The Internet-of-Things (IoT) is poised for dramatic growth. Hundreds of applications have already been deployed for a variety of purposes from avalanche prevention to smart water meters and the number of applications is multiplying rapidly.
IDC forecasts that the worldwide market for IoT solutions will grow from $1.9 trillion in 2013 to $7.1 trillion in 2020. The number of cars connected to the Internet worldwide alone will grow more than six fold to 152 million in 2020 from 23 million in 2013, according to IHS Automotive.
As IoT becomes more prevalent, communications capabilities will be extended to billions of objects making signalling traffic a potential bottleneck.
Signaling Traffic vs. Data Traffic
IoT requires that each device send small amounts of data periodically. When each of these signaling messages are added up and multiplied by the number of devices the impact on network congestion is even more than the increase in data traffic.
Network applications need to communicate with their devices to determine network status including key information on which parts of the network are congested, the location of the device, its wake-up times, who has authorized access and others.
The level of messages can result in an inefficient use of resources in both the network and the device. For example:
• Push notifications can be sent to a large number of devices within a small time window, creating huge spikes in signaling load
• Devices can send frequent “keep alive” messages just to ensure the network address translation (NAT) port remains open
• Devices can ping the network every few minutes when unable to connect to the application server
When each of these inefficiencies is multiplied by millions of devices, the extra load on the network can have a negative impact on the subscriber quality of experience.
Methods for Managing Signaling Traffic
There are several different approaches for addressing increased traffic loads as a result of IoT. One solution is to develop a separate, different kind of network that can send tiny messages across a low-power, wide-area networks or LPWANs. For example, the city of San Francisco is set to get a new cellular network later this year, designed exclusively for devices provided by the French company SigFox. However creating a separate network is extremely expensive, and outside of major cities it is not always a viable option.
A less complicated and probably less costly approach would be to schedule, on the same network, when transmissions occur to avoid traffic spikes. For example, some applications, such as smart meters and vending machines, have very flexible communication requirements and their transmissions can be scheduled for off hours, while other applications, such as security systems and health care, can always have network access. For example, the following schedule could help control signalling congestion:
• Water meters – 1:00 a.m. – 2:00 a.m. in increments of minutes
• Gas meters – 3:00 a.m. – 4:00 a.m.
• Waste Receptacles – 4:00 a.m. – 5:00 a.m.
• Home security devices – whenever necessary
However this approach would require the operator to communicate with all of the sources of IoT data and then have them all agree to a schedule, which is highly impractical.
Signaling Orchestration
The best option is to support more intelligent signalling to reduce congestion. With this approach, inefficiencies with signalling can be eliminated by streamlining and orchestrating the data traffic to reduce the number of signalling messages. The sending of unnecessary data control messages can be managed or delayed until there is a more opportune time for transmission. Here are some examples of how signalling can be tweaked to improve network performance.
• Data control messages can be delayed, queued and then transmitted in batches
• Repeated data can be identified and piggybacked to prevent the need to create and tear down multiple messaging sessions
• Signalling messages can be balanced over time to prevent burst
These optimization techniques can reduce data signalling events by 5%-15%, reduce RF load and save battery life. By influencing traffic flows at the network core, the increased efficiency is automatic and all signalling traffic is optimized. Optimizing signalling transmissions could help ensure IoT will not be causing mass disruption to the network, and lead to a diminished Quality of Experience (QoE) for network subscribers and a potential loss in revenue and reputation for operators. With legislation like the current EU directive requiring all cars to be connected, IoT will most likely become a reality that operators will need to manage, and orchestrating at the core could be the most practical solution.
