Introduction to Wireless Sensor Networks

1
Introduction to Wireless Sensor
Networks -Research Problems (Clustering,
Routing, etc)
2
Energy-Efficient Communication Protocol
Architecture for Wireless Microsensor Networks
(LEACH Protocol) Heinzelman 2000, 2002

• LEACH (Low-Energy Adaptive Clustering Hierarchy)
  is a clustering-based protocol that utilizes the
  randomized rotation of local cluster base
  stations to evenly distribute the energy load
  within the network of sensors
• It is a distributed, does not require any control
  information from base station (BS) and the nodes
  do not need to have knowledge of global network
  for LEACH to function
• The energy saving of LEACH is achieved by
  combining compression with data routing
• Key features of LEACH include
• Localized coordination and control of cluster
  set-up and operation
• Randomized rotation of the cluster base stations
  or clusterheads and their clusters
• Local compression of information to reduce global
  communication

3
LEACH Heinzelman 2000, 2002

• Considered microsensor network has the following
  characteristics
• The base station is fixed and located far from
  the sensors
• All the sensor nodes are homogeneous and energy
  constrained
• Communication between sensor nodes and the base
  station is expensive and no high energy nodes
  exist to achieve communication
• By using clusters to transmit data to the BS,
  only few nodes need to transmit for larger
  distances to the BS while other nodes in each
  cluster use small transmit distances
• LEACH achieves superior performance compared to
  classical clustering algorithms by using adaptive
  clustering and rotating clusterheads assisting
  the total energy of the system to be distributed
  among all the nodes
• By performing load computation in each cluster,
  amount of data to be transmitted to BS is
  reduced. Therefore, large reduction in the energy
  dissipation is achieved since communication is
  more expensive than computation

4
LEACH Heinzelman 2000, 2002

• Algorithm Overview
• The nodes are grouped into local clusters with
  one node acting as the local base station (BS) or
  clusterhead (CH)
• The CHs are rotated in random fashion among the
  various sensors
• Local data fusion is achieved to compress the
  data being sent from clusters to the BS
  resulting the reduction in the energy dissipation
  and increase in the network lifetime
• Sensor elect themselves to be local BSs at any
  any given time with a certain probability and
  these CHs broadcast their status to other sensor
  nodes
• Each node decided which CH to join based on the
  minimum communication energy
• Upon clusters formation, each CH creates a
  schedule for the nodes in its cluster such that
  radio components of each non-clusterhead node
  need to be turned OFF always except during the
  transmit time
• The CH aggregates all the data received from the
  nodes in its cluster before transmitting the
  compressed data to BS

5
LEACH Heinzelman 2000, 2002

• Algorithm Overview
• The transmission between CH and BS requires high
  energy transmission
• In order to evenly distribute energy usage among
  the sensor nodes, clusterheads are self-elected
  at different time intervals
• The nodes decides to become a CH depending on the
  amount of energy it has left
• The decisions to become CH are made
  independently of the other nodes
• The system can determine the optimal number of
  CHs prior to election procedure based on
  parameters such as network topology and relative
  costs of computation vs. communication (Optimal
  number of CHs considered is 5 of the nodes)
• It has been observed that nodes die in a random
  fashion
• No communication exists between CHs
• Each node has same probability to become a CH

6
LEACH Heinzelman 2000, 2002

• Algorithm Details
• The operation of LEACH is achieved by rounds
• Each round begins with a set-up phase (clusters
  are selected) followed by steady-state phase
  (data transmission to BS occurs)
• Advertisement Phase
• Initially, each node need to decide to become a
  CH for the current round based on the suggested
  percentage of CHs for the network (set prior to
  this phase) and the number times the node has
  acted as a CH
• The node (n) decides by choosing a random number
  between 0 and 1
• If this random number is less than T(n), the
  nodes become a CH for this round
• The threshold is set as follows

