机器对机器环境中分组通信的数据聚合

Andre Riker, E. Cerqueira, M. Curado, E. Monteiro
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引用次数: 6

摘要

机器对机器(M2M)设备的能源需要尽可能持久。数据聚合是延长网络生命周期的一种合适的解决方案,因为它允许设备减少数据流量。在M2M系统中,M2M平台和约束应用协议(CoAP)使多个实体能够向同一毛细管网络发送并发数据请求。例如,在智能计量场景中,有一些设备测量整个建筑物的用电量。供应商公司要求所有设备每1800秒(即30分钟)发送一次数据更新。另一方面,居民要求他/她的设备每600秒(即10分钟)进行通信。这些并发数据请求在相同的毛细管网络上创建异构组,因为每个组可能能够执行不同的网络内功能并具有唯一的通信时间频率。然而,为定期监视而设计的传统数据聚合解决方案假定在整个网络生命周期中执行单个静态数据请求。这使得传统的数据聚合解决方案不适合M2M环境。为了填补这一空白,本文提出了多组数据聚合(DAMiG),它旨在为异构和并发的CoAP数据请求集提供数据聚合。DAMiG利用组通信的周期性来实现组内和组外的流量聚合。为了实现这一点,DAMiG计算一个合适的聚合结构,并沿着路径应用统计和合并聚合函数。DAMiG能够在具有单个或多个并发CoAP数据请求的场景中减少能耗。此外,内部和外部组路径的选择考虑了节点的剩余能量,避免了剩余能量低的路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Aggregation for group communication in Machine-to-Machine environments
The energy resources of Machine-to-Machine (M2M) devices need to last as much as possible. Data aggregation is a suitable solution to prolong the network lifetime, since it allows the devices to reduce the amount of data traffic. In M2M systems, the M2M platform and the Constrained Application Protocol (CoAP) enable multiple entities to send concurrent data-requests to the same capillary network. For example, in a Smart Metering scenario, there are devices measuring the electricity consumption of an entire building. The supplier company requests all devices to send the data updates every 1800 seconds (i.e., 30 minutes). On the other hand, a resident requests his/her devices to communicate every 600 seconds (i.e., 10 minutes). These concurrent data-requests create heterogeneous groups over the same capillary network, since each group might be able to execute different in-network functions and to have a unique temporal-frequency of communication. However, the traditional data aggregation solutions designed for periodic monitoring assume the execution of a single static data-request during all network lifetime. This makes the traditional data aggregation solutions not suitable for M2M environments. To fill this gap, this paper presents Data Aggregation for Multiple Groups (DAMiG), which is designed to provide Data Aggregation for heterogeneous and concurrent sets of CoAP data-requests. DAMiG explores the group communication periodicity to perform internal and external-group traffic aggregation. To achieve that, DAMiG computes a suitable aggregation structure and applies statistical and merger aggregation functions along the path. DAMiG is able to reduce the energy consumption in scenarios with single or several concurrent CoAP data-requests. Moreover, the selection of internal and external-group paths takes into account the residual energy of the nodes, avoiding the paths with low residual energy.
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