[4] Ian F Akyildiz, Dario Pompili, and Tommaso Melodia. Challenges for effi ..... [60] Uichin Lee, Paul Wang, Youngtae Noh, Luiz FM Vieira, Mario Gerla, and Jun-.
Optimizing Energy Consumption with Sink Mobility Management in Underwater Wireless Sensor Networks
Author Engr. Waseem Raza 14F-MS-TE-02
Supervisor Engr. Farzana Arshad Assistant Professor Department of Telecom Engineering University of Engineering and Technology Taxila Co-Supervisor Dr. Nadeem Javaid Associate Professor Department of Computer Science COMSATS Institute of Information Technology Islamabad
DEPARTMENT OF TELECOM ENGINEERING FACULTY OF TELECOM & INFORMATION ENGINEERING UNIVERSITY OF ENGINEERING AND TECHNOLOGY TAXILA May 2016
Optimizing Energy Consumption with Sink Mobility Management in Underwater Wireless Sensor Networks Author Engr. Waseem Raza 14F-MS-TE-02 A thesis submitted in partial fulfillment of the requirements for the degree of M.Sc. Telecom Engineering Supervisor Engr. Farzana Arshad Assistant Professor Telecom Engineering Department External Examiner Signature: ——————– Thesis Supervisor Signature: ——————– Co-Supervisor Dr. Nadeem Javaid Thesis Co-Supervisor Signature: ——————– Associate Professor Department of Computer Science COMSATS Institute of Information Technology Islamabad DEPARTMENT OF TELECOM ENGINEERING FACULTY OF TELECOM & INFORMATION ENGINEERING UNIVERSITY OF ENGINEERING AND TECHNOLOGY TAXILA May 2016
ABSTRACT Optimizing Energy Consumption with Sink Mobility Management in Underwater Wireless Sensor Networks Waseem Raza 14F-MS-TE-02 In this work we propose a novel routing strategy to cater the energy consumption and delay sensitivity issues in deep underwater wireless sensor networks. This strategy is named as Event Segregation based Delay sensitive Routing; in which sensed events are segregated on the basis of their criticality and then forwarded to their respective destinations. Forwarding decisions are made on the basis of forwarding functions which depend on different routing metrics like: Signal Quality Index, Localization free Signal to Noise Ratio, Energy Cost Function and Depth Dependent Function. We have comprehensively investigated the ranges of previously defined forwarding functions. The problems of incomparable values of forwarding functions in different depth regions of network cause uneven delays in forwarding process. Hence forwarding functions are redefined to ensure their comparable values in different depth regions. Packet forwarding strategy is based on event segregation approach which forwards one third of the generated events (delay sensitive) to surface sinks and two third events (normal events) are forwarded to mobile sinks. Motion of mobile sinks is influenced by the relative distribution of normal nodes. key words— Energy efficiency, Delay-sensitive Routing, Forwarding Function, Holding Time, Event Segregation, Network Lifetime, Throughput
ii
UNDERTAKING I certify that research work titled “Optimizing Energy Consumption with Sink Mobility Management in Underwater Wireless Sensor Networks” is my own work. The work has not been presented elsewhere for assessment. Where material has been used from other sources it has been properly acknowledged / referred.
——————Waseem Raza 14F-MS-TE-02
iii
ACKNOWLEDGEMENT I am extremely thankful to Almighty Allah, with out His countless blessing it would not be possible for me to complete this work. I seek His endless blessing for future. After that I am very thankful to my family especially to my father whose persistent support and confidence in me has always been a great source of inspiration and motivation throughout my educational career. He has always been a great person; uneducated but full of rural wisdom and vision to look deeper into the future. I am grateful to Dr Nadeem Javaid from Computer Science Department COMSATS Islamabad for His collaboration and technical support throughout this research work. He has always been supportive and motivating. In the end I am thankful to my supervisor Engr. Farzana Arshad for her considerations and devotions for this work. Without her it would be quite impossible to go through all the phases of research work. Her motivation and blessing has been a constant source of encouragement and strength for me. I am also thankful; to all the respective members of PGS at Telecom Engineering Department UET Taxila, especially to our prestigious Chairman Dr Yasir Amin for the support and matchless facilities provided for fulltime master scholars at TED UET Taxila.
iv
This work is dedicated to: Innocent victims of terrorism from Pakistan and all over the world; APS and Bacha Khan University Martyrs and their families; especially to; Dr. Syed Hamid Hussain Shaheed.
v
TABLE OF CONTENTS The title of my thesis
i
Abstract
iii
Undertaking
iv
Acknowledgement
v
Dedication
vi
Table of Contents
viii
List of Figures
ix
List of Tables
x
Chapter 1
Introduction and Background
1
1.1
Statement of Problem . . . . . . . . . . . . . . . . . . . . . . . . .
