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Data Dissemination and Fusion

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[Heinzelman+ 2000] W. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, Energy-Efficient Communication ... SPIN-PP with low-energy ... wireless network models ... – PowerPoint PPT presentation

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Title: Data Dissemination and Fusion


1
Data Dissemination and Fusion in Sensor Networks
2
The Need for Data Dissemination and Fusion
  • Energy efficiency is an essential factor
    therefore, short-range hop-by-hop communication
    is preferred over direct long-range communication
    to the destination
  • Since sensor network contains large amount of
    data for the end user, methods of combining or
    aggregating data into small set of information is
    necessary and contributes to energy savings
  • Data aggregation (aka data fusion) can combine
    unreliable data readings to produce accurate
    signal by improving the common signal and
    reducing the noise

3
Taxonomy of Data Delivery Models in Wireless
Sensor Networks
  • Wireless sensor networks are classified according
    to their data delivery model into the following
    categories Kulik 2002
  • Continuous
  • LEACH Heinzelman 2000, 2002 is designed for
    routing data to base stations in static wireless
    sensor networks
  • TEEN (Threshold sensitive Energy Efficient sensor
    Network Protocol) Agrawal 2001 and PEGASIS
    (Power Efficient GAthering in Sensor Information
    Systems) Lindsey 2001 are both proposed as
    improvements to LEACH
  • Observer-initiated
  • In Directed Diffusion Intanagonwiwat 2000,
    data are named using attribute-value pairs and
    sensed information in the network can be
    associated with such a pair. The sensor nodes
    send queries expressing their interest for sensed
    information satisfying a specific criteria

4
Taxonomy of Data Delivery Models in Wireless
Sensor Networks
  • Event-driven
  • SPIN (Sensor network Protocols via Information
    Negotiation) Kulik 2002 are set of protocols
    designed to disseminate data to all nodes in the
    network
  • Hybrid
  • The above three approaches can coexist in the
    same network

5
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

6
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

7
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

8
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

9
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

10
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

11
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

12
Directed DiffusionIntanagonwiwat 2000
Figure adapted from Intanagonwiwat 2000
13
Directed DiffusionIntanagonwiwat 2000
  • Advantages
  • Data-centric dissemination
  • Robust multi-path delivery
  • Reinforcement-based adaptation to the empirically
    best network path
  • Energy savings with in-network data aggregation
    and caching
  • Gives designers the freedom to attach different
    semantics to gradient values
  • Reinforcement can be triggered not only by
    sources but also by intermediate nodes

14
Directed DiffusionIntanagonwiwat 2000
  • Disadvantages
  • It may consume memory since all the attribute
    list is being sent
  • Suggestions/Improvements/Future Work
  • Exploration of possible naming schemes

15
Negotiation-Based Protocols for Disseminating
Information in Wireless Sensor Networks (SPIN
Protocols) Kulik 2002
  • SPIN (Sensor Protocols for Information via
    Negotiation) is a family of negotiation-based
    information dissemination protocols which is
    designed to address the deficiencies of classic
    flooding by negotiation and resource-adaptation
  • SPIN disseminates each sensor readings to all
    sensors in the network, treating all sensors as
    potential sink nodes
  • Nodes using SPIN protocols names their data using
    high-level data descriptors, called meta-data and
    usage of meta-data negotiations eliminate
    transmission of redundant data in the network
  • Communication decisions can be based upon both
    application-specific knowledge of the data and
    knowledge of the resources available to nodes

16
SPIN Kulik 2002
  • SPIN has two basic ideas
  • Operate efficiently and conserve energy
    communicate with each other about the sensor data
    received already and the data needed still
  • Monitor and adapt changes in their own energy
    resources extend the lifetime of the system
  • Four different SPIN protocols
  • SPIN-PP
  • SPIN-EC
  • SPIN-BC
  • SPIN-RL
  • Meta Data
  • Used to uniquely and completely describe the data
    being collected by sensors
  • If two pieces of actual data are distinguishable,
    then their meta-data should also be
    distinguishable
  • Since the format of meta-data is
    application-specific, each application needs to
    interpret and synthesize its own meta-data

17
SPIN Kulik 2002
  • Meta Data
  • SPIN applications must define a meta-data format
    for representing data that concerns with the
    costs of storing, retrieving and managing the
    meta-data
  • SPIN nodes uses three types of communication
    messages
  • ADV (new data advertisement)
  • REQ (request for data)
  • DATA (data message)
  • ADV and REQ messages contain only meta-data that
    is smaller than the DATA message
  • SPIN Resource Management
  • SPIN applications are resource-aware and
    resource-adaptive
  • By knowing the resources at hand, the nodes makes
    informed decisions about using their resources
    effectively
  • SPIN specifies an interface that applications can
    use to find out their available resources rather
    than specifying a specific energy management
    protocols

