# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 1 Introduction  
Innovative sensor modules, next-generation wireless networks and modern cloud services are the enabling tools, headed for the realisation of the large scale Internet of Things (IoT) applications [1]. IoT devices having sensing abilities, enable us to interact with the environment to collect data and send them to the cloud through communication networks. The number of such sensing modules that have to be connected to the Internet is increasing at rapid speed and is expected to be 500 billion by 2030 [2]. Traditional sensor network standards and technologies are not expected to sufficiently furnish the connectivity for abundant sensing and interaction with the physical world, thus undermining the development of innovative IoT applications on large scale. The rapid growth of modern technologies in communication, computation and sensing platforms is enabling a wide range of IoT sensing applications providing services in an overly connected way [3]. The energy demands of IoT networks are increasing because of the steadily multiplying number of participating sensor devices and a wide range of modern applications [4]. According to an estimation, 12.3 billion mobile devices are expected to be connectedby 2022, which also includes the IoT devices using machine-to-machine communication [5]. Therefore, for these battery operated IoT platforms, efficient energy utilisation is a crucial factor for an extended network life-time, without replacement of the devices or recharging of the batteries. Data communication is the most energy consuming operation for constrained sensor modules and it can become a bottleneck, especially in IoT sensing applications that generate continuous or plenty of data. Examples of such applications include dense IoT networks and sensor networks with multimedia applications. Such applications use Internet-connected wireless sensor networks with dense deployments of devices in the field or send multimedia (audio, video and images) data from the physical environment. A report by Cisco on visual networking says that during 2015-2016, video monitoring through Internet connectivity increased by 72%, with data sending per month increasing from 516 Petabyte (PB) to 883 PB [6]. These IoT sensing networks provide very useful services in applications such as disaster management, industrial automation, military surveillance, monitoring services in home, agriculture and wildlife, elderly as well as general healthcare and safety. Figure 1 shows a layered architecture of the IoT-based system with typical scenarios for sensing applications. Despite having significant use cases of sensor network-based IoT applications, there are many technical challenges in the practical realisation of their deployments on large scale. These challenges mostly narrow down towards the constrained nature of IoT devices in terms of energy, causing transmission delays and information fidelity. Energy is considered the scarc  
Figure 1: Layered architecture of IoT-based system with typical application scenarios for sensor networks. Sensor Networks (WSN) and many different approaches can be found in the literature for efficient utilisation of node energy, to maximise individual nodes' as well as overall network lifetime. Wireless sensor nodes are usually deployed on a large scale having complex network topology and participating devices mostly are battery operated. This is because large scale wired infrastructure for continuous power supply and connectivity to WSN nodes is not a viable option [7; 8]. Similarly, delivery of multimedia content (audio/video) in sensor networks, presents new distinct design challenges, and for which specialised Wireless Multimedia Sensor Networks (WMSN) are designed. Energy conservation is even more challenging in WMSN, because data are complex and large in size, whereas nodes are battery powered mostly [8; 9]. Excessive traffic may be injected into the network in such application cases, which is not sustainable in constrained IoT networks.