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Introduction to Wireless Sensor Networks Marco Zennaro and Antoine Bagula ICTP and UWC Italy and South Africa Infrastructure-based networks Typical wireless network: Based on infrastructure (E.g., GSM, UMTS, WiFi, ) Base stations

  1. Introduction to Wireless Sensor Networks Marco Zennaro and Antoine Bagula ICTP and UWC Italy and South Africa

  2. Infrastructure-based networks Typical wireless network: Based on infrastructure (E.g., GSM, UMTS, WiFi, … ) Base stations connected to a wired backbone network. Mobile entities communicate wirelessly to these base stations Mobility is supported by switching from one base station to another

  3. Infrastructure-less networks What happens when: ● No infrastructure is available? – E.g., in remote areas ● It is too expensive/inconvenient to set up? – E.g., in remote sites ● There is no time to set it up? – E.g., in disaster relief operations

  4. Infrastructure-less networks We try to construct a network without infrastructure, using networking abilities of the participants This is an ad hoc network – a network constructed “for a special purpose” Without a central entity (like a base station), participants must organize themselves into a network (self-organization)

  5. Challenges for ad hoc networks Without a central infrastructure, things become much more difficult! Problems are due to ● Lack of central entity for organization available ● Limited range of wireless communication ● Mobility of participants ● Battery-operated entities

  6. Wireless sensor networks A Wireless Sensor Network is a self-configuring network of small sensor nodes communicating among themselves using radio signals, and deployed in quantity to sense, monitor and understand the physical world. Wireless Sensor nodes are called motes .

  7. Wireless sensor networks WSN provide a bridge between the real physical and virtual worlds. Allow the ability to observe the previously unobservable at a fine resolution over large spatio-temporal scales. Have a wide range of potential applications to industry, science, transportation, civil infrastructure, and security.

  8. Wireless sensor networks log (people per computer) 0 1960 1970 1980 1990 2000 2010 [Culler:2004]

  9. Wireless sensor networks Next Century Challenges: Mobile Networking for “ Smart Dust ” J. M. Kahn, R. H. Katz, K. S. J. Pister (MobiCom 1999)

  10. Mote Anatomy

  11. Mote Anatomy Processor in various modes (sleep, idle, active) Power source (AA or Coin batteries, Solar Panels) Memory used for the program code and for in- memory buffering Radio used for transmitting the acquired data to some storage site Sensors for temperature, humidity, light, etc

  12. Mote Anatomy

  13. Mote Anatomy

  14. Mote Anatomy These motes are highly constrained in terms of ● Physical size ● CPU power ● Memory (few tens of kilobytes) ● Bandwidth (Maximum of 250 KB/s) Power consumption is critical ● If battery powered then energy efficiency is paramount May operate in harsh environments ● Challenging physical environment (heat, dust, moisture, interference)

  15. Potential of WSN US National Research Council report ("Embedded Everywhere"): the use of wireless sensor networks (WSN) could well dwarf previous milestones in the information revolution. MIT’s Technology Review in February 2003 predicted: WSN will be one of the most important technologies in the near future. Nature, in the “2020 computing: Everything, everywhere” report, said that WSN are going to be one of the most interesting technologies!

  16. Potential of WSN - 2007 The Economist, in April 2007, had an issue called “When everything connects”.

  17. Potential of WSN - 2013 Cisco Says its “Internet of Everything” is worth $14.4 Trillion.

  18. Potential of WSN - research 2005 2013

  19. A World of Sensors Predictive Maintenance High-Confidence Transport and Energy Saving Asset Tracking Smart Grid Improve Productivity Intelligent Buildings Enable New Knowledge Enhanced Safety & Security Improve Food and H 2 O Healthcare Smart Home SS 05

  20. WSN application examples Intelligent buildings Reduce energy wastage by proper humidity, ventilation, air conditioning (HVAC) control Needs measurements about room occupancy, temperature, air flow, … Monitor mechanical stress after earthquakes

  21. WSN application examples Bridge Monitoring In California, 13% of the 23,000 bridges have been deemed structurally deficient, while 12% of the nation's 600,000 bridges share the same rating. New York may be the first state with a 24/7 wireless bridge monitoring system.

