Lecture

Information Maximizing Networks

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Description

This lecture delves into the concept of information maximizing networks, exploring the challenges and opportunities in distilling information from unreliable sources. The instructor discusses the importance of estimation theory and information theory in addressing state estimation problems, emphasizing the need for optimization in minimizing errors. The lecture also covers the impact of the information explosion on networks, highlighting the shift towards maximizing information flow rather than throughput. The discussion extends to the implementation of prioritization algorithms in disaster recovery scenarios, such as diversity caching and coverage maximizing transmission. Furthermore, the lecture explores the application of information maximizing principles in participatory sensing services, focusing on optimizing routes for fuel efficiency based on individual car models. The session concludes with insights on updating models and designing namespaces to enhance information transfer in networks.

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Related concepts (31)

Disaster recovery

Disaster recovery is the process of maintaining or reestablishing vital infrastructure and systems following a natural or human-induced disaster, such as a storm or battle. It employs policies, tools, and procedures. Disaster recovery focuses on information technology (IT) or technology systems supporting critical business functions as opposed to business continuity. This involves keeping all essential aspects of a business functioning despite significant disruptive events; it can therefore be considered a subset of business continuity.

Disaster response

Disaster response refers to the actions taken directly before, during or in the immediate aftermath of a disaster. The objective is to save lives, ensure health and safety and to meet the subsistence needs of the people affected. This includes warning/evacuation, search and rescue, providing immediate assistance, assessing damage, continuing assistance and the immediate restoration or construction of infrastructure (i.e. provisional storm drains or diversion dams).

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Combinatorial optimization

Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combinatorial optimization problems are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem. In many such problems, such as the ones previously mentioned, exhaustive search is not tractable, and so specialized algorithms that quickly rule out large parts of the search space or approximation algorithms must be resorted to instead.

Disaster risk reduction

Disaster risk reduction (DRR) sometimes called disaster risk management (DRM) is a systematic approach to identifying, assessing and reducing the risks of disaster. It aims to reduce socio-economic vulnerabilities to disaster as well as dealing with the environmental and other hazards that trigger them.

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