOSOPH
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Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control
Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part
Private and censorship-resistant communication over public networks
Society’s increasing reliance on digital communication networks is creating unprecedented opportunities for wholesale
surveillance and censorship. This thesis investigates the use of public networks such as the Internet to build
robust, private communication systems that can resist monitoring and attacks by powerful adversaries such as national
governments.
We sketch the design of a censorship-resistant communication system based on peer-to-peer Internet overlays in which
the participants only communicate directly with people they know and trust. This ‘friend-to-friend’ approach protects
the participants’ privacy, but it also presents two significant challenges. The first is that, as with any peer-to-peer
overlay, the users of the system must collectively provide the resources necessary for its operation; some users might
prefer to use the system without contributing resources equal to those they consume, and if many users do so, the
system may not be able to survive.
To address this challenge we present a new game theoretic model of the problem of encouraging cooperation between
selfish actors under conditions of scarcity, and develop a strategy for the game that provides rational incentives for
cooperation under a wide range of conditions.
The second challenge is that the structure of a friend-to-friend overlay may reveal the users’ social relationships to
an adversary monitoring the underlying network. To conceal their sensitive relationships from the adversary, the
users must be able to communicate indirectly across the overlay in a way that resists monitoring and attacks by other
participants.
We address this second challenge by developing two new routing protocols that robustly deliver messages across
networks with unknown topologies, without revealing the identities of the communication endpoints to intermediate
nodes or vice versa. The protocols make use of a novel unforgeable acknowledgement mechanism that proves that a
message has been delivered without identifying the source or destination of the message or the path by which it was
delivered. One of the routing protocols is shown to be robust to attacks by malicious participants, while the other
provides rational incentives for selfish participants to cooperate in forwarding messages
Extending cloud-based applications in challenged environments with mobile opportunistic networks
With the tremendous growth of mobile devices, e.g, smartphones,
tablets and PDAs in recent years, users are looking for more advanced
platforms in order to use their computational applications
(e.g., processing and storage) in a faster and more convenient
way. In addition, mobile devices are capable of using cloud-based
applications and the use of such technology is growing in popularity.
However, one major concern is how to efficiently access these
cloud-based applications when using a resource-constraint mobile
device. Essentially applications require a continuous Internet connection
which is difficult to obtain in challenged environments
that lack an infrastructure for communication (e.g., in sparse
or rural areas) or areas with infrastructure (e.g., urban or high
density areas) with restricted/full of interference access networks
and even areas with high costs of Internet roaming. In these situations
the use of mobile opportunistic networks may be extended
to avail cloud-based applications to the user.
In this thesis we explore the emergence of extending cloud-based
applications with mobile opportunistic networks in challenged
environments and observe how local user’s social interactions
and collaborations help to improve the overall message delivery
performance in the network. With real-world trace-driven simulations,
we compare and contrast the different user’s behaviours in
message forwarding, the impact of the various network loads (e.g.,
number of messages) along with the long-sized messages and the
impact of different wireless networking technologies, in various
opportunistic routing protocols in a challenged environment