CROWN “Thales” project Optimal ContRol of self-Organized Wireless Networks WP1 Understanding and influencing uncoordinated interactions of autonomic wireless networks Iordanis Koutsopoulos

WP1 overview Leader: U of Thessaly Duration: M1-M30 Person Months – UTH: 30 – NKUA: 10 – AUEB: 10

WP1 Objectives Understand and optimize fundamental tradeoffs about creation and evolution of self-awareness – crucial accuracy-energy-latency-overhead tradeoff which has direct ramifications to efficient wireless network management. Fortify autonomic network operation by efficiently coping with resource conflicts, selfishness and competition – Predict stable operating points emerging from interaction – guide network to the desired /optimal operating point in terms of derived utility and energy consumption. – Spontaneous cooperation among nodes

WP1 Structure Task 1.1: Efficient real-time learning and information extraction amidst uncertainties and partial information Task 1.2: Predicting and resolving conflicts in wireless networks through non-cooperative game theory Task 1.3: Spontaneous cooperation in un- coordinated autonomic wireless networks

Task 1.1: Efficient real-time learning and information extraction amidst uncertainties and partial information Framework for reliable and efficient information extraction and inference in uncertain / time-varying wireless networks – Optimize process of in-network feedback collection, to be inserted to the network management loop Optimize real-time learning – Information aggregation / fusion – Inference rules Learning: gradually becoming aware of surroundings – Spectrum availability, link volatility – Neighboring node traffic patterns, locations of traffic congestion,…

Task 1.1: Efficient real-time learning and information extraction amidst uncertainties and partial information Challenges: – stochastic environment (errors, dynamicity) – Delayed or outdated state information – Feedback collection and management across multiple dimensions (frequency channels, links, neighbors, time scales) with limited resources Nodes should gradually develop belief about state and decide accordingly Inherent tradeoffs in learning: learning quality vs. delay Tools: – Estimation and detection theory – Partially observable Markov Decision Processes – Multi-armed bandit theory – Machine learning – Network optimization

Task 1.2: Predicting and resolving conflicts in wireless networks through non-cooperative game theory Use concepts and methods from non-cooperative game theory to model and analyze node interaction in autonomic wireless networks Exemplify to: – spectrum trading for access – Storage capacity management in cloud systems – scheduling, route selection, PHY layer transmission adaptation, source rate control, selection of nodes to request network state information from

Task 1.2: Predicting and resolving conflicts in wireless networks through non-cooperative game theory Simple models to capture node behavior profile (ranging from egotistic, altruistic, malicious, …) – Bounded rationality Predict the stable outcome of node interactions Mechanism design to drive interaction to specific equilibrium points Devise methods that drive node interaction to desirable equilibrium points through mechanism design – Pricing mechanisms to penalize or reward selected user strategies – Auctions Main attractive feature of actions: achieve desired resource allocation goal (E.g maximize social welfare) while agnostic to utility functions Tools : – Non-cooperative game theory – Mechanism design – Auction theory – Network optimization

Basic Auction Types Seller Demand Auction Buyers Supply Auction Sellers Buyer Double Auction Sellers Buyers bids Ask bids bids 9 Single sided auction Double sided auction

Task 1.3: Spontaneous cooperation in uncoordinated autonomic wireless networks Consider possibility for spontaneous cooperation among nodes, if mutual benefits can be attained Allow for negotiations among nodes until they reach a mutually agreeable point, and subsequent commitments – Stability of coalitions? – How to enforce or discourage coalitions? – Coalition profit sharing Exemplify for cases where resources are pooled (spectrum, service capacity, processing capacity, storage capacity) Tools: – Cooperative (coalitional) game theory – Negotiation and alternate offer theory