Resource management in energy harvesting wireless sensor networks

With the emergence of energy harvesting techniques, it is now possible for wireless sensor networks to operate perpetually while supporting certain performance levels. Due to the renewable but non-deterministic nature of the energy harvesting source, the way to manage the harvested energy and provi...

Full description

Saved in:
Bibliographic Details
Main Author: Peng, Shuai
Other Authors: Low Chor Ping
Format: Theses and Dissertations
Language:English
Published: 2015
Subjects:
Online Access:https://hdl.handle.net/10356/65361
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-65361
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
spellingShingle DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Peng, Shuai
Resource management in energy harvesting wireless sensor networks
description With the emergence of energy harvesting techniques, it is now possible for wireless sensor networks to operate perpetually while supporting certain performance levels. Due to the renewable but non-deterministic nature of the energy harvesting source, the way to manage the harvested energy and provide such perpetual operation becomes a major challenge. Thus, in this thesis, we focus on the energy resource management mechanisms for energy harvesting wireless sensor networks. In order to achieve perpetual operations, an Energy Neutral Management (ENM) mechanism is needed to make sure that the harvested energy will be able to replenish the energy that is being consumed by a sensor. Based on different performance maximization goals, we identify two levels of energy neutral management, namely Node-level ENM and Network-level ENM. For Node-level ENM, we study the ways to efficiently utilize the harvested energy so that a sensor can operate perpetually with desired sensor performance level. We firstly consider the case when the sensor performance level (such as the duty cycle) has a linear relationship with the amount of energy consumed by the sensor. We analytically derive a set of energy allocation principles to maximize the amount of harvested energy that can be utilized by a sensor, in the presence of battery storage inefficiencies. These principles in turn maximize the sensor average duty cycle while maintaining its energy neutral state. Since the energy harvesting information is not always available before sensor deployment, we develop a Prediction-FRee Energy Neutral (P-FREEN) management mechanism to implement the derived energy allocation principles based solely on current observed energy harvesting rate and battery residual energy level, which enables perpetual sensor operation with maximized sensor performance level. We next consider the case when the sensor performance level (such as the communication channel throughput) has a non-linear relationship with the amount of energy consumed by the sensor. An off-line optimal energy allocation mechanism, which maximizes the average channel throughput while maintaining the energy neutral state of the sensor, is developed via convex optimization. Based on this optimal mechanism, we propose an on-line Adaptive Energy Budget Assignment Policy (ABAP) that asymptotically maximizes the average channel throughput by using the historical energy harvesting and channel state information observed by the sensor. We also study a method to reduce the energy loss caused by the battery energy storage inefficiencies. The fraction of the harvested energy that can be utilized by using this method is analytically derived and is integrated into ABAP to provide improved average channel throughput. For Network-level ENM, we study the network layer routing protocols that coordinately control the energy consumption of sensors in the network, (by controlling the routing paths of the data traffic), so that perpetual network operations can be achieved with improved network performance levels. We focus on developing routing protocols based on two widely used data delivery models, namely the Query Driven Model and the Continuous Model. Using the query driven model, only data queried by the user will be sensed and delivered to the destination, which prevents the delivery of non-desired data and is thus highly energy efficient. Hence, we propose a query driven Energy Neutral Directed Diffusion (ENDD) protocol to provide Network-level ENM. ENDD employs the traffic flow admission control mechanism to regulate the traffic load carried by a sensor based on its energy harvesting status, which in turn prevents sensors from shutting down due to excessive usage of energy. In this way, routing path failures can be prevented, which ensures the data delivery consistency and improves the network data throughput. Continuous model is used for applications that require the periodical sensing and delivery of data information. Clustering protocols are suitable for such data delivery model as it can enable in-network data aggregations and thus reduces energy wastage caused by the delivery of the redundant data information. We develop an Energy Neutral Clustering (ENC) protocol to group the network into several clusters with the goal of providing perpetual network operation with consistent data delivery. ENC employs a novel Cluster Head Group (CHG) mechanism that allows a cluster to use multiple cluster heads to share the heavy traffic load. This CHG mechanism can help reduce the frequency of cluster re-formations, which in turn reduces the control message overhead. The optimum number of clusters that maximizes the amount of information gathered from the network is mathematically derived via convex optimization. Based on this optimum number of clusters, an extension to ENC is proposed to group the network into equal sized clusters so that maximized network information gathering can be achieved. The performance of our proposed energy management mechanisms is verified through theoretical analysis and extensive empirical studies. We believe that these mechanisms and their results make important contributions to the study of the energy management mechanisms in energy harvesting wireless sensor networks.
author2 Low Chor Ping
author_facet Low Chor Ping
Peng, Shuai
format Theses and Dissertations
author Peng, Shuai
author_sort Peng, Shuai
