## 6 Future Research Directions  
### Technology  
This section presents the analysis aspect of the Data Acquisition Systems (DAS) that were used to monitor and collect data on water quality in real-time experiments. The three columns in the fourth are used to measure the suitability of DAS, starting with identifying the types of hardware components, i.e., electronic boards, sensors, communication medium, and the technology of data transition to achieve IoT-based implementation. Accordingly, several evaluation criteria are proposed to evaluate the DASs proposed in the literature in terms of cost, complexity, and other criteria [69]. The Evaluation criteria are used with subjective evaluation values to reflect the general aspect of DASs performance and characteristics including physical and software computational based. This section presents articles that used DAS to collect water quality data. Table 1 lists articles with corresponding evaluation values by the assessment criteria. Design and implementation procedures are examined and reviewed subjectively to analyze the cost-effectiveness of currently available DAS in the literature using the following criteria. (1) Design and Programming: These procedures should be performed when sophisticated devices are integrated into the DAS design. Notably, adding more electronic boards/sensors to a DAS system requires more time to program/interface these devices, i.e., not plug and play. (2) Operation and Maintenance: Routine procedures should be performed on a large DAS to ensure that sophisticated sensors/board components are working properly; if they fail, then replacing them is not economical. (3) Equipment Reliability: If the electronic components of DAS are expensive/sophisticated, then the DAS is mostly reliable; otherwise, using unreliable components is not cost-efficient, i.e., off-the-shelf components have low reliability compared with sophisticated ones. (4) Implementation Cost: This criterion refers to the cost of wiring/adjustment and modification performed on a site location to house DAS components. These modifications increase implementation costs, and DAS which requires a low level of maintenance on the location site must be designed. In the end, cost-efficiency is evaluated subjectively depending on the DAS design layout and provided features. (5) Total Equipment cost: This criterion indicates/describes the level of cost-efficiency of the currently proposed/implemented DAS. For example, if DAS consists of NB-IoT, Wi-Fi, and UAV with 15 or 20 sensors, then the system exhibits a very high cost. All the assessment criteria are subjectively evaluated in five scales (very low, low, medium, high, and very high). (6) DAS Size: This criterion describes the size of the architecture of the proposed DAS in the literature. The assessment value considers the number of electronic elements of DAS. (7) Power Consumption: This criterion estimates the power level required by DAS to fully operate. (8) Latency: This criterion is the time between cause and effect, i.e., the time that elapses when DAS starts measuring the environment until the outcome is delivered to the user. (9) Information Size: This criterion represents the amount of raw data that DAS can collect. (10) Information Diversity: This criterion represents the variety of raw data that DAS can collect. (11) Computational Complexity: This criterion represents the difficulty in processing the amount of collected raw data, i.e., the higher number of boards and sensors can cause this criterion to go high. (12) DAS Complexity Level: This criterion assesses the overall level of complication in the current proposed/implemented DAS in the literature based on the preceding criteria. For example, if the DAS system consists of an Intel board MCU (FPGA), hardware-based computational algorithm, a high number of sensors, and more than one MCU, then the complexity level is very high. DAS complexity increases as the number of components increases.