# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 1 Introduction  
IoT is an emerging technology in Information and Technology (IT) [1]. It is a collection of internet-connected embedded devices that are capable of sensing and transmitting data from one location to another location without human support [2]. The name IoT indicates that things are connected tothe internet via communication technologies such as wireless sensor networks, near field communication, radio frequency identification, and Bluetooth [3]. IoT provides a lot of potential benefits in all aspects of human life [4]. IoT applications include smart homes, industrial Internet, smart supply chains, smart cities, wearables, smart grids, connected health, connected cars, smart farming, smart retail, etc. [5]. LLN is a type of IoT network which consists of routers and nodes restricted by resources [6]. The Routing Protocol for Low Power and Lossy Networks (RPL) has been standardized by IETF [7]. RPL forms the Destination Oriented Directed Acyclic Graph (DODAG) to transfer the data from the source to the Sink. The DODAG consists of a DODAG root and DODAG nodes. The top of a DODAG node is called DODAG's root and the rest of the nodes are called DODAG nodes. The direction of the participant node towards the root of DODAG is called up routing, and vice versa is called down routing [8]. It supports traffic patterns, including point to multipoint, multipoint to point, and point to point [9]. The participant node chooses the best parent based on the objective function, which can decide on the application requirements [10]. Energy is a scarce resource in IoT. Therefore, energy conservation is a significant challenge in IoT [11]. Many techniques are proposed to reduce energy consumption in IoT. Energy-aware routing, sleeping mechanisms, and congestion-aware routing and data aggregation are the available techniques in IoT. In energy-aware routing, the existing protocols are energy-aware solar routing, node reliance techniques, multi-hop hierarchical clustering, duty cycling, and data-driven approaches to extend the network lifetime [12]. Data aggregation is a process of collecting the data and aggregates it from the sensor's node. It is one of the essential processes to remove redundant data and save energy [13]. The primary objective of the data aggregation is to collect and aggregate the data. Also, it can be extended the network lifetime [14]. These techniques are suitable for some applications like temperature monitoring and gas leakage monitoring [15]. The existing data aggregation techniques are clustering and region-based routing to reduce duplicate data transmission across the network [16]. In IoT Routing, we need a suitable technique to aggregate the data among the network nodes. Hence, the Compressed Sensing (CS) theory is adopted in IoT, which is widely used in Wireless Sensor Networks (WSN) [17]. CS is a new theory that provides compression, coding, and decoding to reduce storage [18]. The applications for CS theory include data networks, digital images and video cameras, sensor networks, etc. CS is a beneficial technique to improve the performance of IoT. Also, it can be used for signal detection and processing, channel estimation, etc. [19]. The contribution of the proposed EDADA-RPL is to distribute the data and perform the single-hop data aggregation. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in each parent node to aggregate the data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the DODAG root. The primary highlights of the proposed Energy and Delay Aware Data Aggregation in RPL (EDADA-RPL) are the following:  
* The EDADA-RPL's primary goal is to prevent redundant data transmission from the source to the root of DODAG. * The CS theory-based data aggregation avoids redundant data transmission and prolongs the network lifetime.