# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 2 System Model and Related Works
### Data Aggregation Strategies in IoT  
Data aggregation (DA) is counterproductive to this distributed raw data from various sources within the network. The accumulation in the network has a significant impact on the number of packets transmitted or their length, thereby reducing energy consumption and prolonging network life [15]. The three main scheduling policies for DA are summarized in [16]: (i) Quadruple simple aggregation requires each router to collect all data items periodically and collect them after a period of time Preset. (ii) Periodic dispersal aggregation operates in a similar manner to a simple periodic strategy; however, transferring data as soon as the node receives data from all its children. (iii) Finally, in a moderated accumulation periodically, each node in the aggregate tree adjusts one hour of time-based on its position and sends the result of the conclusion. One of the chief motivations of utilizing data aggregation schemes in low-power and lossy networks (LLNs) and IoT is to decrease energy consumption and increase network lifetime [17]. Since each network node not only has limited capacity to generate, process, and save data; but it is also responsible for data exchange of its neighbor nodes; the necessity of efficient utilization of network resources is axiomatic. Toward this goal, a number of data aggregation methods are proposed, in order to remove the redundancy of unnecessary and digressive data, and reduce communication costs as well [18; 19]. The process and architecture of data aggregation in LLN networks and IoT is illustrated in Figure 2  
Authors in [20] proposed a three-layer data aggregation infrastructure, called dynamic data aggregation scheme (PDDA) based on priorities for sensor networks because the sensors collect a large amount of redundant data. The proposed PDDA scheme is a hybrid approach that uses clustered and tree-based approaches based on application types. Thus, the proposed PDDA approach achieves energy efficiency and reduces data processing time and overhead at the big data server level. The proposed data aggregation infrastructure has three Layers [21]:  
Figure 1: The IoT five-layer architecture [12; 14]. Figure 2: Data aggregation architecture [11]. * Data aggregation at the level of the sensors-layer 1
* Data aggregation at the base station (BS)-layer 2
* Data aggregation at the big data Server or NoSQL Server-layer 3 server. It should be noted that our proposed method in this paper is at the level of sensor nodes to combine effective data with energy and efficient delay. Therefore, the other two layers mentioned in the PDDA method do not fall within the scope of our paper. The important data aggregation mechanisms, as well as their differences, benefits, and weaknesses, are all described in this section. This section is divided into three categories, including cluster-based, centralized and tree-based aggregation mechanisms for IoT:  
(1) **Cluster-based mechanisms:** Cluster-based networks can reduce the performance load in terms of reducing the aggregate computation and energy consumption of all nodes [22]. In cluster-based mechanisms, the network environment is divided into different clusters while each cluster comprises a number of sensor nodes [23; 24]. In each cluster, one node is selected as the head of all other nodes in a cluster. This will not only decrease the number of transmitted packets, but it also reduces the transmission overhead and bandwidth consumption in transmissions from the cluster to a base station [25]. In [26], a data storage system is presented, which has an efficient functionality in terms of security, scalability, flexibility, and reliability in IoT to be used in the procedure of enormous data analysis. The proposed approach of this research is to present a distributed storage infrastructure providing scalability and reliability in IoT. Multiple data storage in the distributed system provides networks with fault tolerance, reliability, and stability.