Edge computing use cases are discussed in this blog. Both the Fog Computing and Edge Computing are meant to bring the processing and computation tasks near the edges of the network or closer to the origin of data. The paradigms have recently received widespread acceptability from the environments supporting real-time applications. As a result of the deployment of fog and edge computing services, not only the latency but the network bandwidth is also minimized as compared to the purely cloud based environments.
To read the difference between fog and edge computing, click here.
Edge computing use cases
There are several use cases of fog and edge computing that have been discussed widely. However, here in this blogpost we discuss some use cases of fog and edge computing that have received little attention.
1. Edge computing for poultry farms
The first edge computing use case we discuss is the smart poultry farms for flock monitoring. Smart poultry farms are supposed to be equipped with different Internet of Things (IoT) sensors, for example humidity and temperature sensors, CO2 sensor, and Ammonia sensors etc. The purpose of sensor deployment in the farms is to monitor the levels of different gases produced. The increased levels of gases can be harmful for the health of the birds in the farm. Consequently, the levels of different gases have to be kept at a balanced level and hence it requires continuous monitoring for subsequent transmission to staff so that necessary actions could be taken. Therefore, deploying an edge device in the environment would be effective in handling the frequent data monitoring and transmission to the monitoring staff. The reason to deploy edge node is that since the data is being sensed continuously from multiple sensors, a device that can efficiently process all the data near the location of its generation is needed. Therefore, the edge node in the vicinity of data generation sensors effectively manages and processes the data streams. Â
2. Waste collection and management
Waste collection and management is one of burning issues in the contemporary urban settings. Particularly, in societies with limited resources, the issue becomes even more grave because improper or lack of waste collection and management procedures result in messy surroundings. This further leads to several health-related issues and also causes the spread of virus. Therefore, using the IoT based solutions coupled with fog or edge computing can contribute towards the development of sustainable and environment friendly surroundings.
We discuss a use case that is truly practical. Â Imagine there are smart bins or smart dumpsters located in different areas or streets of a city. The bins contain sensors that help indicate the level of waste in the bin. If the waste in the bin or dumpster reaches a certain threshold, the sensors notify the relevant system or personnel equipped with the smartphone to collect the waste. As a result, the waste collection vehicles can be routed to those specific locations for waste collection. Now, the question is where and why we will deploy the fog nodes. Since the sensors in bins or dumpsters are continuously sensing the waste levels in the bins, therefore, high end fog or edge nodes are required near the locations of the bins. Fog nodes can efficiently process the information being received from the sensors and hence, the issues of latency and bandwidth can be effectively resolved. Moreover, significant fuel costs for the waste collection vehicles can be minimized because the vehicles will visit a particular area for waste collection only when it is notified by the monitoring sensors in the bins or dumpsters. Thus, the unnecessary visits of the collection vehicles can be reduced.
Read more from TechoReview:
Internet of Things Applications and Challenges
Research Trends in Fog and Edge Computing
Virtualization in cloud computing Â
3. Safe cities for surveillance
Another use case of edge computing is in safe cities for surveillance and monitoring to prevent the undesirable incidents and events. Most of the advanced cities are equipped with smart cameras. The cameras contain sensors and when a particular vehicle or object gets in the range of the camera, an image of the vehicle and the people inside the vehicle is captured and stored in the repository. This captured information can be highly beneficial if appropriately utilized by applying the machine learning techniques.
A fog-based deployment is more beneficial in this case instead of cloud implementation. The reason is that in cloud-based implementation, the machine learning algorithm to identify the suspected individuals or culprits executes on the cloud. Transmitting each of the captured images to the cloud for subsequent processing not only increases the latency but also increases the network bandwidth utilization. Consequently, relaying the information regarding a suspect to other check posts where the suspects is likely to pass might not be useful due to the delays. On the other hand, in a fog-based implementation, the machine learning algorithms can be executed on the fog nodes. Therefore, the frequent accesses to the cloud that eventually result in increased latency and bandwidth consumption can be reduced.
4. Edge nodes in parking lots
In smart parking lots, fog or edge nodes can be deployed to help riders find the vacant parking spaces. This is an interesting use case particular in crowded cities where finding the vacant parking lots in rush hours is an issue. Smart parking lots contain smart cameras or sensors and are continuously monitoring the parking area to determine the availability of parking slots at a certain time. Upon entering the parking lot, the system directs the driver to the appropriate position.
5. Fog gaming
Another interesting use case is fog gaming. Today most of the games are multiplayer games that are played in distributed fashion where the players are not at the same location. These games require enormous amount of memory and processing power due to massive amount of data generated. For example, Fortnite and World of Warcraft generate around 100 GB of data in a second, which is beyond the capacity of the ordinary computing machines to handle. Therefore, cloud gaming was introduced to overcome the issues related to processing. Subsequently, the concept of fog gaming was introduced to bring the processing closer to the game players with the objective to minimize the latency and make the streaming efficient.
In addition to the use cases discussed above, there are also various other interesting applications of fog and edge computing namely, Industrial IoT, smart healthcare, and smart homes etc.
In conclusion, both the fog and edge computing have attained great importance in this data driven era to efficiently process the data and bring both the computations and intelligence closer to the place of data creation.