18
SPIN Kulik 2002
  • The Problem
  • In conventional classic flooding, the source
    nodes sends data to all its neighbors and the
    neighbors check their record of already sent data
    to see if they have forwarded the data to their
    neighbors. If not, they forward the data and
    update the record
  • This requires small amount of protocol state at
    any node, disseminates data quickly in the
    network where neither the bandwidth is scarce and
    the links are error prone
  • The problems include implosion, overlap and
    resource blindness
  • Implosion A node always sends data to its
    neighbors without being concerned about
  • if the same data has been received by the
    neighbors from other nodes
  • Overlap The nodes waste energy and bandwidth by
    sending the overlapping data
  • Resource Blindness Nodes do not make decisions
    based on the energy available

19
SPIN Kulik 2002
  • The Solution
  • SPIN provides solution to the problems of
    implosion and overlap by negotiating with each
    other before transmitting data eliminates the
    transmission of redundant data
  • Nodes poll their resources before transmitting or
    processing data by probing the resource manager
    which keeps track of the resource consumption
  • Nodes can make efficient decisions based on the
    available energy level
  • The use of meta-data descriptors eliminates the
    possibility of overlap since the nodes can name
    the part of the data the nodes are interested in
    receiving
  • Resource-awareness of local resources allow
    sensors to make meaningful decisions to extend
    longevity

20
SPIN Kulik 2002
  • SPIN Protocols
  • 1. SPIN-PP A Threestage handshake protocol for
    point-to-point media
  • This protocol works in three stages
    (ADV-REQ-DATA) with each stage corresponding to
    one of the messages
  • The node sends ADV message to its neighbors
  • Neighbors check to see if they already have
    received or requested this data
  • If not, the neighbors respond by sending REQ
    message to the sender
  • The sender responds to the REQ message sent by
    sending the actual DATA to the neighbors
    requesting the data
  • If the neighbor already has the advertised data,
    it does not send any message
  • Simplicity is the main strength, meaning that
    nodes make simple decisions, resulting in usage
    of small energy in computation
  • Each node only needs to know about its one hop
    neighbors

21
SPIN Kulik 2002
  • SPIN Protocols
  • 2. SPIN-EC SPIN-PP with low-energy threshold
  • Adds simple energy-conservation heuristic to the
    SPIN-PP protocol
  • When energy is abundant, SPIN-EC acts as SPIN-PP
    protocol
  • Whenever energy comes close to low-energy
    threshold, it adapts by reducing its
    participation
  • The node will only participate in the full
    protocol if it believes that it has enough energy
    to complete the protocol without reaching below
    the threshold value
  • It does not prevent nodes from receiving messages
    such as ADV or REQ below its low-energy
    threshold, but prevents the nodes to handle a
    DATA message below the threshold

22
SPIN Kulik 2002
  • SPIN Protocols
  • 3. SPIN-BC A Threestage handshake protocol for
    broadcast media
  • Improves upon SPIN-PP for broadcast networks by
    using cheap, one-to-many communications, meaning
    that all messages are sent to broadcast address
    and processed by all the nodes that are within
    transmission range of the sender
  • This approach is often called broadcast-message-su
    ppression
  • SPIN-BC has three main differences from SPIN-PP
    are
  • All SPIN-BC nodes send their messages to the
    broadcast address such that all nodes within the
    transmission range of sender will receive message
  • Upon receiving ADV message, each node checks to
    see if they already have the data. If not, node
    sets a random timer to expire, uniformly chosen
    from a predetermined interval. After timer
    expires, the node sends an REQ message to the
    broadcast address, including the original
    advertiser in the header of message. When the
    nodes who are not original advertiser receive the
    REQ, they cancel their own request timers,
    preventing from sending out redundant copies of
    the same REQ
  • The nodes will send out the requested data to the
    broadcast address only once to get the data all
    its neighbors. It will not respond to multiple
    requests of the same data

23
SPIN Kulik 2002
  • SPIN Protocols
  • 4. SPIN-RL SPIN-BC for lossy networks
  • Reliable version of SPIN-BC which disseminates
    data through a broadcast network even in the
    cases of network loses packets or communication
    is asymmetric
  • Adds two adjustments to SPIN-BC to achieve
    reliability
  • Each node maintains a record of which
    advertisements it hears from which nodes, and if
    does not receive the data within a set time after
    request, node rerequests the data
  • Nodes limit the frequency with which they will
    resend the data, meaning that it will wait for a
    set time before responding to any additional
    requests for the same data

24
SPIN Kulik 2002
  • Advantages
  • Meta-data negotiation and resource adaptation
  • Maintains only local information about the
    nearest neighbors
  • Suitable for mobile sensors since the nodes base
    their forwarding decisions on local neighborhood
    information
  • Disadvantages
  • It cannot isolate the nodes that do not want to
    receive information unnecessary power may be
    consumed

25
SPIN Kulik 2002
  • Suggestions/Improvements/Future Work
  • Study SPIN protocols in mobile wireless network
    models
  • Develop more sophisticated resource-adaptation
    protocols to use available energy well
  • Design protocols that make adaptive decisions
    based not only on the cost of communicating data,
    but also the cost of synthesizing it

26
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • This work considers searches over semantically
    rich high-level events, and presents the design,
    analysis, and numerical simulations of a
    spatially distributed index that provides for
    efficient index construction and range searches
  • The conventional approach to storing time series
    data is to have all sensing node sending their
    data to a central repository external to the
    environment
  • While obtaining the flexibility of processing the
    data, sending every sensor reading to external
    site incurs high energy consumption
  • In addition, the links near a gateway or an
    external storage repository can become
    communication bottlenecks as the network size and
    the sensed data increase
  • As a result, it may be advisable to store data
    locally at or near the location of the generation
    of the sensed data

27
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • One approach to retrieve this stored data is to
    flood a query to all nodes that may have suitable
    data and have those nodes send their response to
    the querying node
  • In this approach, data is sent when and where it
    is required
  • If some queries are originated within the sensor
    network, it is not advisable to send the data to
    an external site instead of sending it to the
    internal querying data
  • If more data is collected than required, this
    local storage approach increase energy savings
  • There are two extensions to this approach for
    further energy savings
  • Data can be processed, aggregated, and/or pruned
    while propagating towards the query sink

28
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • There are two extensions to this approach for
    further energy savings
  • The developers of Directed Diffusion
    Intanagonwiwat 2000,TAG Madden 2002, and
    others describe specific forms of in-network
    aggregation and pruning of data that can select
    relevant data and produce statistics. This
    approach uses data-centric routing that queries
    are not directed towards individual nodes, but
    they are stated only in terms of desired data
  • The data can be processed locally to identify
    high-level events that of interest. These
    events can refer directly to sensor readings. The
    queries are directly for such events, and the
    responses comprised of summarized data about
    those events. Here, the routing is also
    data-centric, but queries and responses interact
    with higher-level abstractions

29
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • These energy savings approaches reduce the energy
    required to respond to queries, but do not deal
    with the cost of basic flood-then-respond
    approach in that cost of flooding each query to
    all possible nodes
  • Data-centric storage (DCS) approach Shenker
    2002 avoids the flooding of queries -- all
    events are named and stored at a network location
    based on the name and queries for an event are
    routed to appropriate network node where the
    relevant data can be accessed
  • Storing data by name allows creation of a
    mechanism between data and queries such that
    queries need not be flooded
  • GHT Ratnasamy 2002 proposes a specific
    solution to achieve DCS in which event names are
    hashed to geographic locations and stored at the
    node closest to the hashed location

30
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • DIFS extend the the data-centric storage
    architecture to support range queries where only
    events with attributes in a certain range are
    desired. It provides for low average search and
    storage communication requirements and tries to
    balance these requirements over participating
    nodes
  • DIMENSIONS Ganesan 2002 also relies on the
    placement of data within the sensornet and use of
    data-centric rendezvous points with lower level
    sensor readings and produces a multiresolution
    index (or view) of data
  • High-Level Events
  • High-level events, such as a hot region or a
    target detection, a map, or a histogram can be
    described in many ways
  • The paper propose adding new data structures to
    store high-level data abstractions to the simple
    attribute types introduced by Diffusion
  • Such abstractions would be defined system-wide at
    deployment time

31
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Classification of Event Properties and
    Relationships
  • Classification proposed has been designed with
    the consideration of attribute range and
    distribution queries
  • The goals of a system directed at binary events
    such as zebra sightings are different from the
    goals of providing range searches over events
    that are each comprised of attributes with values
  • The goal of a search over binary events is to
    determine the locations of those events and when
    such events are rare, it is much more
    energy-efficient to construct a rendezvous point
    where events could register and queries could
    search than to flood a search
  • Events defined by attributes with values that
    fall within a specified range are less common,
    i.e., there may be many hot regions in a network,
    but few with a heat gradient with a slope greater
    than s

32
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Classification of Event Properties and
    Relationships
  • For this reason, this paper develops a new method
    to support range queries more efficiently and
    proposes mechanisms to run on top of GHT to
    address range queries
  • The high-level events are classified as follows
  • Sensor value(s)
  • Includes raw sensor values that comprise
    high-level events, composite measurements and
    summary statistics such as average, median, etc
  • Examples include the peak temperature of a hot
    region, the speed that an animal target is moving
  • Sensor values can be search over a designed area
    and they are represented as integers or floating
    point numbers

33
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Classification of Event Properties and
    Relationships
  • Timing parameters
  • Essential to know not only a specific value for a
    region, but also how this value varies over time,
    I.e., a hot region that has been hot for some
    period of time
  • Spatial dimensions
  • Refers to physical shape and location of an
    event, i.e., hot regions larger than a given area
  • Regions can described as enclosing circles,
    ellipses, or polygons and their points of
    interest can be represented as integer or
    floating point coordinates

34
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Classification of Event Properties and
    Relationships
  • Event Interrelationships
  • In the spatial domain, relationships between
    events translates to proximity or intersection,
    i.e., is an area of high CO2 concentration also
    an area of bright sunlight?
  • In the temporal domain, event interrelationships
    translate to succession and temporal separation,
    i.e., did an area of high CO2 concentration
    happens immediately after bright sunlight?

Table 1 Event Property and Relationship
Classification
35
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Storage and Search Architecture
  • Time series data generated by sensor nodes is
    locally processed by statistical and pattern
    recognition engines to generate high-level events
    that these events are stored locally where they
    are created, and information about their various
    attributes is inserted into indices
  • An interested user or an automaton poses queries
    to these indices
  • The query results are found in the indices
    themselves, at the storage nodes, and even at the
    nodes that generate time series data
  • In terms of event generation and search, nodes
    serve two functions
  • all nodes may be used to store raw time series
    data and events
  • a subset of nodes serve as index nodes to
    facilitate search

36
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
Storage and Search Architecture
Figure 2 A storage and search architecture
37
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Advantages
  • DIFS efficiently supports range queries and
    queries related to distribution of values in
    space by using histograms, that direct queries to
    the relevant nodes
  • The paper builds on an already proven technique
    and simulation results show that DIFS
    outperforming GHT in query and communication
    costs
  • DIFS was designed to incorporate balancing of
    communication load over the network by having
    more than one query entry point and provision to
    originate search at any node in the tree
  • DIFS is scalable to large number of searches or
    stores as it eliminates the restriction of
    propagating every data information to the root
    and originating every query at the root

38
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Disadvantages
  • No mentioning about the failure of sensor nodes
    at level of the hierarchy in the quad tree
    structure of DIFS
  • In case of dense deployment, a uniform
    distribution of data values causes the DIFS
    algorithm exploring all the leaves hence not a
    very good option as far as energy consumption is
    considered
  • No mentioning about making the querying and event
    insertion resilient to packet loss
  • Overhead incurred while maintaining extra parent
    information

39
DIFS A Distributed Index for Features in Sensor
Networks Greenstein 2003
  • Suggestions/Improvements/Future Work
  • Introduce dynamic repartitioning when the
    distribution changes over a time period
  • To handle large queries, may be they can be split
    into smaller sub-queries, encoding them to be
    identified later and process them separately,
    either locally or forwarding to other nodes that
    have lesser traffic this will avoid energy
    depletion of the really busy query access nodes
  • Handle data corruption at index nodes
  • Improve DIFS search cost
  • route the query using hierarchical dissemination,
    as in structured replication, rather than sending
    unicast messages to each of the covering nodes
  • route to nodes in the highest tree level that
    will cover the entire query range, rather than
    decomposing the query range into minimal covering
    set

40
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • Introduction
  • This work presents a new event-based
    communication model
  • The proposed protocol called Topology-Divided
    Dynamic Event Scheduling (TD-DES), organizes the
    wireless network into a multi-hop network tree
  • The root of the tree creates a data dissemination
    schedule and propagates this schedule throughout
    the tree
  • The schedule is divided into fixed-size time
    slots, each indicating the type of data that are
    sent (or received), and whether it is for
    downstream (i.e., away from the root) or upstream
    (i.e., toward the root) communication
  • The schedule can be periodic or refreshed in
    arbitrary intervals, depending on the data
    traffic and applications -- the idea is that
    nodes can save energy by powering down their
    radios to standby mode when they have no data to
    send, and when they (and their descendants) do
    not wish to receive the data being transmitted

41
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • Introduction
  • The system uses the publish/subscribe model each
    node has a specific subscription profile that
    indicates which data types the node is interested
    in receiving
  • TD-DES allows each node to selectively listen for
    interested data based on the its position in the
    network topology
  • Since data must be scheduled before it is sent,
    the main tradeoff investigated is increased power
    efficiency in exchange for sub-optimal message
    dissemination latency
  • This work addresses application-specific
    scheduling and data dissemination issues, which
    was not taken into consideration by the previous
    in this area

