EECS 122: Introduction to Computer Networks Evolution of the Internet

1. EECS 122: Introduction to Computer Networks Evolution of the Internet Computer Science Division Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1776

2. R U RDY 4 WOTS NXT?

3. 93 Million Internet Computers Today’s Internet Internet Users 407 Million Automobiles 663 Million Telephones 1.5 Billion X-Internet Electronic Chips 30 Billion “X-Internet” Beyond the PC Forrester Research, May 2001

4. Millions PC Internet X Internet Year “X-Internet” Beyond the PC Forrester Research, May 2001

5. Shape of Things Today: Diverse Appliances and Devices Game Consoles Personal Digital Assistants Digital VCRs Communicators Smart Telephones E-Toys The Old Days All will demand broadband Internet connectivity … and 10BaseT won’t be sufficient

6. Future of the Internet • Mobile IP • Networked Everything: Sensor Nets • Internet Economics

7. Why Mobile IP? • Need a protocol that maintains network connectivity while hosts move between nets • Must avoid massive changes to router software, etc. • Must be compatible with large installed base of IPv4 networks/hosts • Confine changes to mobile hosts and a few support hosts that enable mobility G. G. Richard III, UNO

8. Mobile IP: Basics • Proposed by IETF (Internet Engineering Task Force) • Standards development body for the Internet • Allows a mobile host (MH) to move about without changing its permanent IP address • Each mobile host has a home agent (HA) on its home network • MH establishes a care-of address when it's away from home G. G. Richard III, UNO

9. Mobile IP: Basics • Correspondent host (CH) is a host that wants to send packets to the MH • CH sends packets to the MH’s IP permanent home address • Packets routed to the MH’s home network • HA forwards IP packets for MH to current care-of address • MH sends packets directly to correspondent, using permanent home IP as source IP G. G. Richard III, UNO

10. Mobile IP: Basics correspondent host home agent G. G. Richard III, UNO

11. Mobile IP: Care-of Addresses • When MH connects to a remote network: • Care-of can be the address of a foreign agent (FA) on the remote network • FA delivers packets forwarded from HA to MA • Care-of can be a temporary, foreign IP address obtained through, e.g., DHCP • HA tunnels packets directly to the temporary IP address • Care-of address must be registered with HA G. G. Richard III, UNO

12. IP header IP header data data IP-in-IP Tunneling • Packet to be forwarded is encapsulated in a new IP packet • In the new header: • Destination = care-of-address • Source = address of home agent • Protocol number = IP-in-IP IP header G. G. Richard III, UNO

13. At the Other End... • Depending on type of care-of address: • FA or • MH • … strips outer IP header of tunneled packet, which is then fed to the MH G. G. Richard III, UNO

14. Routing Inefficiency MH and CH may even be on the same network!! correspondent host home agent G. G. Richard III, UNO

15. Route Optimizations • Possible Solution: • HA sends current care-of address to CH • CH caches care-of address • Future packets tunneled directly to care-of address • But … • Cache consistency problem arises ... • Cached care-of address becomes stale when the MH moves • Potential security issues with providing care-of address to correspondent G. G. Richard III, UNO

16. Future of the Internet • Mobile IP • Networked Everything: Sensor Nets • Internet Economics

17. Embedded Sensor Nets: Enabling Technologies Embednumerous distributed devices to monitor and interact with physical world Networkdevices tocoordinate and perform higher-level tasks Embedded Networked Exploitcollaborative Sensing, action Control system w/ Small form factor Untethered nodes Sensing Tightly coupled to physical world Exploit spatially/temporally dense, in situ/remote, sensing/actuation Jim Kurose, UMass

18. Sensor Nets: New Design Themes • Self configuring systems that adapt to unpredictable environment • Dynamic, messy (hard to model) environments preclude pre-configured behavior • Leverage data processing inside the network • Exploit computation near data to reduce communication • Collaborative signal processing • Achieve desired global behavior with localized algorithms (distributed control) • Long-lived, unattended, untethered, low duty cycle systems • Energy a central concern • Communication primary consumer of scarce energy resource Jim Kurose, UMass

