A Two-Tier Adaptive Data Aggregation Approach for M2M Group-Communication

Abstract

Network lifetime is the time interval in which the nodes are operational. Considering that machine-to-machine (M2M) devices have limited energy resources, an important challenge in M2M communications is to prolong the network lifetime. The constrained application protocol (CoAP) supports multi-target monitoring applications in M2M communications, allowing the creation and maintenance of groups, as well as their periodic communication. It is essential to aggregate the CoAP group-communication over the paths to increase the network lifetime of low-power M2M devices, since data aggregation reduces the use of energy-consuming hardware (e.g., central processing unit and wireless interface). However, the current data aggregation solutions do not specify how to support data aggregation with multiple CoAP-based groups in multi-target monitoring applications. In this paper, the proposed approach, called two-tier aggregation for multi-target applications (TTAMAs), aggregates the data originated from nodes belonging to either the same or different CoAP groups. Furthermore, TTAMA is an adaptive solution because it performs the data aggregation in accordance with the CoAP configurations, such as communication periodicity and data aggregation functions. We compare TTAMA with current data aggregation approaches that use minimum spanning tree and shortest path tree. The results show that TTAMA outperforms the related works in terms of network lifetime and energy consumption.