A Summery on M2M Communication System

The world is on its way to a futuristic technological age in which everything is interconnected. In smart-grid, vehicular telematics, health-care, public surveillance systems, personal navigation, tracking and tracing, and other disciplines, machine-to-machine (M2M) applications are becoming more essential [1]. There are numerous distinctions between M2M and regular human-to-human conversations. The fundamental property that distinguishes M2M communication from other types is that it does not require human supervision. M2M traffic also typically consists of short data packets sent by a large number of low-power, low-mobility devices/MCs [2]. Because M2M traffic differs from standard communication traffic, it is difficult to support M2M traffic with a traditional communication system.

Sources

To boost communication efficiency, data aggregation (DA) can be used [3]. Multiple aggregaters (AGs) are positioned between the data source and the data destination in a cluster-based DA system [4]. These AGs accept data from several sources, combine it, and send it to the appropriate destination. Identifying which serving station serves which machine (MC) according to given regulations is one of the top-tier difficulties. Additionally, in the case of battery-powered MC, energy efficiency improvement is critical. As a result, M2M communications and the MC association have become key study topics.

Signal intensity has traditionally been used to associate nodes. It boosts data rates while lowering latency and energy consumption for a specific transmission. The medium-access strategy, on the other hand, determines whether or not transmission is successful. Because there are so many MCs in M2M communication, random access is preferred. If MCs are linked to AGs based only on signal strength in a DA-based M2Mcommunication system, numerous MCs will be connected to certain AGs while a limited number of MCs will be connected to the other AGs. Collision will be considerable for AGs with multiple MCs in a random access-based system, but collision will be modest for the other AGs.

The MC association, on the other hand, might be based on load balancing, that is, balancing the linked MCs with the AGs. Collisions with all of the MCs will be close in this instance. If the number of MCs assigned to each AG has to be balanced, some MCs will be connected to AGs over substantial distances. As a result, naively balancing the amount of MCs is not a good idea. Furthermore, it is uncertain which technique, signal strength-based or load balancing-based association, gives superior latency, throughput, and energy consumption performance.

REFERENCES

• [1] G. Wu, S. Talwar, K. Johnsson, N. Himayat and K. D. Johnson, "M2M:From mobile to embedded internet," IEEE Communications Magazine,vol. 49, no. 4, pp. 36-43, 2011.
• [2] L. Ferdouse and A. Anpalagan, "A dynamic access class barring scheme to balance massive access requests among base stations over the cellularM2M networks," 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp.1283-1288, 2015.
• [3] D. Li, H. Zhao, L. Gao and H. Zhu, "M2M Access With DynamicCognitive Virtual Operators: A Data Aggregator’s Perspective," IEEEAccess, vol. 5, pp. 5662-5677, 2017.
• [4] V. Pandey, A. Kaur and N. Chand, "A review on data aggregation techniques in wireless sensor network," Journal of Electric and Electrical Engineering, vol. 1, pp. 1-8, 2010.