Intelligent transportation systems (ITS) have become an increasingly common part of everyday life. Using a combination of wireless communications networks, Internet of Things (IoT) devices, automation, artificial intelligence, edge and fog computing and various other cutting-edge technologies and frameworks, transportation providers are enhancing and improving both their operations and services.
With 5G and autonomous vehicles coming to the roads in the not too distant future, transportation sector is constantly looking for ways to increase capacity of their infrastructure to harness the power of big data and edge computing.
There are numerous researchers and developers from a multitude of different disciplines dedicating their research to trying to do exactly that and various different approaches to both networking and computing have been proposed.
In this article, we’ll be looking at Edge Mesh networks and how their application could revolutionize the way in which we approach intelligent transportation systems.
So, lets jump straight in.
What is an “Edge Mesh” Network?
“Edge Mesh” is a concept introduced in the 2017 paper Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things, authored by Yuvarj Sahni, Jiannong Cao, Shigeng Zhang, and Lei Yang.
In the paper, Edge Mesh is described as being a new computing paradigm that is able to distribute decision making tasks among the various edge devices connected to a network, rather than transmitting all of this data to a single centralized server.
Combining the use of both mesh networks and edge computing within intelligent transportation systems, Edge Mesh integration could bring various benefits such as better scalability, distributed intelligence, and enhanced performance, security and privacy.
But, there are several important differences between traditional mesh networks and Edge Mesh. For example, in traditional mesh networks data is transmitted from one node to another until it reaches its destination and the nodes themselves make purely routing-focused decisions.
However, within an Edge Mesh architecture, alongside routing decisions, edge devices are able to make decisions regarding different computational tasks as well as enabling interactions between connections such as end-devices and cloud technologies.
In this role, edge devices are able to make decisions such as establish whether two different devices can understand one another’s data or which devices should be able to share data with others, this in turn enables them to take on much more responsibility.
How Could Edge Mesh Revolutionize Intelligent Transportation Systems?
Now that we understand the idea behind Edge Mesh, let’s look at the benefits that it could bring to intelligent transportation systems.
We’ve listed five of the most likely ways in which Edge Mesh could enhance or improve intelligent transportation systems, however, with further investigation and development, many other benefits could become known as these technologies mature.
One of the most obvious advantages of Edge Mesh architectures is the distribution of computational tasks to other edge devices. This can enable enhanced processing and response times for all devices involved as well as acting as a safeguard to overloading individual devices.
Which is great for intelligent transportation systems.
With vehicles generating a vast amount of data while in use, while also moving at variable speeds over variable destinations, it is essential that data processing and response times are as efficient as can be.
Eventually, autonomous vehicles will require computational tasks to be performed locally at real-time speeds. At the moment, Edge Mesh architectures within intelligent transportation systems look to be one of few potential routes to realizing this ambition.
Scalability has often been an issue with cloud services and, with the vast distances many intelligent transportation systems are required to cover, offloading data straight to the cloud would be impractical and counter-productive.
As the number of devices involved in ITS grows, scalability will become an increasingly important issue within intelligent transportation systems. However, Edge Mesh architectures could solve this issue.
Limited bandwidth is a known issue among IoT systems and has been known to cause a communications bottleneck. Within an Edge Mesh system, however, data would be distributed among many devices that can then share the data amongst themselves so as to prevent a communications bottleneck.
This enables intelligent transportation systems to be scaled up as and when they are required to be.
Within intelligent transportation systems, technologies such as traffic management systems, smart parking, autonomous vehicles, and emergency response systems all require low latency.
When time is critical, again, cloud solutions are incapable of providing an efficient enough service for this application.
In this instance, Edge Mesh networks solve the issue through the use of edge devices, which can be used to both share data amongst other edge devices and perform computational tasks themselves.
This combination of device cooperation and local computation can work to bring enhanced response times from the devices involved and improve the overall performance and ability of intelligent transportation systems.
Fault tolerance within mesh networks is an increasingly attractive aspect of traditional mesh networks. Because data is shared across many different devices, if one were to fail, other devices can be used to distribute the same data.
In Edge Mesh networks, redundancy is also provided for computational tasks due to the distributed nature of their architecture.
With multiple devices responsible for computational tasks, the failure of one devices won’t bring its specific computational tasks to a standstill.
This would be incredibly beneficial to intelligent transportation systems and autonomous vehicles so as to ensure that both computational and data communication processes remain viable regardless of individual device failure.
5-Enhanced Privacy & Security
Security and privacy are two of the biggest and most current issues within connected technology. Businesses and enterprises looking to secure their assets are tasked with not only finding out what’s a risk, but also how to deal with it.
With IoT devices collecting an increasing amount of identifiable and sensitive data, the necessity for improved approaches to security is becoming more and more apparent.
However, with Edge Mesh networks, that security could potentially be part of the design. As data is processed locally, it doesn’t come into contact with external parties and becomes much more difficult for anyone to intercept as it is not shared over the internet.
This approach would help to bolster the security of intelligent transportation systems by ensuring that personal, sensitive, or critical data was secure and the risk of being compromised significantly lowered.