ABSTRACT
Wireless Sensor Network (WSN) is considered to be a highly resource constrained class of network where energy consumption has being a major challenge. In this work, a cross layer design methodology was adopted to design an energy efficient routing protocol called “Position Responsive Routing Protocol” (PRRP). Position Responsive Routing Protocol is designed to minimize energy consumption of the network by reducing the average communication distance over the network, through information obtained from global position system (GPS) to provide the exact location of the Nodes. The performance of the proposed Position Responsive Routing Protocol was critically evaluated in terms of average energy consumption of the network is 9.8kw with PRRP and 12.7kw without PRRP, which represents energy saving of about 23%. Network congestion of the network is 0.68 over 1.89 without PRRP, BER 2.8 x 10-5 and 7.8 x 10-5 without PRRP also SNR is 7.19 with PRRP and 0.9856 without PRRP. The simulated results show a significant improvement in the WSN in terms of energy efficiency and overall performance of WSN.
TABLE OF CONTENTS
Cover Page i
Title Page ii
Declaration iii
Certification iv
Dedication v
Acknowledgements vi
Table of content viii
List of Table ix
List of Figures x
Abstract xi
CHAPTER 1
INTRODUCTION
1.1 Background of Study 1
1.2 Statement of The Problem 9
1.3 Aim And Objectives of the Study 9
1.4 Significance of the Study 10
1.5 Scope of the Study 10
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction to Wireless Sensor Network 11
2.1.1 Hierarchal routing protocols 15
2.2 Energy Consumption in Transmission 19
2.3 Enhancing Energy Efficiency of Wireless Sensor Network 21
2.3.1 Causes of Energy Waste 21
2.3.2 Classification of Energy Efficient Techniques 22
2.3.3 Production Step 23
2.3.4 Processing and Communication Step 25
2.3.5 Protocol Overhead Reduction 27
2.3.6 Adaptive Transmission Period 27
2.3.7 Cross layering Optimization 28
2.3.8 Optimized Flooding 30
2.3.9 Energy Efficient Routing Protocols 31
2.4 Other Methods of Enhancing Energy Efficiency In Wireless Network 31
2.4.1 Routing Protocols 31
2.4.2 Reactive Routing Protocols 32
2.4.3 Proactive Routing Protocols 33
2.4.4 Hybrid Routing Protocols 33
2.5 Enhancing Energy Efficiency In Wireless Network Using Responsive Routing Protocol (PRRP) Approach 34
2.5.1 Position Based Routing Protocols 34
2.5.2 Performance of Position Based Routing Protocol 35
2.5.3 Location Services 35
2.5.4 Forwarding strategies 36
2.5.5 Restricted Directional Flooding 36
2.5.6 Hierarchical Routing 37
2.5.7 Location-aided Routing Protocol (LAR) 37
2.6 Key Performance Indicators 37
2.7 Summary Review of Related Literature 37
2.8 Proposed PRRP model and current research gap 41
CHAPTER 3
MATERIALS AND METHOD
3.1 Materials 44
3.1.1 Choice of Environment 45
3.2 Methods 45
3.2.1 Procedure for Data Collection 45
3.3 Details of Data Collected on Receiving Antenna Power,
Area, Antenna Gain, Distance, Packet Transmitted and Packet
Received for the Period of Nine Nodes 46
3. 4 To determine the congestion, attenuation, interference, bit error rate and signal to noise ratio of the characterized network. 47
3.5 To develop a set of instructions on energy inefficiency recognition
and enhancement. 50
3.6 To design a model for enhancing energy efficiency of wireless sensor network using position responsive routing protocol (PRRP). 51
CHAPTER 4
RESULTS AND DISCUSSION
4.1 Node Variation of the window size 53
4.2 Nodes variation of packet loss 54
4.3 Nodes variation of bit-error rate 55
4.4 Nodes variation of packet loss and window size 57
4.5 Nodes variation of congestion parameters 58
4.6 Variation of packet loss against window size 60
4.7 Comparing energy in enhancing energy efficiency of wireless
sensor network with and without using position responsive
routing protocol (PRRP) 62
4.8 Variation of window size against node under PRRP approach 64
CHAPTER 5
CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion 69
5.2 Recommendations 70
REFERENCES
LIST OF TABLES
Table 3.1 Data Collected On Receiving Antenna Power, Area,
Antenna Gain, Distance, Packet Transmitted And
Packet Received 46
Table 3.2 Packet Loss Experienced on Node Basis 47
Table 3.3 Packet loss and bit error rate variations on node basis 49
Table 4.1: Comparing energy in enhancing energy efficiency of
wireless sensor network with and without using position
responsive routing protocol (PRRP) 61
Table 4.2: Comparing Window Size in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP) 63
Table 4.3: Comparing bit error rate in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP) 65
Table 4.4: Comparing SNR in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP) 67
LIST OF FIGURES
Fig 2.1: The design testbed of wireless sensor network 43
Fig 3.1 Developed set of instructions on energy inefficiency
recognition and enhancement 50
Fig 3.2 Designed model for enhancing energy efficiency of
wireless sensor network without using position responsive
routing protocol (PRRP) 51
Fig 3.3 Designed model for enhancing energy efficiency of
wireless sensor network using position responsive
routing protocol (PRRP) 52
Figure 4.1 Node Variation of Window Size 53
