# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 1 Introduction
As a consequence, the network can become congested, which results in high latency and packet losses. This becomes a massive source of energy consumption/wastage, especially due to lost packets which were transmitted and partially traveled on their multi-hop path. IoT devices have to consume additional energy if those packets may need re-transmissions for information fidelity in reliable application or due to critical nature of the payload. The commonly used approach in the literature for energy management is radio duty cycling, that is enabling the IoT devices to turn off the transceiver during idle periods, having no data transmitting or receiving [10]. However, in IoT sensing applications such as those discussed above, data communication is to be continuous and/or with many packets [7]. Therefore, duty cycling opportunities are expected to be very minimal, making this approach less functional and not helpful for energy saving. In the light of duty cycling being less effective in such IoT applications, controlling the excessive traffic generation becomes a key factor for achieving significant energy conservation. Traffic management of the network can be done through controlling data sending rate and number of round trips in the form of number of packets. Thus, efficient data management becomes crucial for longer operating times of IoT devices as well as the lifetime of the network. The channel contention is also increased when the data flows towards the neighbourhood of the gateway node. This issue can be better seen in the example scenario presented in Figure 2. In such circumstances, the nodes which are close to the gateway node are particularly under severe pressure. In commonly used sensor network architectures, this kind of pressure is because of the extra contention near a single sink, due to multi-path data forwarding to it. This phenomenon is also commonly referred to as the energy-wave problem, as shown in Figure 2. Nodes shown in red colour are under heavy pressure and their residual energy can quickly deplete due to excessive forwarding on behalf of other nodes. Therefore, these nodes are critical in the IoT sensing applications and solutions like proposed in this paper are needed to prolong their lifetime by avoiding unnecessary forwarding, as otherwise the network would soon be disconnected. The contributions of the study presented in this paper for efficient energy consumption and data management in IoT sensing applications are as follow:  
1. We proposed a scheme based on interfacing efficient web framework (GraphQL) and cloud services with constrained application protocol. 2. We implemented a testbed involving real IoT devices for evaluation of the proposed approach in the target applications. The rest of the paper is organised as follows. Section 2 is about related work. Section 3 presents possible architectures for cloud-based IoT sensing applications considered in this research work. The proposed approach and its implemented components are presented in Section 4. Setting up of the experimental testbed is explained in Section 5. Results and discussions are included in Section 6. The energy-wave problem alternate resolution is in Section 7. Section 8 concludes the work.