Mobile Ad hoc Networks COE 549 Introduction to Sensor Networks

1. Mobile Ad hoc Networks COE 549Introduction to Sensor Networks Tarek Sheltami KFUPM CCSE COE www.ccse.kfupm.edu.sa/~tarek

2. Outline • Introduction • Application Areas • Systems Involved • Communications • Challenges in SNETs • Unique constraints • Power Issues

3. Involved Technologies Network Technology Sensor Network Computational Power Sensor Technology

4. Application Areas • Military • Infrastructure security • Environment & Habitat Monitoring • Industrial Sensing • Traffic Control • Seismic Studies • Life Sciences

5. The Systems involved • Sensor Node Internals • Operating System • Physical Size

6. Sensor Node Internals CPU INFRARED ACOUSTIC SEISMIC IMAGE MAGNETIC… POWER SUPPLY ELECTRO-MAGNETIC INTERFACE SENSOR COMMUNICATION NODE • Some Current Node Platforms: • Sensoria WINS • Smart Dust – Dust Inc. Berkeley • UC Berkeley mote – Crossbow (www.xbow.com)

7. Operating System - TinyOS • Custom built at UC, Berkeley for wireless sensor nodes • Component-based architecture: ensures minimum code size • Component library includes: • Network protocols • Sensor drivers • Data acquisition tools • Distributed services

8. Physical Size WINS NG 2.0 Berkley Motes AWAIRS I LWIM III AWACS

9. Communication • Network Protocol • Network Discovery • Network Control & Routing

10. Network Protocol • For wireless sensor networks: IEEE 802.11 standards • Personal Area Networks (PAN): IEEE 802.15 standard • Radius of 5 to 10m • Ideal application in short-range sensors

11. Network Discovery • Knowledge of identity and location of its neighbor • Ad hoc protocols can be used • GPS system can be used as well

12. Network Control & Routing • Network adapts dynamically to • conserve resources like energy and available nodes • Make optimum use of bandwidth and processing power • Connectivity must emerge as needed from algorithms • Directed Diffusion routing • Data identity is separate from node identity • Promotes adaptive, in-network processing

13. Sensors • Passive elements: seismic, acoustic, infrared, strain, 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

14. Sensor Network Challenges • Low computational power • Current mote processors run at < 10 MIPS (Microprocessor without Interlocked Pipeline Stages) • Not enough horsepower to do real signal processing • Memory not enough to store significant data • Poor communication bandwidth, current radios achieve about 10 Kbps per mote • Note that raw channel capacity is much greater Overhead due to CSMA backoff, noise floor detection, start symbol, etc. • 802.15.4 (Zigbee) radios now available at 250 Kbps • But with small packets one node can only transmit around 25 kbps

15. Sensor Network Challenges.. • Limited energy budget • 2 AA motes provide about 2850 mAh • Coin-cell Li-Ion batteries provide around 800 mAh • Solar cells can generate around 5 mA/cm2 in direct sunlight • Must use low duty cycle operation to extend lifetime beyond a few days

16. Sensor Network Challenges.. • Portable, energy-efficient devices • End-to-end quality of service • Seamless operation under context changes • Context-aware operation • Secure operation • Sophisticated services for simple clients

17. Unique Aspects • Number of sensor nodes can be many orders of magnitude larger than number of nodes in an ad hoc network • Tens of thousands. • But individual ID might not be needed. • Sensors might be very small, cheap, and prone to failure. • Therefore, we need redundancy. • Extremely limited in power, and must stay operative for long time • Energy harvesting might be considered. • Sensors might be densely deployed. • Opportunity for using redundancy to improve the robustness of the system

18. Unique Aspects.. • Very limited mobility • Helps with the design of the protocols • Measurements might be correlated. • Example: measurements of temperature, pressure, humidity, etc. • Volume of transmitted data might be greatly reduced. • For many applications, nodes are randomly deployed. • Thrown by a plane, carried by wind, etc.

19. Location-dependent Information • Changing context • small movements may cause large changes • caching may become ineffective • dynamic transfer to nearest server for a service

20. Portability • Power is key • long mean-time-to-recharge, small weight, volume • Risk to data due to easier privacy breach • network integrated terminals with no local storage • Small user interfaces • small displays, analog inputs (speech, handwriting) instead of buttons and keyboards • Small storage capacity • data compression, network storage, compressed virtual memory, compact scripts vs. compiled code

21. Low Power & Energy-awareness • Battery technology is a hurdle… • Typical laptop: 30% display, 30% CPU, 30% rest • wireless communication and multimedia processing incur significant power overhead • Low power • circuits, architectures, protocols • Power management • Right power at the right place at the right time • Battery model

22. Low Power & Energy-awareness.. • There are many means for powering nodes, although the reality is that various electrical sources are by far the most convenient. • Technology trends indicate that within the lifetime of CENS, nodes will likely be available that could live off ambient light. • However, this cannot be accomplished without aggressive energy management at many levels; continuous communications alone would exceed the typical energy budgets.

23. Source: ISI & DARPA PAC/C Program Sensor Node Energy Roadmap 10,000 1,000 100 10 1 .1 Rehosting to Low Power COTS (10x) • Deployed (5W) • PAC/C Baseline (.5W) Average Power (mW) • (50 mW) -System-On-Chip -Adv Power Management Algorithms (50x) (1mW) 2000 2002 2004

24. Battery Technology • Battery technology has historically improved at a very slow pace • NiCd improved by x2 over 30 years! • require breakthroughs in chemistry

25. Source: UC Berkeley Comparison of Energy Sources With aggressive energy management, ENS might live off the environment.

26. Computation & Communication Energy breakdown for MPEG Energy breakdown for voice Decode Decode Transmit Encode Encode • Radios benefit less from technology improvements than processors • The relative impact of the communication subsystem on the system energy consumption will grow Receive Receive Transmit Radio: Lucent WaveLAN at 2 Mbps Processor: StrongARM SA-1100 at 150 MIPS

27. Power Analysis of Mote-Like Node

28. Key Issue: Resource Awareness Inherent unpredictability Wireless Backbone Networks • High traffic load • Limited available spectrum Focus on transmission resources Solution: adaptation Resource awareness “right resource at the right time and the right place” • Wireless Ad-Hoc Networks • Unattended operation • Limited available battery • Focus on energy resources

29. Event Driven Model

30. On-Demand Model

31. TinyOS is an open-source operating system designed for wireless embedded sensor networks. It features a component-based architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools – all of which can be used as-is or be further refined for a custom application. TinyOS's event-driven execution model enables fine-grained power management yet allows the scheduling flexibility made necessary by the unpredictable nature of wireless communication and physical world interfaces. TinyOS has been ported to over a dozen platforms and numerous sensor boards. A wide community uses it in simulation to develop and test various algorithms and protocols. New releases see over 10,000 downloads. Over 500 research groups and companies are using TinyOS on the Berkeley/Crossbow Motes. Numerous groups are actively contributing code to the sourceforge site and working together to establish standard, interoperable network services built from a base of direct experience and honed through competitive analysis in an open environment.