Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs

An Integrated Software and Hardware Architecture for Gravity-driven and Remotely-operated Water Release

The present study proposes an integrated real-time hardware and software architecture for gravity-driven and remotely-operated water release from storage units such as detention ponds, reservoirs, and wetlands using a siphon and conventional drainage pipe system. The hardware involves a communication component, a control function component, and a hydraulics component. The communication component consists of a Virtual Private Network (VPN) router that collects the Programmable Logic Controller (PLC) data and sends it to the remote center. The hydraulics hardware consists of an actuated butterfly valve, water level sensor, bilge pump, and air vent. Multiple flow release tests were carried out at the FIU Engineering Center to test the functionality and operational capability of the proposed system. The cost analysis for a 6 diameter siphon and conventional drainage pipe showed that they both are significantly less expensive than the widely used Supervisory Control and Data Acquisition (SCADA) system. The proposed low-cost systems can open new avenues for water management. Another proposition of this study is a derivation of an analytical equation for calculating the time-varying flow releases for both types of drainage. The validity of these equations was confirmed using the experimental flow release tests

Field Deployment and Integration of Wireless Communication & Operation Support System for the Landscape Irrigation Runoff Mitigation System

The study of water conservation technologies is critically important due to the rapid growth in urban population leading to a shortage in potable water supplies throughout the world. Current water supplies are not expected to meet the water demand in the coming decades; this could seriously affect human lives and socio-economic stability. About 30 percent of the current municipal supplies are being used for outdoor irrigation such as gardening and landscaping. These numbers are increasing due to the increase in urban population. Due to the current inefficient or improper landscape irrigation practices, substantial amounts of water are lost in the form of runoff or due to evaporation. Runoff occurs when the irrigation precipitation rate exceeds the infiltration rate of the soil, which depends on the soil and site characteristics such as soil type and the slope of the site. Runoff being an obvious water wastage, it also poses a great problem to the environment with its potential for transporting fertilizers and pesticides into storm sewers and, eventually, surface waters. Thus, this study focuses on designing a smart operational support system for landscape irrigation that has the potential to reduce runoff and also decrease water losses in the form of evaporation. The system consists of two main units, the landscape irrigation runoff mitigation system (LIRMS) and an operational support system (OSS). The combined system is referred to as the second-generation LIRMS. The LIRMS is installed at the border of a field/lawn. The LIRMS consists of a central controller unit and a runoff sensor. Based on the feedback from the runoff sensor, the controller unit pauses and resumes irrigation as needed in order to reduce runoff. The main purpose of OSS is to automate the scheduling of the irrigation process. A multilayer perceptron based OSS was designed and implemented on a dedicated web-server. The OSS processes historical irrigation data and the environmental/weather data to choose an optimal schedule to irrigate on a given day. The OSS aims to reduce irrigation water losses due to natural environmental factors such as evaporation and rain. A wireless communication link is established between LIRMS and OSS for monitoring and analyzing irrigation events. The second-generation LIRMS was installed in the Texas A&M Turfgrass Research Field Laboratory, College Station, TX for performing irrigation tests. The preliminary results show that the average soil wetting efficiency has increased with the use of the operational support system when compared to previous tests performed without the operational support system. Also the results suggest that the second generation LIRMS has comparable runoff reductions when compared to the first-generation LIRMS. Yet, more tests are required to quantify the overall water savings

Technology development of electric vehicles: A review

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Power Beacon’s deployment optimization for wirelessly powering massive Internet of Things networks

Abstract. The fifth-generation (5G) and beyond wireless cellular networks promise the native support to, among other use cases, the so-called Internet of Things (IoT). Different from human-based cellular services, IoT networks implement a novel vision where ordinary machines possess the ability to autonomously sense, actuate, compute, and communicate throughout the Internet. However, as the number of connected devices grows larger, an urgent demand for energy-efficient communication technologies arises. A key challenge related to IoT devices is that their very small form factor allows them to carry just a tiny battery that might not be even possible to replace due to installation conditions, or too costly in terms of maintenance because of the massiveness of the network. This issue limits the lifetime of the network and compromises its reliability. Wireless energy transfer (WET) has emerged as a potential candidate to replenish sensors’ batteries or to sustain the operation of battery-free devices, as it provides a controllable source of energy over-the-air. Therefore, WET eliminates the need for regular maintenance, allows sensors’ form factor reduction, and reduces the battery disposal that contributes to the environment pollution. In this thesis, we review some WET-enabled scenarios and state-of-the-art techniques for implementing WET in IoT networks. In particular, we focus our attention on the deployment optimization of the so-called power beacons (PBs), which are the energy transmitters for charging a massive IoT deployment subject to a network-wide probabilistic energy outage constraint. We assume that IoT sensors’ positions are unknown at the PBs, and hence we maximize the average incident power on the worst network location. We propose a linear-time complexity algorithm for optimizing the PBs’ positions that outperforms benchmark methods in terms of minimum average incident power and computation time. Then, we also present some insights on the maximum coverage area under certain propagation conditions

In Review Magazine / January 2012

In Review magazine, published quarterly, created to showcase the best of NPS

Energy management techniques for ultra-small bio-medical implants

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 167-174).Trends in the medical industry have created a growing demand for implantable medical devices. In particular, the need to provide medical professionals a means to continuously monitor bio-markers over long time scales with increased precision is paramount to efficient healthcare. To make medical implants more attractive, there is a need to reduce their size and power consumption. Small medical implants would allow for less invasive procedures and greater comfort for patients. The two primary limitations to the size of small medical implants are the batteries that provide energy to circuit and sensor components, and the antennas that enable wireless communication to terminals outside of the body. In this work we present energy management and low-power techniques to help solve the engineering challenges posed by using ultracapacitors for energy storage. A major problem with using any capacitor as an energy source is the fact that its voltage drops rapidly with decreasing charge. This leaves the circuit to cope with a large supply variation and can lead to energy being left on the capacitor when its voltage gets too low to supply a sufficient supply voltage for operation. Rather than use a single ultracapacitor, we demonstrate higher energy utilization by splitting a single capacitor into an array of capacitors that are progressively reconfigured as energy is drawn out. An energy management IC fabricated in 180-nm CMOS implements a stacking procedure that allows for more than 98% of the initial energy stored in the ultracapacitors to be removed before the output voltage drops unsuitably low for circuit operation. The second part of this work develops techniques for wide-input-range energy management. The first chip implementing stacking suffered an efficiency penalty by using a switchedcapacitor voltage regulator with only a single conversion ratio. In a second implementation, we introduce a better solution that preserves efficiency performance by using a multiple conversion ratio switched-capacitor voltage regulator. At any given input voltage from an ultracapcitor array, the switched-capacitor voltage regulator is configured to maximize efficiency. Fabricated in a 180-nm CMOS process, the chip achieves a peak efficiency of 90% and the efficiency does not fall below 70% for input voltages between 1.25 and 3 V.by William R. Sanchez.Ph.D

A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks

The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, called S-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer