Over the last several decades the Salton Sea region has been negatively impacted by climate, agricultural practices, and reduced water flow [1
]. This can be seen in the decline of the lake’s water level and increase in salinity. The reduced water levels expose new lakebed. The composition of particles in this newly exposed lakebed, known as the playa, is strongly tied to the ambient particulate matter composition [2
]. Wind erosion acting on this playa aids in the production of hazardous fine dust pollutants [1
]. This imposes a potential health hazard for those in the nearby regions by increasing their risk and exposure to deadly diseases like West Nile Virus, chronic obstructive pulmonary disease, emphysema, and cancer [3
]. These health risks are not just a result of the environment but also a failure to act. This lack of action has grounds to constitute as state-corporate crime [4
] and thus it is in the best interest of multiple parties to combat the degradation of this salt lake environment. Monitoring this environment lays the foundation for further studies on the change that is taking place.
A system that has the potential to monitor these changes would need to withstand extremes up 50 °C, 90% humidity, water surface temperature up to 33 °C and as low as 14 °C [5
]. Constant monitoring in these conditions along with the remote location are impractical for live human subject. In contrast, many commercial off the shelf (COTS) components are rated to operate within those parameters. Popular COTS sensors are commonly built around Arduino based board to generate a proof of concept [6
]. The goal of this system will be to maximize the number of data points collected on a finite amount of stored battery energy. The implementation is considered successful if the composite system power consumption can be minimized without sacrificing system utility.
In this section several systems that monitor environmental conditions and quality via various sensors are shown. Followed up environmental monitoring conditions that utilize energy harvesting and power controlling techniques to prolong the operating lifetime of remote, finite energy sensor systems [7
A system designed to monitor water quality and fish behavior has been introduced by Parra et al. [7
]. Through this system a cost-effective way to monitor an aquatic environment is shown. Arduino based microcontroller and commercial sensors are showcased to effectively monitor select water, tank, and feeding parameter. The architecture discussed deals with reducing energy consumption in the wireless sensor network. A technique to further reduce energy consumption for an aquatic system is shown by Hu [8
]. This technique factors in the measured conditions of the environment to determine an appropriate sampling rate. The sampling interval is dynamic and show potential to save energy compared to a fixed rate sampling system. Hu elaborates that the energy savings brought about by reducing sample quantity outweighs the required of additional energy cost to update the sampling rate dynamically. Since environmental sensor systems may be deployed in off-the-grid location and not easily accessible location, they would need to operate on finite amounts of energy without human support. Many energy harvesting techniques available for such systems are discussed by Prauzek at al [9
] and differentiates the difference between ambient energy and externally emitted energy sources. Groundwork is laid for environmental wireless sensor networks to utilize existing energy in their deployed location to enhance their energy sustainability. Various storage devices are explored as well and energy harvesting-storage topologies are highlighted. The environment and the limited energy challenge are also addressed by Engmann et al. [12
] and they call for the use of efficient energy management and energy harvesting. The techniques analyzed by Engmann et al. expand across wireless sensor networks and seek to prolong the lifetime of the energy hungry nodes. Simulations to achieve efficient energy consumption are discussed for multiple layers of the network. Techniques to harvest solar energy, vibration energy, and radio energy from the environment are highlighted. Storage techniques and wireless transmission of energy is also proposed to distribute energy across devices in the network. The papers discussed so far have introduced excellent ways to manage energy for wireless sensor networks, harvest energy for the wireless sensor networks and monitor the environment with such network. However, a lower level analysis is required as well. Techniques shown by Prauzek at al [10
] can improve energy harvesting by adding an awareness of stored energy via observing the state of charge and considering future available energy for harvesting. This is done by having an adaptive, energy harvesting-aware sensing strategy developed by the differential evolution of fuzzy controller. A low-cost Internet of Things (IoT) based weather monitoring system is proposed by Rao et al. [13
] to monitor specific environment parameters, execute analysis on collected data, and making the data available to the end user via Wi-Fi protocol. The Arduino based microcontroller (MCU) hosts 4-tier architectural model to pull temperature, humidity, light intensity, and CO levels from the environment. Another Arduino based embedded system is proposed by Djajadi and Wijanarko [11
]. This system monitors the environment for establishing a measurement of quality and sustainability for human life. The paper delves down into the sensor level of monitoring in order to develop a customizable system capable of hosting multiple sensor device configurations specific to the user’s intended need. Low Power Energy Aware Processing (LEAP) has been introduced by McIntire, Ho, Yip, Singh, Wu, and Kaiser as a combined hardware-software architecture to incorporate power awareness into task scheduling. Awareness is implemented by monitoring energy dissipation of the sensors and subsystem and scheduling the tasks between multiple processors to control power consumption [14
The environmental monitor shown by Parra et al. is a good representation of the size, scope, and power management of our system, we would like to incorporate similar smart energy saving algorithms to more aspects than the sending of information. We built a system similar in intend and seek to add sensor power management and data security. Prauzek et al. energy harvesting sources, storage devices, and system topologies lay excellent groundwork for building a deployable environmental monitoring system. This paper will attempt to incorporate the intent of this paper into a physical embedded system. Hu’s techniques to control sampling intervals to save energy are useful for our system. We expand from this by applying various device on and off time to enhance the energy efficiency of our entire monitoring system. Instead of using measured environment as a factor to control sampling rate, energy storage will be focused upon. Prauzek, Kromer, Rodway, and Musilek’s introduced method of differential evolution of fuzzy controller is complex for or intended scope. However, we build an architecture that pulls from this concept by creating a forward-thinking scheduling logic with respect to energy and energy harvesting. Techniques by Engmann et al. shows excellent potential for energy management. The analysis was done on a network of nodes. We seek to incorporate these concepts to a single node and attached peripherals. The systems proposed by Rao et al. and Djajadi and Wijanarko provide excellent representation in scope, cost, and intent of our project. We will seek to incorporate technique introduced by the previous authors on our system as well as add a data security feature that would help secure the IoT enabled system.
Our introduced method builds upon the above works by approximating the stored energy remaining and energy harvesting availability. The approximations are inputs to a decision-making algorithm that will control system software and hardware features. This method will contrast LEAP architecture by utilizing a single processor on a single board. The solitary unit will be built around the Cortex-M4F powered Arduino-based Teensy 3.5 as it contains an integral SD card slot to accommodate data storage as well as plenty I/O pins for a multitude of sensor. The soft side of this architecture will continue with the LEAP concept and add status checks to energy storage and energy harvesting prior to task scheduling and incorporate these finding into the scheduling decision making.
Lastly, security of the system is considered. Specifically, the security of the collected data. We tested the impact of Cipher Block Chained (CBC) 128-bit Advanced Encryption Standard (AES) with Base64 encoding has on system power. AES encryption was selected as it has been shown to be successfully implemented on an Arduino-based board and had desirable characteristics such as low memory usage and an efficient use of time [15
]. This allows an Arduino based system to add encryption and decryption without absorbing large amounts of memory that is crucial for the primary functions. The efficient use of time ensure that encryption also does not occupy a significant amount of time in place of the systems primary function as well.
In this paper we will introduce the COTS hardware that comprises our system and provide power analysis of these hardware. Then select system software features and their power consumptions are discussed as well. A logic architect is then proposed to manage the power of the overall system. Simulation results are then analyzed to compare the effectiveness of this power management. The implication of encryption is discussed last as an option for data security. Finally, conclusions are drawn based on the findings.