title Resource management in energy harvesting wireless sensor networks
title_short Resource management in energy harvesting wireless sensor networks
title_full Resource management in energy harvesting wireless sensor networks
title_fullStr Resource management in energy harvesting wireless sensor networks
title_full_unstemmed Resource management in energy harvesting wireless sensor networks
title_sort resource management in energy harvesting wireless sensor networks
publishDate 2015
url https://hdl.handle.net/10356/65361
_version_ 1772827644453191680
spelling sg-ntu-dr.10356-653612023-07-04T16:08:46Z Resource management in energy harvesting wireless sensor networks Peng, Shuai Low Chor Ping School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks With the emergence of energy harvesting techniques, it is now possible for wireless sensor networks to operate perpetually while supporting certain performance levels. Due to the renewable but non-deterministic nature of the energy harvesting source, the way to manage the harvested energy and provide such perpetual operation becomes a major challenge. Thus, in this thesis, we focus on the energy resource management mechanisms for energy harvesting wireless sensor networks. In order to achieve perpetual operations, an Energy Neutral Management (ENM) mechanism is needed to make sure that the harvested energy will be able to replenish the energy that is being consumed by a sensor. Based on different performance maximization goals, we identify two levels of energy neutral management, namely Node-level ENM and Network-level ENM. For Node-level ENM, we study the ways to efficiently utilize the harvested energy so that a sensor can operate perpetually with desired sensor performance level. We firstly consider the case when the sensor performance level (such as the duty cycle) has a linear relationship with the amount of energy consumed by the sensor. We analytically derive a set of energy allocation principles to maximize the amount of harvested energy that can be utilized by a sensor, in the presence of battery storage inefficiencies. These principles in turn maximize the sensor average duty cycle while maintaining its energy neutral state. Since the energy harvesting information is not always available before sensor deployment, we develop a Prediction-FRee Energy Neutral (P-FREEN) management mechanism to implement the derived energy allocation principles based solely on current observed energy harvesting rate and battery residual energy level, which enables perpetual sensor operation with maximized sensor performance level. We next consider the case when the sensor performance level (such as the communication channel throughput) has a non-linear relationship with the amount of energy consumed by the sensor. An off-line optimal energy allocation mechanism, which maximizes the average channel throughput while maintaining the energy neutral state of the sensor, is developed via convex optimization. Based on this optimal mechanism, we propose an on-line Adaptive Energy Budget Assignment Policy (ABAP) that asymptotically maximizes the average channel throughput by using the historical energy harvesting and channel state information observed by the sensor. We also study a method to reduce the energy loss caused by the battery energy storage inefficiencies. The fraction of the harvested energy that can be utilized by using this method is analytically derived and is integrated into ABAP to provide improved average channel throughput. For Network-level ENM, we study the network layer routing protocols that coordinately control the energy consumption of sensors in the network, (by controlling the routing paths of the data traffic), so that perpetual network operations can be achieved with improved network performance levels. We focus on developing routing protocols based on two widely used data delivery models, namely the Query Driven Model and the Continuous Model. Using the query driven model, only data queried by the user will be sensed and delivered to the destination, which prevents the delivery of non-desired data and is thus highly energy efficient. Hence, we propose a query driven Energy Neutral Directed Diffusion (ENDD) protocol to provide Network-level ENM. ENDD employs the traffic flow admission control mechanism to regulate the traffic load carried by a sensor based on its energy harvesting status, which in turn prevents sensors from shutting down due to excessive usage of energy. In this way, routing path failures can be prevented, which ensures the data delivery consistency and improves the network data throughput. Continuous model is used for applications that require the periodical sensing and delivery of data information. Clustering protocols are suitable for such data delivery model as it can enable in-network data aggregations and thus reduces energy wastage caused by the delivery of the redundant data information. We develop an Energy Neutral Clustering (ENC) protocol to group the network into several clusters with the goal of providing perpetual network operation with consistent data delivery. ENC employs a novel Cluster Head Group (CHG) mechanism that allows a cluster to use multiple cluster heads to share the heavy traffic load. This CHG mechanism can help reduce the frequency of cluster re-formations, which in turn reduces the control message overhead. The optimum number of clusters that maximizes the amount of information gathered from the network is mathematically derived via convex optimization. Based on this optimum number of clusters, an extension to ENC is proposed to group the network into equal sized clusters so that maximized network information gathering can be achieved. The performance of our proposed energy management mechanisms is verified through theoretical analysis and extensive empirical studies. We believe that these mechanisms and their results make important contributions to the study of the energy management mechanisms in energy harvesting wireless sensor networks. DOCTOR OF PHILOSOPHY (EEE) 2015-08-19T04:59:32Z 2015-08-19T04:59:32Z 2015 2015 Thesis Peng, S. (2015). Resource management in energy harvesting wireless sensor networks. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/65361 10.32657/10356/65361 en 250 p. application/pdf