Networked CPS: Some Fundamental Challenges

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Howard Dixon
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1 Networked CPS: Some Fundamental Challenges John S. Baras Institute for Systems Research Department of Electrical and Computer Engineering Fischell Department of Bioengineering Department of Mechanical Engineering Applied Mathematics, Statistics and Scientific Computation Program University of Maryland College Park Panel on Networking challenges for CPS 2014 INFOCOM Toronto, Canada, May 1, 2014

2 Wireless and Networked Embedded Systems: Ubiquitous Presence 2

3 A Network Immersed World: Swarms and the Cloud Courtesy: J. Rabaey 3

4 Networked CPS: Wireless Sensor Networks Everywhere 4

5 Networked CPS: Smart Grids Courtesy: Rockwell 5

6 Networked CPS: FAA NextGen 6

7 Networked CPS: Autonomous Swarms Component-based Architectures Communication vs Performance Tradeoffs Distributed asynchronous Fundamental limits At work: Two ASIMOs working together in coordination to deliver refreshments Credit: Honda 7

8 A Network Immersed World A complex collection of sensors, controllers, computing nodes, and actuators that work together to improve our daily lives From very small: Ubiquitous, Pervasive, Disappearing, perceptive, Ambient To very large: Always Connectable, Reliable, Scalable, Adaptive, Flexible Emerging Service Models Building energy management Automotive safety and control Management of metropolitan traffic flows Distributed health monitoring Smart Grid 8

9 Networked CPS Architecture: Multiple Interacting Multigraphs Multiple Interacting Graphs Nodes: agents, individuals, groups, organizations Directed graphs Links: ties, relationships Weights on links : value (strength, significance) of tie Weights on nodes : importance of node (agent) Value directed graphs with weighted nodes Real life problems: Dynamic, time varying graphs, relations, weights, policies Information network I w kl Communication network S j : w j S w ij : I k: w l: w l I k C m: w m C w mn i w i n: w n Networked System architecture & operation C S 9

10 Networks: The Fundamental Trade off The nodes gain from collaborating But collaboration has costs (e.g. communications) Trade off: gain from collaboration vs cost of collaboration Vector metrics involved typically Constrained Coalitional Games Example 1: Network Formation Effects on Topology Example 2: Collaborative robotics, communications Example 3: Web based social networks and services Example 4: Groups of cancer tumor or virus cells 10

11 Efficient Communication Graphs: Small World Graphs Simple Lattice C(n,k) Small world: Slight variation adding nk Adding a small portion of well chosen links significant increase in convergence rate 11

12 Efficient Communication Graphs: Expander Graphs Fast synchronization of a network of oscillators Network where any node is nearby any other Fast diffusion of information in a network Fast convergence of consensus Decide connectivity with smallest memory Random walks converge rapidly Easy to construct, even in a distributed way (ZigZag graph product) Graph G, Cheeger constant h(g) All partitions of G to S and S c, h(g)=min (#edges connecting S and S c ) / (#nodes in smallest of S and S c ) (k, N, e) expander : h(g) > e ; sparse but locally well connected (1 SLEM(G) increases as h(g) 2 ) 12

13 Expander Graphs Ramanujan Graphs 13

14 Construction of Efficient Communication Graphs by Computational Optimization Examples of resulting topologies 14

15 Distributed self organization Goal: design a scheme that gives each node a vector of compact global information 15

16 Component Based Networking and Security Component Based Networking : Leads to a compositional approach to the synthesis and operation of networks. Fits very well MANET, WNAN (and WAND), beyond. Does away with classical layers and with classical cross layer Compositionality, and Compositional Synthesis Cross linked executable, formal and performance models is addressing this challenging problem directly. Interacting Control, Information and Communication Graphs 16

17 Executable Models Component base Networks and Composable Security Formal Models Universally Composable Security of Network Protocols: Network with many agents running autonomously. Agents execute in mostly asynchronous manner, concurrenty several protocols many times. Protocols may or may have not been jointly designed, may or not be all secure or secure to same degree. Performance Models Key question addressed : Under what conditions can the composition of these protocols be provably secure? Investigate time and resource requirements Studying compositionality is necessary! Compositional Security is critical for all CPS! for achieving this 17

18 Trust and Collaborative Control/Operation Two linked dynamics Trust / Reputation propagation and collaborative control evolution Integrating network utility maximization (NUM) with constraint based reasoning and coalitional games Beyond linear algebra and weights, semirings of constraints, constraint programming, soft constraints semirings, policies, agents Learning on graphs and network dynamic games: behavior, adversaries Adversarial models, attacks, constrained shortest paths, Interacting Control, Information and Communication Graphs 18

Biological Network Types Examples of biological networks: [A] Yeast transcription factor binding network; [B] Yeast protein protein interaction network; [C]

19 Biological Network Types Examples of biological networks: [A] Yeast transcription factor binding network; [B] Yeast protein protein interaction network; [C] Yeast phosphorylation network ; [D] E. Coli metabolic network ; [E] Yeast genetic network ; Nodes colored according to their YPD cellular roles [Zhu et al, 2007] 19

20 How Biology Does IT? 20

21 Modularity vs Performance Optimize only on performance poor adaptivity Add cost of communications improved adaptivity Communication motifs Evolvable modularity for some networked CPS?? 21

22 Neural Network Evolution: from programmed structure to function feedback on structure 22

markets Monetize the value of social network Advertising - major source of income

23 Social Networks as Networked CPS We are much more social than ever before Online social networks (SNS) permeate our lives Such new Life style gives birth to new markets Monetize the value of social network Advertising - major source of income for SNS Joining fee, donation etc. Need to know the common features of social networks 23

24 Challenges in Social Networks Can we integrate? Context based distribution Include user and product similarity Combine with user user similarity Exploit both user preferences and network structure Maximize relevance and potential profit Ensure message delivery to all interested nodes Increase recommendation accuracy and diversity Can hyperbolic embedding help? Is it real? 24

25 Key Idea: Virtual Geometry Of the network graph Of an auxiliary space underlying the graph 25

26 Possible Underlying Hyperbolic Geometry? 26

27 Navigation Efficiency and Robustness Percentage of successful greedy paths 99.99% Percentage of shortest greedy paths 100% Percentage of successful greedy paths after removal of x% of links or nodes x =10% 99% x =30% 95% 27

28 Taxonomy for Large Scale Networks based on Curvature 1987 Gromov: d hyperbolicity Courtesy: Iraj Saniee, Bell Labs 28

29 Thank you! Questions?

Networked Cyber-Physical Systems (Net-CPS) and the Internet of Things (IoT)

Networked Cyber-Physical Systems (Net-CPS) and the Internet of Things (IoT) John S. Baras Institute for Systems Research, University of Maryland, USA ACCESS Linnaeus Center, Royal Institute of Technology,