## Problem Formulation  
The limitations of currently used WSN technologies for WARM applications are:  
* A satellite-based solution requires considerable power and is very expensive due to image data acquisition and processing. * Satellite-based sensor data acquisition is a high-cost and high-energy solution and therefore, is used only to acquire data from a few locations or those of concern. * Present WSN technology using a short-range wireless link, such as ZigBee or Wi-Fi, requires a large number of sensor nodes for extensive coverage. * A WSN with a long-range wireless link, such as LoRa and Sigfox, may reduce the sensor node count. However, this requires a large number of base stations, as most of the water resources have no or inadequate cellular coverage. This will eventually increase the cost. * A large number of sensor nodes and base stations will require complicated multi-hop data routing. It is not cost effective in terms of energy cost effective or resource requirements, and may induce latency for some wireless technology, such as LoRa. * Finally, the lack of or poor unavailability of cellular data coverage, including the NB-IoT for widely distributed water resources, is a bottleneck for IoT connectivity. Along with the above technical limitations, WARM requires a considerable volume of data acquisition for its distributed resources and a wide range of quality parameters. This requirement is further intensified because of the randomness and fuzziness of acquired data [45], the spatial and temporal variation of the parameters [46], and its dynamics [47]. The WSN for a WARM application, such as WQM, also demands an IoT connectivity to utilize its cloud-based data processing [45] for various reporting and modeling types. Therefore, an IoT-connected WSN is a preferable choice for this type of WARM application. Figure 1a shows the water resource distribution in lower Northern Saskatchewan in Canada with five different zones based on their distribution and cellular network coverage availability. Table 1 summarizes the area covered by each zone with its IoT-WSN size measured by visual estimation and also shows the required number of SNs, the BS, and the highest number of hops using the presently available technology. The dimensions of the zones mentioned here is the measure of length and width. This WSN deployment is done using the short-range link ZigBee, commonly used for various clustered WSNs [1], and the long-range link LoRa, used for wide-area coverage [30]. The short-range link is of 500 m and the long-range link 1 km to 5 km. Figure 1b shows a probable WSN deployment in zone  
1 to monitor almost 80% of the water resources. The rest of the zones can be distributed similarly. There are 16 WSNs in zone 1; each one has at least one BS, and the number of SNs under every BS depends on the range of the wireless link used and the preferable number of hops. IoT connectivity can be implemented using the available cellular data network coverage close to the urban area and long-range point-to-point wireless links for the remote area. From Table 1, it can be seen that the WSN dimension is not directly related to the geographical area but to the distribution of the water resources, the technology used, the availability of wireless connectivity, and the network topology to be used. A large number of SNs increases the network topological complexity, which can be reduced using long-range SNs and a larger number of BSs. However, it still suffers from the network routing complexity, the increased energy requirement for the repeater nodes introducing heterogeneity, and the requirement for various optimization algorithms [48] or a layered protocol [49]. Considering the above limitations and complexity of the available WSN technologies, this paper proposes a lightweight, dynamic, and auto-reconfigurable WSN protocol (LDAP) for the resource and energy-constrained SNs. This LDAP uses a homogeneous network with both static and mobile data sinks instead of static or relocatable base stations [23].