Energy Harvesting Autonomous Sensor Systems

Design, Analysis, and Practical Implementation

Yen Kheng Tan

• Publisher: CRC Press
• Year: 2017
• ISBN:
  9781351832564
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Energy Harvesting Autonomous Sensor Systems: Design, Analysis, and Practical Implementation provides a wide range of coverage of various energy harvesting techniques to enable the development of a truly self-autonomous and sustainable energy harvesting wireless sensor network (EH-WSN). It supplies a practical overview of the entire EH-WSN system from energy source all the way to energy usage by wireless sensor nodes/network.

After an in-depth review of existing energy harvesting research thus far, the book focuses on:

• 
  
  

• Outlines two wind energy harvesting (WEH) approaches, one using a wind turbine generator and one a piezoelectric wind energy harvester
• Covers thermal energy harvesting (TEH) from ambient heat sources with low temperature differences
• Presents two types of piezoelectric-based vibration energy harvesting systems to harvest impact or impulse forces from a human pressing a button or switch action
• Examines hybrid energy harvesting approaches that augment the reliability of the wireless sensor node's operation
• Discusses a hybrid wind and solar energy harvesting scheme to simultaneously use both energy sources and therefore extend the lifetime of the wireless sensor node
• Explores a hybrid of indoor ambient light and TEH scheme that uses only one power management circuit to condition the combined output power harvested from both energy sources

Although the author focuses on small-scale energy harvesting, the systems discussed can be upsized to large-scale renewable energy harvesting systems. The book goes beyond theory to explore practical applications that not only solve real-life energy issues but pave the way for future work in this area.

1

Introduction

The rapid growth in demand for computing everywhere has made the computer a pivotal component of human’s daily lives [2]. Whether we use the computers to gather information from the Web, for entertainment, or for running a business, computers are noticeably becoming more widespread, mobile, and smaller in size. What we often overlook is the presence of those billions of small pervasive computing devices around us that provide intelligence integrated into the real world smart environments [2] to help us solve some crucial problems in the activities of our daily lives. While we were asleep at the switch learning to “plus one” on Google, the Internet of Things (IOT) just exceeded the number of people that reside on the planet. Beyond just smartphones and tablets, the number of “things” that connect to the Internet will only continue to scale as the growing number of connected gizmos and appliances—and even cows—are coded and catalogued to send messages to the Web. Dave Evans at Cisco noted that “there are more devices tapping into the Internet than people on Earth to use them.” How is that possible? Well, an infographic the firm just published, as shown in Figure 1.1, provides us insight with a visual snapshot of the increase in things connected to the Internet—and how they will serve us in the very foreseeable future.

By the year 2020, as can be seen in Figure 1.1, there will be 50 billion of these things around us. To achieve this vision of the smart environment with pervasive computing, also known as ubiquitous computing, many such miniaturized computing devices will be integrated in everyday objects and activities to enable better human-computer interaction. These computational devices, which are generally equipped with sensing, processing, and communicating abilities, are known as wireless sensor nodes. When these wireless sensor nodes are connected, they form a network called the wireless sensor network (WSN), as illustrated in Figure 1.2.

1.1 Motivation of Wireless Sensor Networks (WSNs)

The postmodern era is a world where everything including people is connected like the illustration given in Figure 1.3. With surrounding close to invisibly small smart computing devices and sensors embedded in everyday ambient objects, environments are able to recognize and respond to the presence and behaviour of any individual in a personalized and relevant way. With the recent advances in wireless communication technologies, sensors and actuators, and highly integrated microelectronics technologies, WSNs have gained worldwide attention to facilitate monitoring and controlling of physical environments from remote locations that could be difficult or dangerous to reach. In the Massachusetts Institute of Technology (MIT) Technology Review magazine of innovation published in February 2003 [3], the editors identified WSNs as the first of the top 10 emerging technologies that will change the world.

FIGURE 1.1
Internet of Things exceeds the Internet of People.

FIGURE 1.2
Overlap between WSN and IOT. (RFID: radio-frequency identification.)

FIGURE 1.3
When everything connects.

Across many industries, products and practices are being transformed by these networked communicating sensors and computing intelligence. The smart industrial gear includes jet engines, bridges, and oil rigs that alert their human minders when they need repairs, before equipment failures occur. Computers track sensor data on operating performance of a jet engine or slight structural changes in an oil rig, looking for telltale patterns that signal coming trouble. Sensors on fruit and vegetable cartons can track location and sniff the produce, warning in advance of spoilage so shipments can be rerouted or rescheduled. Computers pull GPS data from railway locomotives, taking into account the weight and length of trains, the terrain, and turns to reduce unnecessary braking and curb fuel consumption by up to 10%.

