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

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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

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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

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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

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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

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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

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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

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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

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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

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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 difference 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

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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

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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

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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

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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

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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

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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

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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

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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

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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
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• 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
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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
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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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
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• 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
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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
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• 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
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• 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
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• 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
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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
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• 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
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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)

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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Power-Efficient Data Dissemination in Wireless
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• 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

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• 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
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