Routing Challenges and Design Issues in WSN
November 1, 2016
Categorised in: Data Communicaiton & Wireless Sensor Networks
The design of routing protocols in WSNs is influenced by many challenging factors. These factors must be overcome before efficient communication can be achieved in WSNs.
- Node deployment
- Energy considerations
- Data delivery model
- Node/link heterogeneity
- Fault tolerance
- Network dynamics
- Transmission media
- Data aggregation/converge cast
- Quality of service
Node deployment in WSNs is application dependent and affects the performance of the routing protocol.
The deployment can be either deterministic or randomized.
In deterministic deployment, the sensors are manually placed and data is routed through pre-determined paths.
In random node deployment, the sensor nodes are scattered randomly creating an infrastructure in an ad hoc manner.
Sensor nodes can use up their limited supply of energy performing computations and transmitting information in a wireless environment. Energy conserving forms of communication and computation are essential.
In a multi-hop WSN, each node plays a dual role as data sender and data router. The malfunctioning of some sensor nodes due to power failure can cause significant topological changes and might require rerouting of packets and reorganization of the network.
Data Delivery Model
–Suitable for applications that require periodic data monitoring
–React immediately to sudden and drastic changes
–Respond to a query generated by the BS or another node in the network
–The routing protocol is highly influenced by the data reporting method
Depending on the application, a sensor node can have a different role or capability.
The existence of a heterogeneous set of sensors raises many technical issues related to data routing.
Even data reading and reporting can be generated from these sensors at different rates, subject to diverse QoS constraints, and can follow multiple data reporting models.
Some sensor nodes may fail or be blocked due to lack of power, physical damage, or environmental interferences
It may require actively adjusting transmission powers and signaling rates on the existing links to reduce energy consumption, or rerouting packets through regions of the network where more energy is available
The number of sensor nodes deployed in the sensing area may be on the order of hundreds or thousands, or more.
Any routing scheme must be able to work with this huge number of sensor nodes.
In addition, sensor network routing protocols should be scalable enough to respond to events in the environment.
Routing messages from or to moving nodes is more challenging since route and topology stability become important issues
Moreover, the phenomenon can be mobile (e.g., a target detection/ tracking application).
In general, the required bandwidth of sensor data will be low, on the order of 1-100 kb/s. Related to the transmission media is the design of MAC.
- TDMA (time-division multiple access)
- CSMA (carrier sense multiple access)
High node density in sensor networks precludes them from being completely isolated from each other.
However, may not prevent the network topology from being variable and the network size from shrinking due to sensor node failures.
In addition, connectivity depends on the possibly random distribution of nodes.
In WSNs, each sensor node obtains a certain view of the environment.
A given sensor’s view of the environment is limited in both range and accuracy.
It can only cover a limited physical area of the environment.
Since sensor nodes may generate significant redundant data, similar packets from multiple nodes can be aggregated to reduce the number of transmissions.
Data aggregation is the combination of data from different sources according to a certain aggregation function.
Converge casting is collecting information “upwards” from the spanning tree after a broadcast.
Quality of Service
In many applications, conservation of energy, which is directly related to network lifetime.
As energy is depleted, the network may be required to reduce the quality of results in order to reduce energy dissipation in the nodes and hence lengthen the total network lifetime.
Pratik Kataria is currently learning Springboot and Hibernate.
Softwares known and worked on: Adobe Photoshop, Adobe Illustrator and Adobe After Effects.