1.1.1 Architecture of WSNs

WSNs represent a significant improvement over wired sensor networks with the elimination of the hardwired communication cables and associated installation and maintenance costs. An overview of these network systems is illustrated in Figure 1.4. The architecture of a WSN typically consists of multiple pervasive sensor nodes, sink, public networks, manager nodes, and end user [4]. Many tiny, smart, and inexpensive sensor nodes are scattered in the targeted sensor field to collect data and route the useful information back to the end user. These sensor nodes cooperate with each other via a wireless connection to form a network and collect, disseminate, and analyze data coming from the environment. To ensure full connectivity, fault tolerance, and a long operational life, WSNs are deployed in an ad hoc manner, and the networks use multihop networking protocols to obtain real-world information and perform control ubiquitously [5]. As illustrated in Figure 1.5, the data collected by node A is routed within the sensor field by other nodes. When the data reaches the boundary, node E, it is then transferred to the sink. The sink serves as a gateway with a higher processing capacity to communicate with the task manager node. The connection between the sink and task manager node is the public network in the form of the Internet or a satellite. Once the end user receives the data from the task manager node, some processing actions are then performed on the received data.

FIGURE 1.4
Comparison of WSN and IOT.

In Figure 1.5, the sink is essentially a coordinator between the deployed sensor nodes and the end user, and it can be treated like a gateway node. The need of a sink in WSN architecture is due to the limited power and computing capacity of each of the wireless sensor nodes. The gateway node, typically powered by the readily available power source from the AC (alternating current) main, is equipped with a better processor and sufficient memory space that it is able to provide the need for extra information processing before data is transferred to the final destination. The gateway node can therefore share the loadings posed on the wireless sensor nodes and hence prolong their working lifetime. To understand how data is communicated within the sensor nodes in a WSN as shown in Figure 1.5, the protocol stack model of the WSN as shown in Figure 1.6 is investigated. With this understanding, the energy-hungry portions of the wireless sensor node can be identified, and then the WSN can be redesigned accordingly for lower power consumption. To start the basic communication process, consists of sending data from the source to the destination. Primarily, it is the case of two wireless sensor nodes wanting to communicate with each other. The sensor node at source generates information, which is encoded and transmitted to the destination, and the destination sensor node decodes the information for the user. This entire process is logically partitioned into a definite sequence of events or actions, and individual entities then form layers of a communication stack. The WSN protocol stack [4] shown in Figure 1.6 consists of five network layers: physical (PHY) (lowest), data link, network, transport, and application (highest) layers.

FIGURE 1.5
Architecture of a WSN to facilitate smart environments. (From I.F. Akyildiz, W.L. Su, S. Yogesh, and C. Erdal, “A survey on sensor networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, 2002 [4].)

FIGURE 1.6
Sensor networks protocol stack. (From I.F. Akyildiz, W.L. Su, S. Yogesh, and C. Erdal, “A survey on sensor networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, 2002 [4].)

Starting from the lowest level, the PHY layer receives and transfers data collected from the hardware. It is well known that long-distance wireless communication can be expensive in terms of both energy and implementation complexity. While designing the PHY layer for WSNs, energy minimization is considered significantly more important than the other factors, like propagation and fading effects. Energy-efficient PHY layer solutions are currently being pursued by researchers to design for tiny, low-power, low-cost transceiver, sensing, and processing units [6]. The next-higher layer is the data link layer, which ensures reliable point-to-point and point-to-multipoint connections for the multiplexing of data streams, data frame detection, medium access, and error control in the WSN. The data link layer should be power aware and at the same time minimize the collisions between neighbours’ signals because the environment is noisy and sensor nodes themselves are highly mobile. This is also one of the layers in the WSN whereby power saving modes of operation can be implemented. The most obvious means of power conservation is to turn the transceiver off when it is not required. By using a random wake-up schedule during the connection phase and by turning the radio off during idle time slots, power conservation can be achieved. A dynamic power management scheme for WSNs has been discussed [7]; five power-saving modes were proposed, and intermode transition policies were investigated.

The network layer takes care of routing the data supplied by the transport layer. In WSN deployment, the routing protocols in the network layer are important because an efficient routing protocol can help to serve various applications and save energy. By setting appropriate energy and time delay thresholds for data relay, the protocol can help prolong the lifetime of sensor nodes. Hence, the network layer is another layer in the WSN to reduce power consumption. The transport layer helps to maintain the flow of data if the sensor network application requires it. Depending on the sensing tasks, different types of application software can be built and used on the application layer. In contrast to traditional networks that focus mainly on how to achieve high quality-of-service (QoS) provisions, WSN protocols tend to focus primarily on power conservation and power management for sensor nodes [7, 8] as well as the design of energy-aware protocols and algorithms for WSNs [5, 9] to reduce the power consumption of the overall wireless sensor network. By doing so, the lifetime of the WSN can be extended.

However, there must be some embedded trade-off mechanisms that give the end user the option of prolonging the WSN lifetime but at the cost of lower throughput or higher transmission delay. Conversely, the power consumption of the WSN can be reduced by sacrificing the QoS provisions, that is, by lowering the data throughput or having a higher transmission delay. Among th...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• 1. Introduction
• 2. Wind Energy Harvesting System
• 3. Thermal Energy Harvesting System
• 4. Vibration Energy Harvesting System
• 5. Hybrid Energy Harvesting System
• 6. Electrical Power Transfer with “No Wires”
• 7. Conclusions and Future Works
• References
• Index