Figure 4.2: Node variation of packet loss 54
Figure 4.3: Node variation of Bit-Error Rate 55
Figure 4.4: Graph of Bit-Error Rate against Window Size 56
Figure 4.5: Node variations of Packet Loss and window size 57
Figure 4.6: Graph of Packet Loss against Bit-Error Rate 58
Figure 4.7: Graph of Node Congestion Parameters variation 59
Figure 4.8: Graph of Packet Loss against Window Size 60
Figure 4.9: Comparing energy in enhancing energy efficiency of
wireless sensor network with and without using position
responsive routing protocol (PRRP) 62
Figure: 4.10 Comparing congestion in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP) 63
Figure 4.11: Graph of Window Size (W1) against node for PPRP 64
Figure 4.12: Comparing bit error rate in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP) 65
Figure 4.13: Graph Congestion Parameters with PPRP against Nodes 66
Figure 4.14: Comparing SNR in enhancing energy efficiency of wireless sensor network with and without using position responsive routing protocol (PRRP). 67
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF STUDY
A Wireless Sensor Network (WSN) can be defined as a network of small embedded devices, called sensors, which communicate wirelessly following an ad hoc configuration. They are located strategically inside a physical medium and are able to interact with it in order to measure physical parameters from the environment and provide the sensed information (Zaman et al, 2015). The nodes mainly use a broadcast communication and the network topology can change constantly due to the fact that nodes are prone to fail. Because of this, we should keep in mind that nodes should be autonomous and, frequently, they will be disregarded. This kind of device has limited power, low computational capabilities and limited memory.
In a definite term, Sensors are devices that produces measureable response to a change in physical or chemical conditions, a device that respond to a stimulus such as heat, light or pressure and generates a signal that can be measured or interpreted (Heinzelman, et al, 2000).
The desire to advance in research and development of WSN was initially motivated by military applications such as surveillance of threats on the battlefield, mainly because WSN can replace single high-cost sensor assets with large arrays of distributed sensors. There are other interesting fields like home control, building automation and medical applications. A number of hospitals and medical centers are exploring the use of WSN technology in a wide range of applications, including pre-hospital and in-hospital patient monitoring and rehabilitation and disaster response. WSNs can also be found in environmental monitoring applications such as marine fish farms and fire detection in forest and rural areas.
Wireless sensor technology is playing a vital role in many of the commercialized industrial automation processes and various other real life applications. It is particularly suitable for harsh environment applications where deploying of other network infrastructure is difficult and/or almost impossible such as in battlefield, in hazardous chemical plant, and in high thermal environment. It is not uncommon to see that most of the crucial surveillance and security applications also rely on sensor based applications. Sensors which are tiny in size and cheap in cost have the capabilities to be deployed in a range of applications as explained. Essentially all sensor networks comprise some forms of sensing mechanism to collect data from an intended physical environment either by a time driven approach or by event triggering approach (Nurhayati et al, 2011). By these approaches a sensor will convey the sensed data to a destination or sink (multiple destinations/sinks are also possible) via some kinds of routing algorithm such as Minimum Cost Forwarding Algorithm (MCFA), Directed Diffusion Routing Protocol (DDRP), or one of the cluster-based routing protocols. Being very small in size, sensor nodes are built with limited computational capacity, small storage memory, and finite battery power capacity.
The structure of a typical WSN node consists of four main components: a sensing element, normally used for sensing a physically measureable parameter; an Analog-to-Digital Converter (ADC), used for converting analog signals to some digital formats; a processing unit, providing simple/basic data processing and computation capabilities; and a power unit, responsible for sensor node’s operation life span. It is a known fact that WSN is a resource constrained network in which energy efficiency is always the main issue since the operation of WSN depends heavily on the life span of the sensor nodes’ battery (Heinzelman, et al, 2000). The most energy consuming operation in WSN is the data packet routing activity. The characteristics of the WSN are different from the conventional networks. These unique characteristics are often taken into account for addressing the issues and challenges related to network coverage, runtime topologies management, node distribution, node administration, node mobility energy efficiency/consumption, network deployment, application areas/environment, and so forth.