P desired percentage of CHs r current
round G set of nodes that have not been
CHs in the last 1/P rounds
7
LEACH Heinzelman 2000, 2002

• Algorithm Details
• 1. Advertisement Phase
• Assumptions are (i) each node starts with the
  same amount of energy and (ii) each CHs consumes
  relatively same amount of energy for each node
• Each node elected as CH broadcasts an
  advertisement message to the rest
• During this clusterhead-advertisement phase,
  the non-clusterhead nodes hear the ads of all CHs
  and decide which CH to join
• A node joins to a CH in which it hears with its
  advertisement with the highest signal strength
• 2. Cluster Set-Up Phase
• Each node informs its clusterhead that it will be
  member of the cluster
• 3. Schedule Creation
• Upon receiving all the join messages from its
  members, CH creates a TDMA schedule about their
  allowed transmission time based on the total
  number of members in the cluster

8
LEACH Heinzelman 2000, 2002

• Algorithm Details
• 4. Data Transmission
• Each node starts data transmission to their CH
  based on their TDMA schedule
• The radio of each cluster member nodes can be
  turned OFF until their allocated transmission
  time comes minimizing the energy dissipation
• The CH nodes must keep its receiver ON to receive
  all the data
• Once all the data is received, the CH compresses
  the data to send it to BS
• Multiple Clusters
• In order to minimize the radio interference
  between nearby clusters, each CH chooses randomly
  from a list of spreading CDMA codes and it
  informs its cluster members to transmit using
  this code
• The neighboring CHs radio signals will be
  filtered out to avoid corruption in the
  transmission

9
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• Distributed, randomized clustering algorithm to
  organize the sensors in a wireless sensor network
  into clusters to minimize the energy used to
  communicate information from all nodes to the
  processing center
• Hierarchy of clusterheads leads to the energy
  savings
• In the clustered environment, the data gathered
  by the sensors is communicated to the data
  processing center through a hierarchy of
  clusterheads
• The processing center determines the final
  estimates of the parameters using information
  communicated by the clusterheads
• The processing center can be a specialized device
  or one of the sensors
• Sensor data communicated over smaller distances,
  the energy consumed in the network will be much
  lower than the energy consumption when every
  sensor communicates directly to the information
  processing center

10
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Single-Level Clustering
  Algorithm
• Each sensor becomes a clusterhead (CH) with
  probability p and advertises itself as a
  clusterhead to the sensors within its radio range
  these clusterheads are called volunteer
  clusterheads
• This advertisement is forwarded to all the
  sensors that are no more than k hops away from
  the clusterhead
• Any sensor node that is not clusterhead itself
  receiving such advertisement joins the cluster of
  the closest clusterhead
• Any sensor node that is neither a clusterhead nor
  has joined any cluster itself becomes a
  clusterhead called forced clusterheads
• Since the advertisement forwarding has been
  limited to k hops, if a sensor does not receive a
  CH advertisement within time duration t (where t
  is the time required for data to reach the CH
  from any sensor k hops away), it means that the
  sensor node is not within k hops of any volunteer
  CHs

11
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Single-Level Clustering
  Algorithm
• Therefore, the sensor node becomes a forced
  clusterhead
• The CH can transmit the aggregated information to
  the processing center after every t units of time
  since all the sensors within a cluster are at
  most k hops away from the CH
• The limit on the number of hops allows the CH to
  reschedule their transmissions
• This is a distributed algorithm and does not
  demand clock synchronization between the sensors
• The energy consumed for the information gathered
  by the sensors to reach the processing center
  will depend on the parameters p and k
• Since the objective of this work is to organize
  sensors in clusters to minimize the energy
  consumption, values of the parameters (p and k)
  must be found to ensure the goal

