The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.

IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.

Chlorophyll-A concentration is one of the most commonly measured water quality parameters. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Laboratory-based methods for measuring chlorophyll-A require expensive instrumentation. In this paper, we proposed a smart, low-cost, and portable smart sensor system to measure the concentration of chlorophyll-A in an extracted solution using two consumer-grade spectral sensors that read the reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll solutions of 51 different concentrations were prepared, and at least 100 readings per sample were taken using our smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-A measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. Our final model resulted in a mean error of ±0.9μg/L when applied on our test set. The total cost was around $150.