Data aggregation (DA) is an effective technique to support the growing traffic of the machine-to-machine (M2M)communication systems. A quick overview of M2M Communication System is discussed in First Chapter. Let’s dig in ‘recent development’ on this technology today.
[1] - [5] go into great detail on association difficulties. The authors of [1,] present a low-complexity distributed algorithm-based user association technique for load balancing in a heterogeneous network. The authors of [2] suggest a user-associative technique to improve base station energy efficiency. The suggested method is a cognitive heuristic algorithm that employs context-aware information to link users in a cost-effective manner, taking into account both access and backhaul energy usage. [3] proposes a traffic-aware loadbalancing technique for M2M networks that is based on software-defined networking.
The authors of [4] discuss the twin goals of the gateway and in-network load balancing. The reactive and adaptive load balancing strategies are then proposed as a generic solution for any multi-hop wired/wireless network with numerous data gateways linking it to the infrastructure. The authors of [5] suggest an association strategy for allocating users to their serving access points inside the uplink of a small cell network based on the college admissions game from game theory literature. The authors of [6] propose an artificial neural network that can forecast network performance based on traffic characteristics. For this, a traffic behavior model based on bandwidth and latency data over various channels is created.
Many different sorts of user associations have been studied. I attempted to compare all of these proposed ways to the typical AG selection scheme, which is based only on the received signal strength.
REFERENCES
[1] Q. Ye, B. Rong, Y. Chen, M. Al-Shalash, C. Caramanis and J. G.Andrews, "User Association for Load Balancing in HeterogeneousCellular Networks," IEEE Transactions on Wireless Communications,vol. 12, no. 6, pp. 2706-2716, 2013.
[2] A. Mesodiakaki, F. Adelantado, L. Alonso and C. Verikoukis, "Energyefficient context-aware user association for outdoor small cell heterogeneous networks," 2014 IEEE International Conference on Communications (ICC), pp. 1614-1619, 2014.
[3] Y. Chen, L. Wang, M. Chen, P. Huang and P. Chung, "SDN-EnabledTraffic-Aware Load Balancing for M2M Networks," IEEE Internet ofThings Journal, vol. 5, no. 3, pp. 1797-1806, 2018.
[4] Y. Miao, S. Vural, Z. Sun and N. Wang, "A Unified Solution for Gatewayand In-Network Traffic Load Balancing in Multihop Data CollectionScenarios," IEEE Systems Journal, vol. 10, no. 3, pp. 1251-1262, 2016.
[5] W. Saad, Z. Han, R. Zheng, M. Debbah and H. V. Poor, "A collegeadmissions game for uplink user association in wireless small cellnetworks," IEEE INFOCOM 2014 - IEEE Conference on ComputerCommunications, pp. 1096-1104, 2014.
[6] A. M. R. Ruelas and C. E. Rothenberg, "A Load Balancing Methodbased on Artificial Neural Networks for Knowledge-defined Data Center Networking," Proceedings of the 10th Latin America NetworkingConference (LANC ’18), pp. 106–109, 2018.
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