Mobilized ad-hoc networks: A reinforcement learning approach

Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We first motivate the problem by presenting theoretical bounds for the connectivity improvement of partially mobile networks and then present superior empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with adaptive routing policies and learned movement policies

Connectionless routing protocols for mobile ad-hoc networks.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b128054

An Experimental Study of Basic Communication Protocols in Ad-hoc Mobile Networks

We investigate basic communication protocols in ad-hoc mobile networks. We follow the semi-compulsory approach according to which a small part of the mobile users, the support , that moves in a predetermined way is used as an intermediate pool for receiving and delivering messages. Under this approach, we present a new semi-compulsory protocol called the runners in which the members of perform concurrent and continuous random walks and exchange any information given to them by senders when they meet. We also conduct a comparative experimental study of the runners protocol with another existing semi-compulsory protocol, called the snake, in which the members of move in a coordinated way and always remain pairwise adjacent. The experimental evaluation has been carried out in a new generic framework that we developed to implement protocols for mobile computing. Our experiments showed that for both protocols only a small support is required for ecient communication, and that the runners protocol outperforms the snake protocol in almost all types of inputs we considered

An experimental study of basic communication protocols in ad-hoc mobile networks

We investigate basic communication protocols in ad-hoc mobile networks. We follow the semi-compulsory approach according to which a small part of the mobile users, the support Σ, that moves in a predetermined way is used as an intermediate pool for receiving and delivering messages. Under this approach, we present a new semi-compulsory protocol called the runners in which the members of Σ perform concurrent and continuous random walks and exchange any information given to them by senders when they meet. We also conduct a comparative experimental study of the runners protocol with another existing semi-compulsory protocol, called the snake, in which the members of Σ move in a coordinated way and always remain pairwise adjacent. The experimental evaluation has been carried out in a new generic framework that we developed to implement protocols for mobile computing. Our experiments showed that for both protocols only a small support is required for efficient communication, and that the runners protocol outperforms the snake protocol in almost all types of inputs we considered. © Springer-Verlag 2001

Efficient and flexible geocasting for opportunistic networks

With the proliferation of smartphones and their advanced connectivity capabilities, opportunistic networks have gained a lot of traction during the past years; they are suitable for increasing network capacity and sharing ephemeral, localised content. They can also offload traffic from cellular networks to device-to-device ones, when cellular networks are heavily stressed. Opportunistic networks can play a crucial role in communication scenarios where the network infrastructure is inaccessible due to natural disasters, large scale terrorist attacks or government censorship. Geocasting, where messages are destined to specific locations (casts) instead of explicitly identified devices, has a large potential in real world opportunistic networks, however it has attracted little attention in the context of opportunistic networking. In this thesis, we propose Geocasting Spray And Flood (GSAF), a simple but efficient and flexible geocasting protocol for opportunistic, delay tolerant networks. GSAF follows a simple but elegant and flexible approach where messages take random walks towards the destination cast. Messages that follow directions away from the cast are extinct when the device buffer gets full, freeing space for new messages to be delivered. In GSAF, casts do not have to be pre-defined; instead users can route messages to arbitrarily defined casts. Also, the addressed cast is flexible in comparison to other approaches and can take complex shapes in the network. DA-GSAF as the direction aware version of the GSAF is proposed as well which use location information to aid routing decisions in the GSAF. Extensive evaluation shows that GSAF and DA-GSAF are significantly more efficient than existing solutions, in terms of message delivery ratio and latency as well as network overhead

Approaches to multi-agent learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 165-171).Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing environments they find themselves in. This thesis explores ideas from game theory, online prediction, and reinforcement learning, tying them together to work on problems in multi-agent learning. We begin with the most basic framework for studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can exploit opponents that satisfy certain assumptions, and in a later chapter, we show how we can avoid being exploited ourselves. From this beginning, we branch out to study more complex sequential decision making problems in multi-agent systems, or stochastic games. We study environments in which there are large numbers of agents, and where environmental state may only be partially observable.(cont.) In fully cooperative situations, where all the agents receive a single global reward signal for training, we devise a filtering method that allows each individual agent to learn using a personal training signal recovered from this global reward. For non-cooperative situations, we introduce the concept of hedged learning, a combination of regret-minimizing algorithms with learning techniques, which allows a more flexible and robust approach for behaving in competitive situations. We show various performance bounds that can be guaranteed with our hedged learning algorithm, thus preventing our agent from being exploited by its adversary. Finally, we apply some of these methods to problems involving routing and node movement in a mobilized ad-hoc networking domain.by Yu-Han Chang.Ph.D