Nodes in a WSN are generally energy, computation, and memory constrained. Consequently, there is a need for research and development into low-computation resource-aware algorithms for WSNs, targeting at small, highly resource constrained embedded sensor nodes. Energy consumption is of prime importance in WSNs and thus some algorithms and hardware were designed with energy efficiency or energy awareness as a central focal point of interest. Enhancing energy efficiency of WSN with respect to the communication routing protocol is the primary concern of this research.
As already mentioned, sensor nodes in WSNs are usually battery powered but nodes are typically unattended because of their deployment in hazardous, hostile or remote environments. A number of power saving techniques must be used both in the design of electronic transceiver circuits and in network protocols. The first step towards reduced power consumption is a sound electronic design selecting the right components and applying appropriate design techniques to each case. One of the major causes of energy loss in the WSN node is the idle mode consumption, when the node is not transmitting/receiving any information but listening and waiting for information from other nodes (Manjeshwar and Grawal, 2000). There is also an energy loss due to packet collision, as all packets involved in the collision are discarded and must be retransmitted. A third cause of energy loss is the reception of packets not addressed to the node. The fourth major source of wasted energy is the transmission –and possible retransmission- of control packets, as these can be seen as protocol overhead.
There are several studies that present different aspects related to power saving techniques, but all of them are focused in a single way to improve the energy consumption and save power in WSNs. The main Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, a survey is presented for power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well-known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols
This project is aimed at the design of a new energy efficient routing protocol: Position Responsive Routing Protocol (PRRP), to address the energy issues in Wireless Sensor Network (WSN) and specifically to enhance the energy efficiency in WSN. Consequently, there is a need for research and development into low-computation resource aware algorithms or model for WSNs, targeting at small, highly resource constrained embedded sensor nodes. Energy consumption is of prime importance in WSNs and thus some algorithms (Singh and Mittal, 2013) and hardware was designed with energy efficiency or energy awareness as a central focal point of interest. Enhancing energy efficiency of WSN with respect to the communication routing protocol is the primary concern of this research. The simulation results are expected to show a significant improvement over the aforementioned protocols in terms of energy efficiency and the overall performance of the WSN.
The main operational sustainability concern in WSN is its energy resource constraint. This brings along in recent years that a great number of energy efficient routing protocols have been proposed for WSNs based on the network organization and the routing protocol operations. Some of these focused on minimizing the communication distance to reduce the energy consumption and a handful of them focused on fair energy distribution to avoid the routing hole (hot spot) problems (Gautam, 2009). The routing whole issue was described and addressed by utilizing mobility based energy efficient routing protocols. These protocols are suitable in certain situations; however they may not be applicable in cases where mobility is not feasible such as earthquake, forest fire, and disaster management. Mobility techniques do have other challenges like increased energy overhead owing to frequent network topology changes and data packet drops due to high latency. Various other research work focusing on energy efficiency routing protocols can be found (Liu and Li, 2009)
Many researchers had paid attention to the WSN energy issue by designing different routing techniques and MAC-layer protocols to raise the energy level in WSN. The work reveals that a range of different energy efficient routing protocols in the recent past were designed mostly based on the network structure such as hierarchical routing, location routing, and flat based routing.
Hierarchal routing protocols are considered more energy efficient when compared with flat and location based routing protocols. A number of hierarchal based energy efficient routing protocols have been referred to in some works such as LEACH (Heinzelman, et al, 2000), TEEN and APTEEN (Manjeshwar and Grawal, 2000), PEGASIS (Lindsey and Raghavendra, 2002), SOP (Subramanian and Katz, 2000), HPAR (Aslam, and Rus, 2001), VGA (Al-Karaki et al, 2004), Sensor Aggregate (Fang et al, 2003), TTDD (Ye et al, 2002), Energy Efficient Self-Healing (Zaman et al, 2016), Energy Efficient Position Based (Zaman et al, 2015), and CELRP (Nurhayati et al, 2011). The hierarchal based energy efficient routing protocols refers to the fact that the main advantage of hierarchal approach is to control the data duplication and is best suited for data aggregation.
With this proposed format, nodes are not allowed to communicate with the sink directly they must go through a cluster head for communication purposes, while the cluster head collects the data from different nodes within a specific cluster area, and then it sends the collected data either to another cluster head or directly to the sink. This approach is more balanced and energy efficient comparable to flat and location based routing protocols (Zaman et al, 2014). However, the disadvantage of this approach is that it results in quick energy drain of the cluster head nodes as most of the time they are involved in sending and receiving the data packets. Rotation of cluster heads is possible but it also brings along an issue related to the loss of the energy resource.
