## 1 Introduction  
Remote monitoring of various environmental parameters and resources is a major area of interest in the planning of future initiatives and control methods. The Internet of Things (IoT) and wireless technologies make it simple to accommodate a large number of sensing devices and gradually extend their coverage over the monitoring area. The IoT is the extension of the internet from our personal computers and smartphones to various types of devices, ranging from wristwatches to large industrial machines. It facilitates the flow of information from these devices to the end-user through the internet for monitoring and controlling purposes. Wireless communication plays a primary role in connecting most edge devices with the IoT network, and the Wireless Sensor Network (WSN) connects sensorsin a network to collect various physical parameters of different systems and accumulate them in central storage. A traditional WSN connects the sensor nodes (SNs) in a clustered network [1] with static network topologies using multiple base stations (BSs) or a data sink (DS), and transfers the sensor data using multiple repeater nodes [2] to increase the monitoring coverage area. This type of WSN is used mainly for densely populated sensor networks and is not suitable for wide-area remote monitoring (WARM) applications, where both temporal and spatial data is acquired in real time. The clustering procedure in WSNs uses various optimization methods [3] to improve the energy requirement and WSN lifetime. This clustering process may not be suitable for the battery-powered and resource-constraint nodes that are used in remote locations with less accessibility. A WSN also uses multiple special nodes as a hop that retransmits the sensor data to the BS in order to increase the WSN coverage, and also to distribute the energy load over the SNs. In doing so, a WSN implements complex routing algorithms which generate extra processing loads for the repeater nodes and the SNs, and hence, energy consumption is increased. Many WARM applications, such as environmental, agricultural, and water quality monitoring (WQM), where the resources are geographically spread over a wide area, have little or no cellular data coverage, and face the challenge of connecting with the IoT network. Present WARM applications are implemented using (a) point-to-point or point-to-multi-point connectivity, mainly through satellite links [4], (b) multi-hop wireless communication for long-distance connectivity with the BS, and (c) vehicular mobile SNs [5]. Each of these implementations has limitations in terms of cost, energy consumption, and real-time data acquisition. Water quality monitoring (WQM) is chosen in this work as a useful case for a WARM application. Water resources are widely distributed over the globe and requires both spatial and temporal monitoring. Eighty percent of our daily consumption comes from surface water [6], of which less than 3% is usable (97.2% of surface water is ocean water and is highly saline). Usable surface water mainly comes from glaciers, rivers, and lakes. A natural solvent, surface water carries contaminating substances while traveling on the ground surface and underground. Through the hydrologic cycle, where surface water travels through the air and comes back again, it also becomes contaminated by air pollution from urbanization [7] and other environmental factors [8; 9]. WQM also helps the planning and management of the restoration of water resources and transboundary water distribution [10]. Long-term WQM facilitates the study of ecological changes [11], agricultural conservation practices [12], and long-term changes in water quality parameters. Present WARM is mainly performed by satellite imaging [13], which combines in situ data acquisition with the GIS (Geographic Information System) [14]. On-site digital-color [15], unmanned aerial vehicle (UAV)-based hyperspectral [16], multispectral, thermal, and optical imaging [17] are also used.