Introduction to Wireless Sensor Networks - PowerPoint PPT Presentation

About This Presentation
Title:

Introduction to Wireless Sensor Networks

Description:

Introduction to Wireless Sensor Networks-Research Problems (Clustering, Routing, etc) * C. The decision to transmit a report C. The decision to transmit a report ... – PowerPoint PPT presentation

Number of Views:80
Avg rating:3.0/5.0
Slides: 42
Provided by: DamlaT2
Learn more at: https://www.eecs.ucf.edu
Category:

less

Transcript and Presenter's Notes

Title: 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
    Sensor Networks, In Proceedings of the Sixth
    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.
Write a Comment
User Comments (0)
About PowerShow.com