Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT)

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

Energy-aware Approaches for Energy Harvesting Powered Wireless Sensor Systems

Energy harvesting (EH) powered wireless sensor systems (WSSs) are gaining increasing popularity since they enable the system to be self-powering, long-lasting, almost maintenance-free, and environmentally friendly. However, the mismatch between energy generated by harvesters and energy demanded by WSS to perform the required tasks is always a bottleneck as the ambient environmental energy is limited, and the WSS is power hunger. Therefore, the thesis has proposed, designed, implemented, and tested the energy-aware approaches for wireless sensor motes (WSMs) and wireless sensor networks (WSNs), including hardware energy-aware interface (EAI), software EAI, sensing EAI and network energy-aware approaches to address this mismatch. The main contributions of this thesis to the research community are designing the energy-aware approaches for EH Powered WSMs and WSNs which enables a >30 times reduction in sleep power consumption of WSNs for successful EH powering WSNs without a start-up issue in the condition of mismatch between the energy generated by harvesters and energy demanded by WSSs in both mote and network systems. For EH powered WSM systems, the energy-aware approaches have (1) enabled the harvested energy to be accumulated in energy storage devices to deal with the mismatch for the operation of the WSMs without the start-up issue, (2) enabled a commercial available WSMs with a reduced sleep current from 28.3 μA to 0.95 μA for the developed WSM, (3) thus enabled the WSM operations for a long active time of about 1.15 s in every 7.79 s to sample and transmit a large number of data (e.g., 388 bytes), rather than a few ten milliseconds and a few bytes. For EH powered WSN systems, on top of energy-aware approached for EH powered WSM, the network energy-aware approaches have presented additional capabilities for network joining process for energy-saving and enabled EH powered WSNs. Once the EH powered WSM with the network energy-aware approach is powered up and began the network joining process, energy, as an example of 48.23 mJ for a tested case, has been saved in the case of the attempt to join the network unsuccessfully. Once the EH-WSM has joined the network successfully, the smart programme applications that incorporate the software EAI, sensing EAI and hardware EAI allow the EH powered WSM to achieve (4) asynchronous operation or (5) synchronised operation based on the energy available after the WSM has joined the network.Through designs, implementations, and analyses, it has been shown that the developed energy-aware approaches have provided an enabled capability for EH successfully powering WSS technologies in the condition of energy mismatch, and it has the potential to be used for wide industrial applications

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

Wireless sensor networks, actuation, and signal processing for apiculture

Recent United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. Protecting the honey bee population worldwide, and enabling them to maximise productivity, are important concerns. This research proposes a framework for addressing these issues by considering an inter-disciplinary approach, combining recent developments in engineering and honey bee science. The primary motivation of the work outlined in this thesis was to use embedded systems technology to improve honey bee health by developing state of the art in-hive monitoring systems to classify the colony status and mechanisms to influence hive conditions. Specific objectives were identified as steps to achieve this goal: to use Wireless Sensor networks (WSN) technology to monitor a honey bee colony in the hive and collect key information; to use collected data and resulting insights to propose mechanisms to influence hive conditions; to use the collected data to inform the design of signal processing and machine learning techniques to characterise and classify the colony status; and to investigate the use of high volume data sensors in understanding specific conditions of the hive, and methods for integration of these sensors into the low-power and low-data rate WSN framework. It was found that automated, unobtrusive measurement of the in-hive conditions could provide valuable insight into the activities and conditions of honey bee colonies. A heterogeneous sensor network was deployed that monitored the conditions within hives. Data were collected periodically, showing changes in colony behaviour over time. The key parameters measured were: CO2, O2, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were collected to provide an additional analysis dimension. Extensive energy improvements reduced the node’s current draw to 150 µA. Combined with an external solar panel, self-sustainable operation was achieved. 3,435 unique data sets were collected from five test-bed hives over 513 days during all four seasons. Temperature was identified as a vital parameter influencing the productivity and health of the colony. It was proposed to develop a method of maintaining the hive temperature in the ideal range through effective ventilation and airflow control which allow the bees involved in the activities above to engage in other tasks. An actuator was designed as part of the hive monitoring WSN to control the airflow within the hive. Using this mechanism, an effective Wireless Sensor and Actuator Network (WSAN) with Proportional Integral Derivative (PID) based temperature control was implemented. This system reached an effective set point temperature within 7 minutes of initialisation, and with steady state being reached by minute 18. There was negligible steady state error (0.0047%) and overshoot of <0.25 °C. It was proposed to develop and evaluate machine learning solutions to use the collected data to classify and describe the hive. The results of these classifications would be far more meaningful to the end user (beekeeper). Using a data set from a field deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. A correlation between meteorological conditions and beehive data was also observed. This led to the development of an algorithm for predicting short term rain (within 6 hours) based on the parameters within the hive (95.4% accuracy). A Random Forest based classifier was also developed using the entire collected in-hive dataset. This algorithm did not need access to data from outside the network, memory of previous measured data, and used only four inputs, while achieving an accuracy of 93.5%. Sound, weight, and visual inspection were identified as key methods of identifying the health and condition of the colony. Applications of advanced sensor methods in these areas for beekeeping were investigated. A low energy acoustic wake up sensor node for detecting the signs of an imminent swarming event was designed. Over 60 GB of sound data were collected from the test-bed hives, and analysed to provide a sound profile for development of a more advanced acoustic wake up and classification circuit. A weight measuring node was designed using a high precision (24-bit) analogue to digital converter with high sensitivity load cells to measure the weight of a hive to an accuracy of 10g over a 50 kg range. A preliminary investigation of applications for thermal and infrared imaging sensors in beekeeping was also undertaken

Quality of service optimization in solar cells-based energy harvesting wireless sensor networks

In energy harvesting wireless sensor networks, the sensors are able to harvest energy from the environment to recharge their batteries and thus prolong indefinitely their activities. Widely used energy harvesting systems are based on solar cells, which are predictable (i.e., their energy production can be predicted in advance). However, since the energy production of solar cells is not constant during the day, and it is null at night time, these systems require algorithms able to balance the energy consumption and production of the sensors. In this framework, we approach the design of a scheduling algorithm for the sensors that selects among a set of available tasks for the sensors (each assigned with a given quality of service), in order to keeping the sensors energy neutral, i.e., the energy produced during a day exceeds the energy consumed in the same time frame, while improving the overall quality of service. The algorithm solves an optimization problem by using a greedy approach that can be easily implemented on low-power sensors. The simulation results demonstrate that our approach is able to improve the quality of the overall scheduling plan of all networked sensors and that it actually maintains them energy neutral

Ultra-Low Power and Non-intrusive Wireless Monitoring for Smart Buildings

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances