The document discusses information-based sensor tasking (IBST) methods, which allow sensors in a network to efficiently prioritize sensing activities based on importance, thereby conserving energy and enhancing collaboration. It also covers techniques like data aggregation, information-driven sensor querying (IDSQ), and geographic hash tables (GHT) to improve data management and network efficiency. Additionally, it emphasizes the value of in-network aggregation and data-centric storage (DCS) for optimizing data transmission and retrieval in wireless sensor networks.

1. SENSOR TASKING AND
CONTROL
Presented by
D.S.MYDHEESWARAN
N.SIVA
N.NITHYA

2. Information based sensor
tasking
Information-Based Sensor Tasking (IBST) is a
smart way for sensors in a network to decide when
and what to sense. Instead of just sensing all the
time, sensors use IBST to focus on what's most
important. It helps them save energy and work
together better to gather the right information at
the right time.

3. Importance of routing information
aggregation
 Reduced Energy Consumption
 Improved Network Efficiency
 Minimized Latency
 Reduced Data Redundancy
 Increased Network Lifetime

4. Sensor tasking
 Sensor tasking is the process of assigning
specific sensing tasks to individual
sensors in a sensor network. These tasks
are such as temperature, humidity, light,
or specific events.
 Sensor tasking involves determining when
and where sensors should perform these
tasks to optimize resource usage and
achieve the desired monitoring objectives.

5. Data aggregation and its
importance in WSN
 Data aggregation is a process of
combining and summarizing data from
multiple sensor nodes into a smaller set of
meaningful information.
 Importance:
◦ Reduced Energy Consumption
◦ Bandwidth Conservation
◦ Lower Latency

6. Temporal data
 Temporal data refers to data that is
associated with a specific time or time
range. It includes any information that
has a time component, such as
timestamps, dates, durations, or
sequences of events.

7. IDSQ (Information-Driven Sensor
Querying)
 Information-Driven Sensor Querying
(IDSQ) is a technique used to efficiently
gather information from sensor nodes in
response to specific queries or requests.
 This reduces unnecessary data
transmission and processing, leading to
energy savings and increased network
efficiency.

8. Geographic hash table (GHT)
 The Geographic Hash Table (GHT) is a data
storage and retrieval mechanism used in
sensor networks. It associates data with
geographic coordinates, allowing nodes in the
network to store and retrieve data based on
their physical location.
 GHT uses a hash function to map data to
specific geographic regions, ensuring that
data is stored at nodes closest to its
corresponding coordinates. This approach
helps reduce network traffic and improves
data access efficiency in sensor networks.

9. In-network aggregation
 In-network aggregation is a technique
used to reduce the amount of data that
needs to be transmitted to a central
location or sink node. Instead of each
sensor node sending its raw data directly
to the sink, nodes can aggregate or
combine their data locally before
transmitting it.

10. Data centric storage in WSN
 Data centric storage (DCS) is a method
proposed to support queries from any
node in the network by providing a
rendezvous mechanism for data and
queries that avoids flooding the entire
network.
 Each event is stored in a sensor node,
which is the owner of the event. If a
query is intended for an event, it is
directed to that sensor node.