4
1.2
Research Challenges in UWSNs . . . . . . . . . . . . . . . . . . .
6
1.2.1
Capricious Underwater Environment . . . . . . . . . . . . .
6
1.2.2
Low Data Rates . . . . . . . . . . . . . . . . . . . . . . . .
6
1.2.3
Damage to Equipments . . . . . . . . . . . . . . . . . . . .
6
1.2.4
Deployment and Maintenance Cost . . . . . . . . . . . . .
7
Classification of UWSNs . . . . . . . . . . . . . . . . . . . . . . .
7
1.3.1
One Dimensional UWSNs . . . . . . . . . . . . . . . . . .
7
1.3.2
Two Dimensional UWSNs . . . . . . . . . . . . . . . . . .
7
1.3.3
Three Dimensional UWSNs . . . . . . . . . . . . . . . . .
8
1.3.4
Four Dimensional UWSNs . . . . . . . . . . . . . . . . . .
8
Applications of UWSNs . . . . . . . . . . . . . . . . . . . . . . . vi
9
1.3
1.4
1.5
1.4.1
Monitoring and Exploration Application . . . . . . . . . . .
1.4.2
Military Applications . . . . . . . . . . . . . . . . . . . . . 10
1.4.3
Assisted Navigation for Deep Sea Exploration
1.4.4
Disaster Monitoring and Prevention . . . . . . . . . . . . . 12
2.2
. . . . . . . 11
A Typical UWSN and Solutions . . . . . . . . . . . . . . . . . . . 13 1.5.1
Subnero Solutions . . . . . . . . . . . . . . . . . . . . . . 13
1.5.2
DSPComm Solutions . . . . . . . . . . . . . . . . . . . . . 15
Chapter 2 2.1
9
Literature Review
17
Ad-hoc Network Protocols . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1
Proactive Routing Protocols . . . . . . . . . . . . . . . . . 18
2.1.2
Reactive Routing Protocols . . . . . . . . . . . . . . . . . . 19
Underwater Routing Protocols . . . . . . . . . . . . . . . . . . . . 19 2.2.1
Localization Based Routing Protocols . . . . . . . . . . . . 20
2.2.2
Localization Free Routing Protocols . . . . . . . . . . . . . 23
Chapter 3
Problem Statement and Motivation
28
3.1
Forwarding Function Analysis . . . . . . . . . . . . . . . . . . . . 28
3.2
Motivation for Event Segregation . . . . . . . . . . . . . . . . . . . 34
Chapter 4
ESDR: Event Segregation based Delay sensitive Routing
36
4.1
Network Initialization and Node Deployment . . . . . . . . . . . . 37
4.2
Statistical Event Generation and Segregation . . . . . . . . . . . . . 38
4.3
Packets Forwarding Strategy . . . . . . . . . . . . . . . . . . . . . 40 4.3.1
4.4
Updating Depth Threshold . . . . . . . . . . . . . . . . . . 42
The Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4.1
Thorp Attenuation Formula . . . . . . . . . . . . . . . . . 44
4.4.2
Monterey-Miami Parabolic Equation . . . . . . . . . . . . . 46
4.5
Improvements in Terms of Forwarding Function . . . . . . . . . . . 48
4.6
Improvements in Terms of Holding Time . . . . . . . . . . . . . . . 50 vii
4.7
Mobility of Sink . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.7.1
Synchronized and Uniform Mobility of AUV . . . . . . . . 51
4.7.2
Adaptive Mobility of Courier Nodes . . . . . . . . . . . . . 54
Chapter 5
Performance Evaluation
56
5.1
Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.2
Performance Evaluation Metrics . . . . . . . . . . . . . . . . . . . 57 5.2.1
Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . 58
5.2.2
Average end to end delay . . . . . . . . . . . . . . . . . . . 61
5.2.3
Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2.4
Transmission Loss . . . . . . . . . . . . . . . . . . . . . . 64
5.2.5
Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Chapter 6
Conclusion and Future Work
viii
68
List of Figures 1.1
Underwater Communication Scenario . . . . . . . . . . . . . . . . 14
1.2
Schematic Diagram of a Node . . . . . . . . . . . . . . . . . . . . 14
4.1
Network Diagram in ESDR . . . . . . . . . . . . . . . . . . . . . . 38
4.2
Packet Format in ESDR . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3
Eligible Neighbours with Range and Depth Threshold . . . . . . . . 43
4.4
Synchronized mobility of AUV . . . . . . . . . . . . . . . . . . . . 52
4.5
Adaptive mobility of AUV/CN . . . . . . . . . . . . . . . . . . . . 54
5.1
Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2
Stability Period, half-life and life-time . . . . . . . . . . . . . . . . 60
5.3
Average end to end delay: Comparison . . . . . . . . . . . . . . . . 62
5.4
Delay comparison in ESDR . . . . . . . . . . . . . . . . . . . . . . 63
5.5
Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.6
Packet Delivery Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.7
Transmission Loss . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.8
Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
ix
List of Tables 3.1
Parameters values used in previous schemes . . . . . . . . . . . . . 30
3.2
Ranges w.r.t depth threshold . . . . . . . . . . . . . . . . . . . . . 31
3.3
Forwarding Functions Ranges . . . . . . . . . . . . . . . . . . . . 33
4.1
Centers of elliptical paths . . . . . . . . . . . . . . . . . . . . . . . 53
5.1
Parameters values used in simulations . . . . . . . . . . . . . . . . 58
x
List of Algorithms 1