Routing in WSNs is a challenging issue due to the inherent characteristics which differentiate such network from other wireless networks such as ad hoc networks and cellular networks. In recent years, many algorithms has been proposed for the routing issues in WSNs. In some of these works minimum energy routing problem has been addressed. In this work “Enhancing the energy efficiency of wireless system network using position responsive routing protocol (PRRP) is to minimized the total energy consumed on the path, the objective is to maintain the connected network as long as possible. If sender nodes consume energy more equitable, to provide connectivity for longer, and the network lifetime increases.
Among all topologies based routing protocols, namely Flat routing protocols, Hierarchal routing protocol and location based routing protocols, hierarchal routing protocol technique is more popular regarding the power servicing of sensor nodes. This technique works on the formation of several clusters (a sub network within network) is responsible to transfer data from node to the sink. While direct data sending approach from each node is not supported by this method clusters communication with sink works on the basics of cluster head (CH).
They collect data from neighbouring nodes and send it to another CH, while responsible for any other cluster, this mechanism continues until the data reaches to the sink.
A number of different protocols have been proposed for WSN node localization or location based routing. Different protocols proposed for WSN include the following GAF (Xu et al, 2001), GEAR (Yu et al, 2001), SPAN (Chen et al, 2001), MFR, GEDIR (Stojmenovic and Lin, 1999), and GOAFR (Kuhn et al, 2003). These hierarchal based energy efficient routing protocols referred to the fact that the main advantage of these protocols is the ability to identify the correct location of the sensor node within the sensor network. Node localization is directly linked to energy efficiency of WSN. It saves energy resources of WSN.
However in most cases these protocols resulted in energy loss due to its geographical topology and node distribution in the WSN. There is thus still a gap in energy efficient routing protocol design and solutions for this class of routing.
The list of various type of flat based routing protocols are as follows SPIN (Kulik, et al, 2000), Directed Diffusion (Intanagonwiwat et al, 2000), Rumor Routing (Braginsky and Estrin, 2002), GBR (Hedetniemi et al, 1988), MCFA (Ye et al, 2001), COUGAR by (Yao and Gehrke, 2002). The hierarchal based energy efficient routing protocols referred to the fact that the main advantage of flat based routing protocol is its simplicity in operation and it had a direct communication mechanism with the base station in which all nodes are allowed to participate during the routing operation. For its simplicity, the nodes only need information about their closest neighbours. However, the major disadvantage is that nodes spread out in a flat manner and all nodes are attempting to participate equally thus causing the nodes closer to the sink to deplete their power sooner than those located further away from the sink. This is mainly due to the heavy data transmission load. This is badly affecting the nodes closer to the sink for keeping them alive longer. Therefore the nodes further away may be unable to communicate with the base station after some time due to network isolated segmentation problem in the WSN.
Consequently many of the nodes are not able to participate in routing thus not utilizing their entire energy effectively. More research works are deemed necessary to address the WSN energy efficiency in this aspect. In addition, flat routing is still having issues in data collision overhead, links formed on the fly without synchronization, energy dissipation depending on traffic patterns, and fairness being not guaranteed.
1.2 STATEMENT OF THE PROBLEM
Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where provision of electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs.
The power management schemes of wireless sensor networks have attracted high attention in recent years. Much published research has addressed all kinds of issues related to them and the associated challenges. Most preferred Android devices have very pleasing services and features but with limited battery life. Hence there is a need for a comprehensive power management approach in routing protocols.
1.3 AIM AND OBJECTIVES OF THE STUDY
The project is aimed at enhancing energy efficiency of wireless sensor network using position responsive routing protocol (PRRP) approach
Therefore, the objectives of this work are;
i. To study the various related works to ascertain the current state of art in the field of wireless sensor network.
ii. To study the effect of energy consumption on extending the entire network life span.
iii. To determine the congestion, bit error rate and signal to noise ratio of the characterized network.
iv. To design a model for enhancing energy efficiency of wireless sensor network using position responsive routing protocol (PRRP).
v. To justify and validate the energy consumption in wireless sensor networks with and without PRRP.
1.8 SIGNIFICANCE OF THE STUDY
Wireless Sensor Networks (WSN) consists of very tiny nodes structure, with very small batteries without having facility to recharge it. It works under any hard circumstances; hence this type of network is gaining high importance day by day. The PRRP routing protocols has been designed for energy efficiency or optimization within an approach under controlled conditions.
1.9 SCOPE OF THE STUDY
This work will be carried out on wireless sensor network using an energy efficient routing protocol: Position Responsive Routing Protocol (PRRP), using a GSM operated network of 9 Nodes and I sink performed on a network simulator and the results will be compared with the existing works.
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