42
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II. System Model
  • TD-DES is intended as application overlay to a
    CSMA/CA wireless MAC layer rather than a
    MAC/networking layer in itself
  • II.A Scheduling Model
  • TD-DES monitors when each node of a network (1)
    receives data, (2) transmits data, and (3) powers
    its radio down to a low-power standby mode
  • These radio modes Tx, Rx, and standby are
    cycled among as functions of time determined by
    the networks dissemination schedule, generated
    by the root node and propagated down the tree as
    part of a control event
  • The base station is considered to be the root
    node with higher computational, storage, and
    transmission capabilities than the rest of the
    nodes and it can serve as an entry point to the
    sensor network, integrating the sensor network
    with the external wired network where the
    monitoring task GUI resides

43
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.A Scheduling Model
  • The scheduler depends on topology information,
    event profiles, traffic statistics, and QoS
    requirements when generating dissemination
    schedules
  • The goal of the scheduler is to minimize
    network-wide power consumption (by minimizing the
    amount of time spent in the Rx and Tx modes)
    without sacrificing timely dissemination of data
  • II.B Network Model
  • TD-DES has an integrated network construction
    layer that organizes a wireless network into a
    tree topology
  • The topology is constructed by broadcasting
    advertisements from all nodes
  • First, the root node broadcasts a parent
    advertisement
  • Each node hearing this advertisement replies with
    a child message that indicates that the node will
    become a child of the root

44
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.B Network Model
  • Whenever a node becomes a child, it broadcasts
    its own parent advertisement
  • The process continues until all the nodes get
    attached to the tree
  • A node that hears multiple parent advertisements
    chooses its parent node with the lowest hop count
    to the root
  • The tree construction layer is adaptive to
    topology changes due to node failures, additions,
    and mobility
  • The data events are disseminated throughout the
    network based on per-node event description
    rather than point-to-point messaging
  • This publish/subscribe type of event-based
    communication is the data dissemination model of
    choice since it decouples the producers and
    consumers of information

45
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.C Data/Event Model
  • Overlaying applications define predefined event
    types and these event types are maintained in a
    global event schema
  • For instance, a network with n different event
    types may publish event types e1, e2, e3,, en
  • Each node maintains its own event subscription
    which is the set of event types that a node is
    interested in as well as its own effective
    subscription which is the union of its own
    subscription and the subscriptions of all its
    descendants
  • Each node subscribes to any event type of its own
    interest as well as any event type of a
    descendent node is interested in since each node
    is responsible for forwarding all relevant events
    to its descendants in the tree topology

46
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
II.C Data/Event Model
Figure 3 An example dissemination
tree Subscriptions are given at the upper left
corner of each node, effective subscriptions at
the upper right. Arrows indicate the links over
which the event is broadcast
47
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.C Data/Event Model
  • Figure 3 presents a dissemination tree of eight
    nodes and three event types e1, e2, and e3 with
    N1 being the root node of the tree
  • The subscription of each node is given in
    parentheses at the upper left of the node and the
    effective subscription is given at the upper
    right of each node in square brackets
  • Note that an event of type e2 generated at node
    N5
  • The arrows indicate the links across which the
    event is broadcast to disseminate the event to
    all subscribing nodes
  • Note that the event is propagated both upstream
    (to the root and then downstream to the
    interested parties in the other sub-tree) and
    downstream therefore, events do not always go
    through the root node

48
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.C Data/Event Model
  • An event is a message type with its own unique
    application-specific semantics
  • Consider a scenario where a sensor network whose
    purpose is to detect fires is deployed over a
    forested region
  • A sensor node may issue a fire_detected event to
    the network if its temperature reading is very
    high
  • This event would be disseminated through the
    network to all those nodes, (such as forest
    ranger stations, a centralized forest fire
    monitoring station, or a sink node which could
    notify the police, local fire-fighting units, and
    public news services) subscribing to
    fire_detected events
  • These nodes can also include any intermediate
    nodes which had to forward such events to
    interested nodes, even if themselves may not be
    interested

49
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • II.D Application-defined QoS
  • Besides carrying unique type semantics, event
    types may be associated with network-specific
    physical characteristics, such as minimum and
    maximum event payload sizes, latency constraints,
    and relative event priorities
  • The overlaying applications specify such event
    latency and priority values
  • III. Protocols
  • TD-DES event schedule determines the temporal
    partitioning of the RF medium for all of the
    event types by allocating time slots (or slots)
    for each event type
  • Each time slot is assumed to be wide enough for a
    single event to be propagated one hop in other
    words, each slot should provide sufficient time
    to the underlying MAC layer to perform collision
    detection and retransmissions under contention

50
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III. Protocols
  • Time slots are allocated for each event based on
    the determined or expected bandwidth requirements
    needed to propagate all generated events reliably
    throughout the network
  • Once the numbers of upstream and downstream time
    slots for each event type are determined, the
    ordering of the time slots must then be
    determined
  • Iterations are intervals of schedule that starts
    with a control event slot and it is also possible
    to interleave downstream and upstream slots
    together to fit into a single iteration