  22. WSN application examples

  23. WSN application examples Disaster relief operations Drop sensor nodes from an aircraft over a wildfire Each node measures temperature Derive a “temperature map” Biodiversity mapping Use sensor nodes to observe wildlife

  24. WSN application - Zebranet ZebraNet: an application to track zebras on the field The objective of the application is to gather dynamic data about zebra positions in order to understand their mobility patterns. What are the motivations for the zebras to move? water? food? weather? How do they interact? The sensors are deployed in collars that are carried by the animals. The users are the biologists.

  25. WSN application - Zebranet

  26. WSN application - Zebranet [Princeton, 2004]

  27. WSN application - Zebranet Z e b r a s d o n ' t l i k e collars! Well... who likes collars? The zebras rip off the solar cells from the collar in less than one week! After that, the batteries died...

  28. WSN application - Volcano

  29. WSN application - Volcano Reference: “Deploying a Wireless Sensor Network on an Active Volcano”, Geoffrey Werner-Allen, Konrad Lorincz, Matt Welsh, Omar Marcillo, Jeff Johnson, Mario Ruiz, Jonathan Lees, IEEE Internet Computing, Mar/Apr 2006 Tungurahua, Ecuador

  30. WSN application - Volcano

  31. WSN application - Volcano

  32. WSN application - Volcano Challenges Encountered Event detection: when to start collecting data? High data rate sampling Spatial separation between nodes Data transfer performance: reliable transfer required Time synchronization: data has to be time- aligned for analysis by seismologists

  33. WSN application - Agriculture Agriculture e.g., TU Delft Deployment

  34. WSN application - Medicine [CodeBlue: Harvard]

  35. WSN application - roles Sources of data: measure data, report them “somewhere” Sinks of data: interested in receiving data from WSN Actuators : control some device based on data, usually also a sink

  36. WSN application - patterns Interaction patterns between sources and sinks classify application types: Event detection : Nodes locally detect events (maybe jointly with nearby neighbors), report these events to interested sinks Periodic measurement Function approximation : Use sensor network to approximate a function of space and/or time (e.g., temperature map)

  37. WSN application - patterns Interaction patterns between sources and sinks classify application types: Edge detection : Find edges (or other structures) in such a function (e.g., where is the zero degree border line?) Tracking : Report (or at least, know) position of an observed intruder (“pink elephant”)

  38. WSN application - deployment How are sensor nodes deployed in their environment? Dropped from aircraft: Random deployment Usually uniform random distribution for nodes over finite area is assumed Is that a likely proposition? Well planned, fixed: Regular deployment E.g., in preventive maintenance or similar Not necessarily geometric structure, but that is often a convenient assumption

  39. WSN application - deployment How are sensor nodes deployed in their environment? Mobile sensor nodes Can move to compensate for deployment shortcomings Can be passively moved around by some external force (wind, water) Can actively seek out “interesting” areas

  40. WSN application - requirements Scalability Support large number of nodes Wide range of densities Vast or small number of nodes per unit area Programmability Re-programming of nodes in the field might be necessary, improve flexibility Maintainability WSN has to adapt to changes, self-monitoring, adapt operation

  41. Internet of Things

  42. Internet of Things

  43. What is a Smart Object? A tiny and low cost computer that may contain: A sensor that can measure physical data (e.g., temperature, vibration, pollution) An actuator capable of performing a task (e.g., change traffic lights, rotate a mirror) A communication device to receive instructions , send data or possibly route information This device is embedded into objects For example, thermometers, car engines, light switches, gas meters We now talk about Internet of Things

  44. Internet of Things

  45. Internet of Things

  46. IPv4 or IPv6 Smart Objects will add tens of billions of additional devices There is no scope for IPv4 to support Smart Object Networks IPv6 is the only viable way forward Solution to address exhaustion Stateless Auto-configuration thanks to Neighbour Discovery Protocol Each embedded node can be individually addressed/accessed

  47. Smart Objects World 7.6 Billion Population 6.3 Billion 6.8 Billion 7.2 Billion Connected 500 Million 12.5 Billion 25 Billion 50 Billion Devices Connected More connected devices than people Devices 0.08 1.84 3.47 6.58 Per Person 2008 2003 2010 2015 2020

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