12
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Single-Level Clustering
  Algorithm
• Assumptions made for the optimal parameters are
  as follows
• The sensors are distributed as per a homogeneous
  spatial Poisson process of intensity ? in
  2-dimensional space
• All sensors transmit at the same power level
  have the same radio range r
• Data exchanged between two communicating sensors
  not within each others radio range is forwarded
  by other sensors
• A distance of d between any sensor and its CH is
  equivalent to hops
• Each sensor uses 1 unit of energy to transmit or
  receive 1 unit of data
• A routing infrastructure is in place when a
  sensor communicates data to another sensor, only
  the sensors on the routing path forward the data
• The communication environment is contention- and
  error-free sensors do not have to retransmit any
  data

13
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Hierarchical Clustering
  Algorithm
• This algorithm is extension of the previous one
  by allowing more than one level of clustering in
  place
• Assume that there are h levels in the clustering
  hierarchy with level 1 being the lowest level and
  level h being the highest
• The sensors communicate the gathered data to
  level-1 clusterheads (CHs)
• The level-1 CHs aggregate this data and
  communicate the aggregated data to level-2 CHs
  and so on
• Finally, level-h CHs communicate the aggregated
  data or estimates based on this aggregated data
  to the processing center

14
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Hierarchical Clustering
  Algorithm
• The cost of communicating the information from
  the sensors to the processing center is the
  energy consumed by the sensors to communicate the
  information to level-1 CHs, plus the energy
  consumed by the level-1 CHs to communicate the
  aggregated data to level-2 CHs, ., plus the
  energy consumed by the level-h CHs to communicate
  the aggregated data to the information processing
  center
• Algorithm Details
• The algorithm works in a bottom-up fashion
• First, it elects the level-1 clusterheads, then
  level-2 clusterheads, and so on

15
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Hierarchical Clustering
  Algorithm
• Algorithm Details
• Level-1 clusterheads are chosen as follows
• Each sensor decides to become a level-1 CH with
  certain probability p1 and advertises itself as a
  clusterhead to the sensors within its radio range
• This advertisement is forwarded to all the
  sensors within k1 hops of the advertising CH
• Each sensor receiving an advertisement joins the
  cluster of the closest level-1 CH the remaining
  sensors become forced level-1 CHs
• Level-1 CHs then elect themselves as level-2 CHs
  with a certain probability p2 and broadcast
  their decision of becoming a level-2 CH
• This decision is forwarded to all the sensors
  within k2 hops

16
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Hierarchical Clustering
  Algorithm
• Algorithm Details
• The level-1 CHs that receive the advertisement
  from level-2 CHs joins the cluster of the closest
  level-2 CH the remaining level-1 CHs become
  forced level-2 CHs
• Clusterheads at level 3, 4, 5,,h are chosen in
  similar fashion with probabilities p3, p4,
  p5,...,ph respectively to generate a hierarchy of
  CHs, in which any level-i CH is also CH of level
  (i-1), (i-2),,1.

17
Directed DiffusionIntanagonwiwat 2000

• Motivated by scaling, robustness and energy
  efficiency requirements
• Directed diffusion is data-centric in that all
  communication is for named data
• Data generated by sensor nodes is named using
  attribute-value pairs
• All nodes in the network are application-aware
• A node requests data by sending interests for
  named data
• A sensing task is disseminated via sequence of
  local interactions throughout the sensor network
  as an interest for named data
• Nodes diffusing the interest sets up their own
  caches and gradients within the network to which
  channel the delivery of data
• During the data transmission, reinforcement and
  negative reinforcement are used to converge to
  efficient distribution
• Intermediate nodes fuse interests, aggregate,
  correlate or cache data

18
Directed DiffusionIntanagonwiwat 2000

• Assumes that sensor networks are task-specific
  the task types are known at the time the sensor
  network is deployed
• An essential feature of directed diffusion is
  that interest, data propagation and data
  aggregation are determined by local interactions
• Focused on design of dissemination protocols for
  tasks and events
• Naming
• Task descriptions are named (specifies an
  interest for data matching the list of
  attribute-value pairs) and also called as
  interest
• Example task Every I ms, for the next T
  seconds, send me a location of any four-legged
  animal in subregion R of the sensor field.
• task four-legged animal // detect animal
  location
• interval 20 ms // send back events every 20 ms
• duration 10 seconds // for the next 10
  seconds
• rect -100, 100, 200, 400 // from sensors
  within rectangle

19
Directed DiffusionIntanagonwiwat 2000

• Naming
• A sensor detecting an animal may generate the
  following data
• task four-legged animal // type of animal seen
• instance horse // instance of this type
• location 150, 200 // node location
• intensity 0.5 // signal amplitude measure
• confidence 0.85 // confidence in the match
• timestamp 013045 // event generation time
• Interests and Gradients
• Interest is generally given by the sink node
• For each active task, sink periodically
  broadcasts an interest message to each of its
  neighbors (including rect and duration
  attributes)
• Sink periodically refreshes each interest by
  re-sending the same interest with monotonically
  increasing timestamp attribute for reliability
  purposes

20
Directed DiffusionIntanagonwiwat 2000

• Interests and Gradients
• Every node maintains an interest cache where each
  item in the cache corresponds to a distinct
  interest (different type, interval attributes
  with disjoint rect attributes)
• Interest entries in the cache do not contain
  information about the sink
• In some cases, definition of distinct interests
  allows interest aggregation
• The interest entry contains several gradient
  fields, up to one per neighbor
• When a node receives an interest, it determines
  if the interest exists in the cache
• If no matching exist, the node creates an
  interest entry
• This entry has single gradient towards the
  neighbor from which the interest was received
  with specified data rate
• Individual neighbors can be distinguished by
  locally unique identifiers
• If the interest entry exists, but no gradient for
  the sender of interest
• Node adds a gradient with the specified value
• Updates the entrys timestamp and duration fields

21
Directed DiffusionIntanagonwiwat 2000

• Interests and Gradients
• If there exists both entry and a gradient,
• The node updates the entrys timestamp and
  duration fields
• When a gradient expires, it is removed from its
  interest entry
• When all gradients for an interest entry have
  expired, the interest entry is removed from the
  cache
• After receiving an interest, a node may re-send
  the interest to subset of its neighbors
• To the neighbors, it may seem that interest
  originated from the sending node even though it
  may have been generated a distant sink. This
  represents a local interaction
• This way, interest diffuse throughout the network
  and not each interest have been sent to all the
  neighbors if a node sent matching interest
  recently
• Gradient specifies data rate (value) and a
  direction in directed diffusion, whereas the
  values can be used to probabilistically forward
  data in different paths in other sensor networks

22
Directed DiffusionIntanagonwiwat 2000

• Data propagation
• Data message is unicast individually to the
  relevant neighbors
• A node receiving a data message from its
  neighbors checks to see if matching interest
  entry in its cache exists according the matching
  rules described
• If no match exist, the data message is dropped
• If match exists, the node checks its data cache
  associated with the matching interest entry
• If a received data message has a matching data
  cache entry, the data message is dropped
• Otherwise, the received message is added to the
  data cache and the data message is re-sent to the
  neighbors
• Data cache keeps track of the recently seen data
  items, preventing loops
• By checking the data cache, a node can determine
  the data rate of the received events

23
Directed DiffusionIntanagonwiwat 2000

• Reinforcement
• After the sink starts receiving low data rate
  events, it reinforces one neighbor in order to
  draw down higher quality (higher data rate)
  events
• This is achieved by data driven local rules
• To enforce a neighbor, the sink may re-send the
  original interest with higher data rate
• When the data rate is higher than before, the
  node node must also reinforce at least one
  neighbor
• Reinforcement can be carried out from neighbors
  to other neighbors in a particular path (i.e.,
  when a path delivers an event faster than others,
  sink attempts to use this path to draw down high
  quality data)
• In summary, reinforce one path, or part of it,
  based on observed losses, delay variances, and so
  on
• Negative reinforce certain paths because resource
  levels are low

24
Directed DiffusionIntanagonwiwat 2000
Figure adapted from Intanagonwiwat 2000
25
Stealth Routing Turgut 2009

• Intruder Tracking Sensor Network
• Sensor networks used to detect and track
  intruders in a geographic region
• Interest area
• Observations are disseminated to the sink by hop
  by hop transmission
• Performance metric tracking error
• Difference between the model maintained by the
  sink and the real location of intruders
• See demo here

26
Stealth Routing Turgut 2009

• The problem of stealth
• The intruders belong to malicious and resourceful
  adversary
• If the adversary knows the locations of the
  nodes it can avoid sensors, find and exploit
  blind spots, introduce fake observations, capture
  or compromise nodes
• A node is
• Stealthy if the adversary doesnt know its
  existence
• Disclosed if the adversary can accurately locate
  the node
• Have various levels of stealth between these two
  extremes
• How can a node loose stealth
• Accidentally
• Through wireless transmission in the presence of
  the intruder
• Routing / dissemination protocols did not
  previously consider the issue of stealth
• The issue of stealth has been at best marginally
  addressed in the field of sensor networks.
• Our objective
• Develop a dissemination algorithm which optimizes
  stealth without sacrificing the other performance
  parameters

27
Stealth Routing Turgut 2009

• Quantifying stealth
• Stealth level s(t) as probability at time t that
  the node is not disclosed to the opponent
• Non disclosed node s(t) 1
• Disclosed node s(t) 0
• Stealth level decreases in response to
  transmission events.
• Probability of disclosure at transmission

28
Stealth Routing Turgut 2009

• Try and Bounce (TAB)
• A dissemination algorithm designed from the
  ground up to take into consideration stealth.
• It is based on the creation and forwarding of
  reports about intruder location
• All the transmissions are semantically meaningful
  to every node (there is no blind transmission)
• The local nodes maintain a local model of their
  environments
• Updated through a series of inferences
• We will now investigate three aspect of TAB
• A. The maintenance of the local model
• B. The forwarding path
• C. The decision to make a transmission

29
Stealth Routing Turgut 2009

• A. Maintenance of the local model (1)
• TAB agent maintains a local model of environment
  represented by the triplet ltN, I, Rgt
• N set of node models lists the series of sensor
  nodes known to agent, whether within tx_range,
  active/inactive, or under threat
• I set of intruder models contains the list of
  the intruder nodes believed to be in the area of
  sensor network, their last known position and
  potentially other observed properties
• R set of report models contains the list of
  reports about intruder nodes to the sink
• For each report, model maintains the intruder
  node, its location, time when the observation was
  made, and path record of the report
• The model also keeps track of whether the node is
  responsible for forwarding of the report or if it
  is responsible in checking its forwarding

30
Stealth Routing Turgut 2009

• A. Maintenance of the local model (2)
• Series of inferences triggered every time an
  agent makes an observation, receives, transmits
  or overhears a message
• Inferences are also triggered by passage of time
• Inferences has the complexity of O(1)
• TAB agent never maintains any historical
  information
• The number of reports do not exceed those of the
  active intruders
• Bookkeeping inferences
• Occlusion
• Obsoleting
• Inferences concerning intruder nodes
• Sighting
• Report received
• Inference from silence

31
Stealth Routing Turgut 2009

• A. Maintenance of the local model (3)
• Inferences concerning nodes
• Heartbeat
• Lack of retransmission
• No heartbeat
• Inference from path records
• Inferences concerning reports
• Report from sighting
• Report from received message
• Report transmitted
• Report progress overheard
• Report progress timeout

32
Stealth Routing Turgut 2009

• A. Maintenance of the local model (4)
• Example inference occlusion
• A newer report concerning the same intruder has
  been either
• Observed
• Received from another node
• Overheard in the transmission between other two
  nodes
• The node discards the old report (the new report
  occluded it)
• Observations
• Occlusion works because the sink is not
  interested in history, only on the most recent
  position of the intruder
• The node prefers to overhear a new report
  because the overhearing does not create a
  responsibility, while a received message does

