5.b) With a neat diagram, explain offsite processing topology.
Answer:
Off-site processing
- The off-site processing paradigm, as opposed to the on-site processing paradigms, allows for latencies (due to processing or network latencies); it is significantly cheaper than on-site processing topologies.
- This difference in cost is mainly due to the low demands and requirements of processing at the source itself.
- Often, the sensor nodes are not required to process data on an urgent basis, so having a dedicated and expensive on-site processing infrastructure is not sustainable for largescale deployments typical of IoT deployments.
- In the off-site processing topology, the sensor node is responsible for the collection and framing of data that is eventually to be transmitted to another location for processing.
- Unlike the on-site processing topology, the off-site topology has a few dedicated high processing enabled devices, which can be borrowed by multiple simpler sensor nodes to accomplish their tasks. At the same time, this arrangement keeps the costs of largescale deployments extremely manageable.
- In the off-site topology, the data from these sensor nodes (data generating sources) is transmitted either to a remote location (which can either be a server or a cloud) or to multiple processing nodes. Multiple nodes can come together to share their processing power in order to collaboratively process the data (which is important in case a feasible communication pathway or connection to a remote location cannot be established by a single node).
- The off-site processing topology can be further divided into the following:
- 1) Remote processing and
- 2) Collaborative processing.
1. Remote processing
- This is one of the most common processing topologies prevalent in present-day IoT solutions.
- It encompasses sensing of data by various sensor nodes; the data is then forwarded to a remote server or a cloud-based infrastructure for further processing and analytics.
- The processing of data from hundreds and thousands of sensor nodes can be simultaneously offloaded to a single, powerful computing platform; this results in massive cost and energy savings by enabling the reuse and reallocation of the same processing resource while also enabling the deployment of smaller and simpler processing nodes at the site of deployment. This setup also ensures massive scalability of solutions, without significantly affecting the cost of the deployment.
Figure 3.3 shows the outline of one such paradigm, where the sensing of an event is performed locally, and the decision-making is outsourced to a remote processor (here, cloud). However, this paradigm tends to use up a lot of network bandwidth and relies heavily on the presence of network connectivity between the sensor nodes and the remote processing infrastructure.
2. Collaborative processing
- This processing topology typically finds use in scenarios with limited or no network connectivity, especially systems lacking a backbone network.
- Additionally, this topology can be quite economical for large-scale deployments spread over vast areas, where providing networked access to a remote infrastructure is not viable.
- In such scenarios, the simplest solution is to club together the processing power of nearby processing nodes and collaboratively process the data in the vicinity of the data source itself.
- This approach also reduces latencies due to the transfer of data over the network. Additionally, it conserves bandwidth of the network, especially ones connecting to the Internet.
Figure 3.4 shows the collaborative processing topology for collaboratively processing data locally. This topology can be quite beneficial for applications such as agriculture, where an intense and temporally high frequency of data processing is not required as agricultural data is generally logged after significantly long intervals (in the range of hours). One important point to mention about this topology is the preference of mesh networks for easy implementation of this topology.