51
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.A Schedule Propagation
  • The root node creates a schedule of time slots
    where each time slot is designated as a send or
    receive slot, whether it is for upstream or
    downstream communication, and by the event type
    which it should be used to propagate
  • The schedule is created one iteration at a time
    and passes it down through the dissemination tree
    inside a control event
  • The schedule of slots between two consecutive
    downstream control events is called a single
    iteration of the schedule
  • Figure 4 presents the basic idea of creating a
    schedule and passing it down the network tree
    using a scenario with downstream propagation of
    control and data events
  • The control event is created by TD-DES and
    contains scheduling information

52
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.A Schedule Propagation
  • The control event is received by the node at
    level X in the first time slot
  • In the next time slot, this node transmits the
    control event down to the next level, X1.
  • In the following time slot, the node at level X1
    passes the control event down to level X2, and
    so on
  • Basically, iterations are delimited by control
    events and can consist of a different number of
    data events
  • The control event initiating an iteration
    specifies the schedule of events within that
    iteration
  • Note that the schedule is shifted one slot at
    each level

53
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.A Schedule Propagation
  • The schedule has a sequence of atomic send and
    receive time slots, each one for a specified
    event type
  • Generally, at a given node, for a particular
    event in a schedule, time slots are allocated as
    a receive slot followed by an immediate send slot

Figure 4 An example of schedule propagation
54
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.A Schedule Propagation
  • The node, if both the schedule specifies a
    receive time slot for event e1 and if the node
    subscribes to e1, will listen to the RF medium in
    Rx mode during this time slot to receive such an
    event
  • If the schedule specifies a send time slot for
    e1, the node can transmit an event of this type
  • Each slot is either a downstream slot (for
    parent-to-child communication away from the root)
    or as an upstream slot (for child-to-parent
    communication toward the root)
  • For downstream communication, send and receive
    slots are used whereas upstream slots are not
    designated for event types, as they are allocated
    if any generated event may be able to make use
    of the next upstream slot

55
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.A Schedule Propagation
  • Since nodes must always listen to upstream
    receive slots, as all events must be passed up to
    the root, regardless of event type, the unique
    upstream slots for specific event types would not
    be meaningful
  • The downstream control event includes data used
    by tree construction algorithm such as the number
    of hops to the root and the parent nodes
    network-unique identifier
  • For each downstream send event, the simultaneous
    time slot at the next level down is a receive
    time slot for the same type of event
  • Similarly, for upstream send events, the
    concurrent time slot at the next level up is a
    corresponding receive time slot for the same type
    of event

56
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • TD-DES schedules time slots in two modes
    deterministic and speculative where the
    deterministic algorithm is used for downstream
    and the speculative algorithm for upstream
    dissemination
  • It is assumed that most event propagation would
    be downstream
  • In the deterministic algorithm, events are
    propagated in back to back iterations where each
    iteration is further divided into slots of fixed
    width
  • The scheduler (root node) knows the exact events
    to be broadcast at the beginning of each
    iteration and allocates the number of slots
    required accordingly
  • The schedule is propagated to every node in the
    form of a control packet at the beginning of each
    iteration

57
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • A control packet can also contain timing
    information for the next control packet, if
    iterations are not fixed length
  • When the root node starts transmitting events,
    each node leaves radio in Rx mode for the
    duration of the slot when some interesting event
    will arrive
  • Figure 5 presents the process of deterministic
    scheduling
  • R and S denote the receive and send slots for the
    control events
  • Event e1 generated during iteration k cannot be
    scheduled till iteration k1














  • The control event transmitted during the second S
    includes the schedule for iteration k1
  • The exact time slot during which e1 will be
    scheduled is determined by the specific ordering
    criterion

58
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • In speculative scheduling, the scheduler
    estimates the expected frequency of event types
    at the root node and pre-allocates slots based on
    this frequency estimation
  • Since allocation of slots for each event type is
    periodic which means the same from one iteration
    to the next, no schedule broadcasting is needed
    except when updating schedule

Figure 5 Deterministic Scheduling
59
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • The drawback of speculative scheduling is that
    the nodes may have to stay in Rx mode for
    scheduled slots regardless of whether or not
    event is coming
  • Figure 6 presents the process of speculative
    scheduling
  • Event e1 is received during iteration k after its
    scheduled slot (indicated by the dashed lines),
    therefore, e1 needs to be queued before it can be
    transmitted during its slot in iteration k1