33
Stealth Routing Turgut 2009

• B. Forwarding in TAB
• The unit of forwarding is a report
• At any step during forwarding, the report has
  someone responsible for it
• When forwarding to the next hop, the node passes
  the responsibility for the report
• But it needs to check whether the next hop
  forwards or not
• If the next hop does not forward, responsibility
  bounces back
• The node needs to try another path to send
  messages to the sink
• The path record in the message assures that the
  report does not retry failed paths
• Choosing the next hop
• Preference-ordered list of next hops to sink
• The first choice is identical to what you would
  have in DD or most other protocols

34
C. The decision to transmit a report
Stealth Routing Turgut 2009

• C. The decision to transmit a report
• Start by evaluating the stealth loss if it
  transmits
• How to make the decision to transmit?
• We want to minimize stealth loss, but we also
  want good accuracy
• Idea cap the average stealth loss / intruder /
  node
• Calculate the running average of the stealth loss
• Transmit is the running average is below a
  threshold

35
Stealth Routing Turgut 2009

• Simulation Study
• Intruder tracking sensor network
• Interest area 400x400 meters
• 64 nodes
• Sensing range 50m
• Transmission range 50m
• Experiment time 2hr (7200 seconds)
• 10..80 intruders cross the area during the
  experiment
• Experiment repeated for 4 different protocol /
  parameterization
• DD-10 directed diffusion, interval 10 secs
• DD-25 directed diffusion, interval 25 secs
• TAB-0.001 try and bounce, stealth loss cap
  0.001 stealth units per intruder per unit of time
• TAB-0.003 try and bounce, stealth loss cap
  0.003
• Implemented all the protocols in the YAES
  simulator

36
Stealth Routing Turgut 2009
37
Stealth Routing Turgut 2009
38
Stealth Routing Turgut 2009
39
Stealth Routing Turgut 2009
40
Stealth Routing Turgut 2009

• Conclusions
• Stealth is an important aspect of the operation
  of many sensor networks
• The challenge is to balance between multiple
  performance criteria
• Try and bounce (TAB) an early algorithm towards
  this direction
• We found that it outperforms DD on the stealth
  tracking accuracy combination
• Future work
• Other performance metrics (e.g. power
  conservation)
• Improve the models and inferences
• Extend the range of practical applicability

41
References

• Bandyopadhyay 2003 S. Bandyopadhyay and E.J.
  Coyle, An Energy Efficient Hierarchical
  Clustering Algorithm for Wireless Sensor
  Networks, IEEE INFOCOM 2003, San Francisco, CA,
  March 30 April 3, 2003.
• Heinzelman 2002 W. Heinzelman, A.P.
  Chandrakasan and H. Balakrishnan, An
  Application-Specific Protocol Architecture for
  Wireless Microsensor Networks, IEEE Transactions
  on Wireless Communications, Vol. 1, No. 4,
  October 2002, pp. 660-670.
• Heinzelman 2000 W. Heinzelman, A.P.
  Chandrakasan and H. Balakrishnan,
  Energy-Efficient Communication Protocol for
  Wireless Microsensor Networks, IEEE Proceedings
  of the Hawaii International Conference on System
  Sciences, January 4-7, 2000, Maui, Hawaii.
• Intanagonwiwat 2000 C. Intanagonwiwat, R.
  Govindan and D. Estrin, Directed Diffusion A
  Scalable and Robust Communication Paradigm for
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  Annual International Conference on Mobile
  Computing and Networks (MobiCOM 2000), August
  2000, Boston, Massachusetts.
• Turgut 2009 D. Turgut, B. Turgut, and L.
  Bölöni, Stealthy dissemination in intruder
  tracking sensor networks, Proceedings of IEEE
  LCN, October 2009, pp. 22-29.