Data Dissemination and Fusion
in Sensor Networks
The Need for Data Dissemination and Fusion
– Energy efficiency is an essential factor; therefore, short-range hop-byhop 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
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]:
1.
2.
Continuous
o
LEACH [Heinzelman+ 2000, 2002] is designed for routing data to base
stations in static wireless sensor networks
o
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
o
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
Taxonomy of Data Delivery Models in Wireless Sensor
Networks
3.
Event-driven
o
4.
SPIN (Sensor network Protocols via Information Negotiation)
[Kulik+ 2002] are set of protocols designed to disseminate data to
all nodes in the network
Hybrid
o
The above three approaches can coexist in the same network
Directed Diffusion
[Intanagonwiwat+ 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
Directed Diffusion
[Intanagonwiwat+ 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
Directed Diffusion
[Intanagonwiwat+ 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 = 01:30:45
// 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
Directed Diffusion
[Intanagonwiwat+ 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
1.
2.
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 entry’s timestamp and duration fields
Directed Diffusion
[Intanagonwiwat+ 2000]
Interests and Gradients
3.
If there exists both entry and a gradient,

The node updates the entry’s 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
Directed Diffusion
[Intanagonwiwat+ 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
1.
If no match exist, the data message is dropped
2.
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
Directed Diffusion
[Intanagonwiwat+ 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
Directed Diffusion
[Intanagonwiwat+ 2000]
[Figure adapted from Intanagonwiwat+ 2000]
Directed Diffusion
[Intanagonwiwat+ 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
Directed Diffusion
[Intanagonwiwat+ 2000]
Disadvantages:
–
It may consume memory since all the attribute list is being sent
Suggestions/Improvements/Future Work:
–
Exploration of possible naming schemes
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 resourceadaptation
–
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
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
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
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
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
SPIN [Kulik+ 2002]
SPIN Protocols
1. SPIN-PP: A Three–stage 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
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
SPIN [Kulik+ 2002]
SPIN Protocols
3. SPIN-BC: A Three–stage 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-suppression
–
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
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
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
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
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
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:
1.
Data can be processed, aggregated, and/or pruned while
propagating towards the query sink
DIFS: A Distributed Index for Features in Sensor
Networks [Greenstein+ 2003]
–
There are two extensions to this approach for further energy savings:
1.
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
2.
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
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
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
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 energyefficient 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
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:
1.
Sensor value(s):
o
Includes raw sensor values that comprise high-level events,
composite measurements and summary statistics such as
average, median, etc
o
Examples include the peak temperature of a hot region, the speed
that an animal target is moving
o
Sensor values can be search over a designed area and they are
represented as integers or floating point numbers
DIFS: A Distributed Index for Features in Sensor
Networks [Greenstein+ 2003]
Classification of Event Properties and Relationships
2.
Timing parameters:
o
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
3.
Spatial dimensions:
o
Refers to physical shape and location of an event, i.e., hot regions
larger than a given area
o
Regions can described as enclosing circles, ellipses, or polygons
and their points of interest can be represented as integer or floating
point coordinates
DIFS: A Distributed Index for Features in Sensor
Networks [Greenstein+ 2003]
Classification of Event Properties and Relationships
4.
Event Interrelationships:
o
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?
o
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
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:
1.
all nodes may be used to store raw time series data and events
2.
a subset of nodes serve as index nodes to facilitate search
DIFS: A Distributed Index for Features in Sensor
Networks [Greenstein+ 2003]
Storage and Search Architecture
Figure 2: A storage and search architecture
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
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
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
o
route the query using hierarchical dissemination, as in structured replication,
rather than sending unicast messages to each of the covering nodes
o
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
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
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
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 network’s 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
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
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 pernode 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
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
Power-Efficient Data Dissemination in Wireless Sensor
Networks [Cetintemel+ 2003]
II.C Data/Event Model
(e1,e2)
e2
(e1)
e2
(e1,e2)
N3
N2
N1
[e1,e2,e3]
e2
[e1,e2]
(e1)
e2
[e1,e2] (e2)
N4
N5
[e1,e2,e3]
e2
[e2] (e2)
N6
[e1,e2,e3] (e3)
N7
[e3]
e2
(e1,e2,e3)
N8
[e1,e2,e3]
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
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
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
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
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
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
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, X+1.
–
In the following time slot, the node at level X+1 passes the control event down
to level X+2, 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
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
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
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 node’s
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
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
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 k+1
–
The control event transmitted during the second S includes the schedule for
iteration k+1
–
The exact time slot during which e1 will be scheduled is determined by the
specific ordering criterion
Power-Efficient Data Dissemination in Wireless Sensor
Networks [Cetintemel+ 2003]
III.B Deterministic and Speculative Scheduling
Figure 5: Deterministic 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
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 k+1
Figure 6: Speculative Scheduling
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 node’s downstream schedule is one slot behind its parent node’s
downstream schedule, whereas a child node’s upstream schedule is one slot
ahead of the parent node’s upstream schedule
–
This allows tight pipelining: a downstream/upstream event received by node i
in slot t will be sent downward/upward to i’s 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
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
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:
–
o
priority - the relative priority of an event type over other event types
o
popularity - the number of nodes subscribing to an event type
o
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
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
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
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
–
o
distanceavg(ei): avg. distance for all nodes subscribing to event type ei
o
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
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:
o
costj(ei): the total number of hops an event ei must be propagated to the
entire sub-tree rooted at the current node j
o
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
o
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
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):
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
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
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
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:
o
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
o
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:
o
Implementation and clock synchronization
o
Mobility and reliability
o
Caching and aggregation
References
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in wireless sensor networks, In Proceedings of Tenth International Conference on Computer
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[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 (MobiDE’03), 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.
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. Tilak, N. Abu-Ghazaleh, and W. Heinzelman, A Taxonomy of Wireless Micro-Sensor
Network Models, Mobile Computing and Communications Review (MC2R), vol. 6, no. 2, April
2002, pp. 28-36.