Figure 6 Speculative Scheduling
60
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • The schedule decided by the root node is known to
    every node irregardless of the algorithm used
  • A child nodes downstream schedule is one slot
    behind its parent nodes downstream schedule,
    whereas a child nodes upstream schedule is one
    slot ahead of the parent nodes upstream schedule
  • This allows tight pipelining a
    downstream/upstream event received by node i in
    slot t will be sent downward/upward to is
    children/parent in slot t 1
  • If shifting happens at the boundary of upstream
    and downstream schedule, downstream scheduling
    will shift beyond the neighboring upstream
    schedule and similarly, upstream scheduling will
    shift beyond the neighboring downstream schedule

61
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.B Deterministic and Speculative Scheduling
  • The schedule created by the root node can be
    extended at an internal node to accommodate
    events generated at internal nodes
  • Since a sub-tree rooted at an internal node may
    not be interested in every event therefore, when
    an internal node is propagating down root
    schedule to its descendants, it can extend the
    root schedule by replacing those un-interesting
    slots with its own events or if more slots are
    required, it can modify blank slot in the root
    schedule
  • This extended schedule only affects the sub-tree
    rooted at this internal node

62
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • Determines how the root node decides on event
    ordering in the downstream schedule
  • When iteration length and slot length become
    fixed, a deterministic schedule becomes an
    ordering of events and it is determined according
    to one of (or combination of) three criteria
  • priority - the relative priority of an event type
    over other event types
  • popularity - the number of nodes subscribing to
    an event type
  • latency constraint - the max. dissemination delay
    for an event type
  • Priorities can be specified by the
    application-layer for event types at the root
    node and passed down the tree within the
    downstream control event
  • If the priorities are relatively fixed, they need
    to be included in the control event in case of
    new event types are added or the priorities change

63
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • Events can also be ordered by popularity
  • It is assumed that the event types that are most
    subscribed to are considered most important by
    the system, so they are scheduled first in the
    upcoming iteration(s)
  • The tree-construction and maintenance layer of
    TD-DES gathers the popularity of each event type
    in a bottom up manner
  • Consider a subscription to a specific type of
    event ei
  • Each node p maintains count(ei) indicating how
    many nodes in its sub-tree are interested in this
    event of type ei

64
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • Using subscript to indicate location of the
    variable, countp(ei) can be computed recursively
    by
  • where 1 is for case p itself is subscribed 0
    indicates otherwise
  • If latency constraints are specified by the
    application layer, TD-DES will use the average-
    and worst-case latency dissemination estimates
    when scheduling events
  • The overall dissemination latency of an event can
    be reduced by scheduling it as early as possible
    reduces the scheduling delay component of the
    latency

65
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • Hop-count-based distance is used as an estimator
    instead due to unavailability of real latency at
    time of scheduling
  • The number of hops from root for a node k
    subscribing to event type ei is called the
    distance of ei at node k
  • distanceavg(ei) avg. distance for all nodes
    subscribing to event type ei
  • distancewst(ei) the worst-case distance
  • The tree gathers data by having each internal
    node maintain partial values for its own sub-tree
    and pass these values up to its parent node in
    its upstream control event

66
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • Each node j maintains the following metrics in
    addition to count(ei), which it passes to its
    parent
  • costj(ei) the total number of hops an event ei
    must be propagated to the entire sub-tree rooted
    at the current node j
  • avg_costj(ei) the average number of hops an
    event ei must be propagated per interested node
    inside the sub-tree rooted at the current node j
  • max_costj(ei) the maximum number of hops an
    event ei must be propagated to an interested node
    inside the sub-tree rooted at the current node j
  • Each node j passes its costj(ei) and
    max_costj(ei) values to the parent as parameters
    of its upstream control event

67
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • For each child j of an internal node k, the
    parent node calculates its own values
    recursively the costk(ei) at k is calculated in
    terms of each child
  • The maximum cost value is the maximum of the
    maxima of its children plus 1
  • At each node, the avg_costk(ei) is a derived
    value of countk(ei) and costk(ei)

68
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.C Scheduling Criteria
  • For each child j of an internal node k, the
    parent node calculates its own values
    recursively the costk(ei) at k is calculated in
    terms of each child
  • The root node, r, defines, for each event type
    ei, the system-wide count and distance values in
    the following way as count(ei) countr(ei),
  • distanceavg(ei) avg costr(ei), and
    distancewst(ei) max costr(ei)
  • Since all internal nodes are interested in
    knowing these three values, the root node
    disseminates these values in a downstream control
    event as they change

69
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • III.D Interleaved Scheduling
  • This stage determines the actual sequence of the
    time slots allocated for the next iteration
  • The number and ordering of events in downstream
    schedule and number of slots in upstream schedule
    are complete
  • The sequencer must derive a set of ordered slots
    for the next iteration from these two schedules
  • Two choices either place upstream and downstream
    slots separately side by side (a.k.a. clustered)
    or interleave them
  • In the clustered version, the ordered downstream
    set is placed unbroken, followed immediately by
    ordered upstream set and followed by some blank
    time slots
  • A downstream control event is placed at the
    beginning of each iteration

