Introduction to Embedded Networks Course CSE591 Spring 2007

Sandeep K. S. GuptaArizona State University

Embedded Networks?

• Networks embedded in some natural phenomenon:– Biological networks– Social networks

or• Man-made Networks (designed for particular

application):– Wireless sensor networks for health monitoring

Some Motivating Questions

• “What do metabolic pathways and ecosystem, the Internet, and propagation of HIV infection have in common?”

• “Survival of living cells and organisms is largely based on highly reliable functions of their regulatory networks. However, the elements of biological networks, e.g. regulatory genes in genetic networks or neurons in nervous system, are far from being reliable dynamical elements. How can networks of unreliable elements perform reliably?”

Wireless Sensor Nets –Desirable Properties

• Self-configuring

• Adaptive

• Resilient to attacks

• Energy-efficient

• Scalability

Complex Systems/Networks• “Complex systems" has many components that interact in an

interesting way. More formally, a phenomenon in the social, life, physical or decision sciences is considered a complex system if it has a significant number of the following characteristics:

– Agent-based: The basic building blocks are the characteristics and activities of the individual agents in the environment under study.

– Heterogeneous: These agents differ in important characteristics. – Dynamic: These characteristics that change over time, as the agents adapt

to their environment, learn from their experiences, or experience natural selection in the regeneration process. The dynamics that describe how the system changes over time are usually nonlinear, sometimes even chaotic. The system is rarely in any long run equilibrium.

– Feedback: These changes are often the result of feedback that the agents receive as a result of their activities.

– Organization: Agents are organized into groups or hierarchies. These organizations are often rather structured, and these structures influence how the underlying system evolves over time.

– Emergence: The overlying concerns in these models are the macro-level behaviors that emerge from the assumptions about the actions and interactions of the individual agents.

• From http://www.cscs.umich.edu/old/complexity.html

Complex (natural) systems vis-à-vis Man-made systems• The common characteristic of all complex systems is that they display

organization without any external organizing principle being applied; a central characteristic is adaptability.

• For engineers the conceptual conflict may arise from the fact that the hallmark of complex systems is adaptability and emergence: No one designed the web, the US power grid, or the metabolic processes within a cell. Engineering is not about letting systems be. The etymology of “engineer,” both the verb and the noun, is revealing: ingenitor, contriver, ingenire, to contrive, as in to engineer a scheme.

• Engineering has a purpose and end result. Engineering is about convergence, assembling pieces that work in specific ways, optimum design and consistency of operation; the central metaphor is a clock.

• Complex systems, on the other hand, are about adaptation, self-organization and continuous improvement; the best metaphor may be an ecosystem.

• It is robustness and failure where both camps merge. However, a successful merger will require augmenting the conceptual framework, even to the point of reshaping what one means by prediction.

Network Science• “Networks are all around us, all the time. From the biochemistry

of our cells to the web of friendships across the planet. From the circuitry of modern electronics to chains of historical events. A network is the result of the forces that shaped it. Thus the principles of network formation can be, to some extent, deciphered from the network itself. All such information comprises the structure of the network. The study of network structure is the core of modern network science. This thesis centres around three aspects of network structure: What kinds of network structures are there and how can they be measured? How can we build models for network formation that give the structure of networks in the real world? How does the network structure affect dynamical systems connected to the networks? These questions are discussed using a variety of statistical, analytical and modelling techniques developed by physicists, mathematicians, biologists, chemists, psychologists, sociologists and anthropologists.” From Forms and Function of Complex Networks by Petter Holme, 2004

Course Goals

1. To understand the fundamental properties which ensure robustness and scalability of natural and man-made embedded networked systems

2. To apply these principles in the design of dependable sensor and actuator based embedded systems for diverse application domains including medical, disaster management, and homeland security

Course Goals - Indirect

• To enrich your on-going research with new ideas

• To find an interesting research topic

• To learn to do research, present ideas, interact with peers etc.

• To learn to question/critique published work

Course Goals

• To have fun exploring, learning without worrying about grades

Course Topics• Theoretical foundations

– Graph theory, information theory, game theory • Properties of Complex Networks

– scale-free and small-world networks• Engineering Design Principles

– Energy-latency tradeoff, layering principle• Network types

– Social, biological, informational, man-made• Wireless Sensor Network design and algorithms

– topology control, localization• Application-specific Networking Requirements

– In-vivo biosensor networks should minimize harm to surrounding tissue

Course Mechanics

• No exams!• Grading:

– Assignments (50%)• Presentation – at least one presentation on theory• Summary/critique • Problem sets – may involve some programming

– Project (report & presentation) 50%• Experimental• Theoretical analysis• In-depth survey• Innovative design/algorithm

Ground Rules

• Present only what you understand and can clearly explain to others

• You can select to present only part of the paper which interests you

• Select your paper at least one week in advance• Present background theory which is needed to

understand the ideas in paper your are presenting• Bring some questions for discussion• Even if you are not presenting a paper you should at

least skim it before coming to class.

Contacting Me

• Email: sandeep.gupta@asu.edu• Office: BY 522• Phone: 5-3806• Office Hours: MW 3-4:30pm• http://impact.asu.edu

IMPACT research on EN

• Biosensor networking

• Health-monitoring network

• Network for Shipping Container Tracking

• Criticality-aware networking

Next Class

• Start thinking about what topic you would like to focus on

• Some planning regarding who, when, what for presentation

• Continue with “Structure and Functions of Complex Networks”