19. From Embedded Sensing to Embedded Control • Embedded in unattended “control systems” • Control network, and act in environment • Critical apps extend beyond sensing to control & actuation • Transportation, precision agriculture, medical monitoring and drug delivery, battlefield apps • Concerns extend beyond traditional networked systems and apps: usability, reliability, safety • Need systems architecture to manage interactions • Current system development: one-off, incrementally tuned, stove-piped • Repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scaling Jim Kurose, UMass

20. Why Not Simply Adapt Internet Protocols, “End-to-End” Architecture? • Internet routes data using IP Addresses in Packets and Lookup tables in routers • Humans get data by “naming data” to a search engine • Many levels of indirection between name and IP address • Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection • Special purpose system function(s): don’t need want Internet general purpose functionality designed for elastic applications Jim Kurose, UMass

21. Sample Layered Architecture User Queries, External Database Resource constraints call for more tightly integrated layers Open Question: What are defining Architectural Principles? In-network: Application processing, Data aggregation, Query processing Data dissemination, storage, caching Adaptive topology, Geo-Routing MAC, Time, Location Phy: comm, sensing, actuation, SP Jim Kurose, UMass

22. Sensors • Passive elements: seismic, acoustic, infrared, strain, salinity, humidity, temperature, etc. • Passive Arrays: imagers (visible, IR), biochemical • Active sensors: radar, sonar • High energy, in contrast to passive elements • Technology trend: use of IC technology for increased robustness, lower cost, smaller size • COTS adequate in many of these domains; work remains to be done in biochemical Jim Kurose, UMass

23. Fine Grained Time and Location • Unlike Internet, node time/space location essential for local/collaborative detection • Fine-grained localization and time sync to detect events in 3D and compare detections across nodes • GPS provides solution where available (with diff-GPS providing finer granularity) • GPS not always available, too “costly,” too bulky • Other approaches under study • Localization of sensor nodes has many uses • Beamforming for localization of targets and events • Geographical forwarding • Geographical addressing Jim Kurose, UMass

24. Area coverage: fraction of area covered by sensors Detectability: probability sensors detect moving objects Node coverage: fraction of sensors covered by other sensors Control: Where to add new nodes for max coverage How to move existing nodes for max coverage Coverage Measures D x S Given: sensor field (either known sensor locations, or spatial density) Jim Kurose, UMass

25. In-Network Processing • Communication expensive when limited • Power • Bandwidth • Perform (data) processing in network • Close to (at) data • Forward fused/synthesized results • e.g., find max. of data • Distributed data, distributed computation Jim Kurose, UMass

26. K V K V K V K V K V K V K V K V K V K V Time K V Distributed Representation and Storage • Data Centric Protocols, In-network Processing goal: • Interpretation of spatially distributed data (Per-node processing alone is not enough) • Network does in-network processing based on distribution of data • Queries automatically directed towards nodes that maintain relevant/matching data • Pattern-triggered data collection • Multi-resolution data storage and retrieval • Distributed edge/feature detection • Index data for easy temporal and spatial searching • Finding global statistics (e.g., distribution) Jim Kurose, UMass

27. Directed Diffusion: Data Centric Routing • Basic idea • Name data (not nodes) with externally relevant attributes: data type, time, location of node, SNR, • Diffuse requests and responses across network using application driven routing (e.g., geo sensitive or not) • Support in-network aggregation and processing • Data sources publish data, data clients subscribe to data • However, all nodes may play both roles • Node that aggregates/combines/processes incoming sensor node data becomes a source of new data • Node that only publishes when combination of conditions arise, is client for triggering event data • True peer to peer system? Jim Kurose, UMass

28. Future of the Internet • Mobile IP • Networked Everything: Sensor Nets • Internet Economics

29. The Big Picture Market Structure & Mechanisms Demand Supply Price(s) { Producer Surplus Consumer Surplus Social Surplus Welfare (surplus) John Chueng