70
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • Advantages
  • The authors strive to achieve maximum power
    conservation by way of completely powering down
    the radio of the sensor nodes during the portions
    of the schedule that do not match the its
    particular event subscription
  • The authors did not try to reinvent the wheel by
    introducing a radically new protocol proposed
    protocol, TD-DES, is intended as an application
    overlay to the already established CSMA/CA
    wireless MAC layer
  • The publish/subscribe style of event based
    communication makes the protocol well suited for
    dynamic ad hoc environment
  • Disadvantages
  • Does not consider transmission failure
  • No mentioning about the construction of the
    topology tree
  • The time synchronization is an assumption made by
    the authors

71
Power-Efficient Data Dissemination in Wireless
Sensor Networks Cetintemel 2003
  • Suggestions/Improvements/Future Work
  • Since constructing a tree structure that is
    optimal with respect to power consumption is
    NP-complete, we can have the following two
    heuristics
  • Centralized Tree-topology In this case, we can
    periodically recompute the tree using centralized
    incremental power heuristic, where we add on
    sensor at a time with the least incremental
    transmit power
  • Distributed Tree-topology Decision on the sensor
    nodes position in the tree is done locally by
    collaborating with the neighbor nodes
  • As mentioned in the literature, we can extend the
    protocol to include upstream and downstream
    aggregation and caching
  • Future work can be summarized as follows
  • Implementation and clock synchronization
  • Mobility and reliability
  • Caching and aggregation

72
References
  • Agrawal 2001 D.P. Agrawal and Arati
    Manjeshwar, Teen a routing protocol for enhanced
    efficiency in wireless sensor networks, In
    Proceedings of Tenth International Conference on
    Computer Communications and Networks, 2001, pp.
    304-309.
  • Cetintemel 2003 U. Cetintemel, A. Flinders,
    and Y. Sun, Power-Efficient Data Dissemination in
    Wireless Sensor Networks, In proceedings of the
    3rd ACM International Workshop on Data
    Engineering for Wireless and Mobile Access
    (MobiDE03), September 2003.
  • Ganesan 2002 D. Ganesan, D. Estrin, and J.
    Heidemann, DIMENSIONS Why do we need a new Data
    Handling architecture for sensor networks?,
    Proceedings of First Workshop on Hot Topics in
    Networks (HotNets-I), October, 2002.
  • Greenstein 2003 B. Greenstein, D. Estrin, R.
    Govindan, S. Ratnasamy, and S. Shenker, DIFS A
    Distributed Index for Features in Sensor
    Networks, In the Proceedings of First IEEE
    International Workshop on Sensor Network
    Protocols and Applications, May 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.

73
References
  • Kim 2003 H. S. Kim, T. Abdelzaher, and W. H.
    Kwon, Minimum-Energy Asynchronous Dissemination
    to Mobile Sinks in Wireless Sensor Networks, ACM
    SenSys, Los Angeles, CA, November, 2003.
  • Kulik 2002 J. Kulik, W. Heinzelman and H.
    Balakrishnan, Negotiation-Based Protocols for
    Disseminating Information in Wireless Sensor
    Networks, Wireless Networks 8(2-3), 2002, pp.
    169-185.
  • Lindsey 2001 S. Lindsey and C.S. Raghavendra,
    Pegasis Power-efficient gathering in sensor
    information systems, In Proceedings of
    International Conference on Communications,
    2001.
  • Madden 2002 S. Madden, M.J. Franklin, J.M
    Hellerstein, and W. Hong, TAG a Tiny Aggregation
    Service for Ad-Hoc Sensor Networks, In
    Proceedings of Fifth Symposium on Operating
    Systems Design and Implementation (OSDI), Boston,
    Massachusetts, December, 2002.
  • Ratnasamy 2002 S. Ratnasamy, B. Karp, L. Yin,
    F. Yu, D. Estrin, and R. Govindan, GHT A
    Geographic Hash Table for for Data-Centric
    Storage, In Proceedings of First ACM
    International Workshop on Wireless Sensor
    Networks and Applications (WSNA 2002), Atlanta,
    GA, September, 2002.
  • Shenker 2002 S. Shenker, S. Ratnasamy, B.
    Karp, R. Govindan, and D. Estrin, Data-Centric
    Storage in Sensornets, In Proceedings of First
    ACM SIGCOMM Workshop on Hot Topics in Networks
    (HotNets 2002), Princeton, NJ, October 2002.
  • Tilak 2002 S. Tila
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