Broadcasting to Surface Sink . . . . . . . . . . . . . . . . . . . . . 41
2
Forwarding to CN . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3
Updating Depth Threshold . . . . . . . . . . . . . . . . . . . . . . 43
xi
CHAPTER 1
Introduction and Background Man has always been curious about nature asking questions and willing to find more about the things influencing on his life. Curiosity leads towards exploration and observation of the natural phenomena which is the first step of the scientific process. It provides an intellectual fodder for the scientific discoveries. It’s about gaining knowledge and expanding our horizon and putting the questions that have not yet been asked. Curiosity and exploration have been the motivation for some great thinkers and scientist especially; of last three to four centuries, and it has led towards some greatest discoveries of mankind. As in the word of Einstein [1]: "The important thing is not to stop questioning. Curiosity has its own reason for existing". The imperceptible aspiration to explore and challenge the environs of what we know has provided welfare to our society for centuries. Interest and investigation are imperative motivation to the human soul. This motivation had led human explorers to the farthest and unreachable areas on the earth. This interest has driven human investigation to most noteworthy mountains, coldest landmasses and even into the deeper space. But there is one more unexplored area; much closer to our homes and host of largest and diverse ecosystem; The Ocean. Study by World Register of Marine Species (WoRMS) has shown that the aquatic environs may be home to as many as a million species of animals and plants [2] but only about a quarter of them have actually been properly discovered and recognized. Ocean has been swaying on human race since ancient times. It has disseminated people on different continents and have connected civilizations with each other for 1
different purposes. The seas give advantageous transport routes as around 90% of all trade between nations is conveyed by ships. It provides oxygen to atmosphere and act as sink for carbon dioxide, thus have helped to negate human-caused global warming and climate change. Nearly half the CO2 produced by human activities in the last 200 years has dissolved into the ocean. More than half of the world population live within 150km of coastline. Ocean is the source of their food, business and employment. Besides providing sustenance items and livelihood ocean plays vital role in weather systems, economy and defense system of a country. Ocean covers 72% of earth surface, that is 139 million square miles or 139 with 19 zeros after it [3]. It is not just very wide rather it is 4000m deep as well. That is 10 Empire State buildings heaped on top of each other! It is quite strange that such an important influencer on human civilization has not been explored to its full potential. Conventional exploring mechanism involves sending/deploying a sampler, to the bottom of the ocean or into the monitoring area of interest, which collects required information and then returns on-shore data center. There is lack of real time monitoring especially required in surveillance and seismic monitoring applications. There is also no mechanism of retrieving if there is failure or mis-configuration. These above mentioned limitations empower Under Water Sensor Network (UWSN) to become an appropriate candidate for longtime and reliable monitoring and data gathering applications [4]. UWSN is a fusion of wireless technology with extremely small micro mechanical sensors having smart sensing, intelligent computing, and communication capabilities. UWSN is a network of autonomous sensor nodes which are spatially distributed in underwater to sense the water-related properties such as quality, temperature, and pressure. The sensor nodes, stationary or mobile, are connected wirelessly via communication modules to transfer various events of interest [5, 6, 7]. These nodes are not aware of their exact position, however their depth can be found using depth (pressure) sensors. Generally, in UWSN acoustic transceivers are used for 2
communication. Acoustic signal propagation speed depends on depth, temperature and salinity of the water. Generally these signal propagate at about 1500m/s which is five orders of magnitude lesser than that of radio links in terrestrial wireless networks. The acoustic waves are low frequency waves which offer small bandwidth but have long wavelengths. Thus, acoustic waves can travel long distances and are used for relaying information over kilometers. Hence these are some inherent characteristic of acoustic communication like, limited bandwidth ( 0.4
if
f < 0.4
(4.4)
α( f ) is calculated using absorption loss formula as following:
α( f ) = 10
αdB ( f ) 10
(4.5)
Absorption coefficient is function of depth difference l and absorption loss given in following equation. Abc = l × αdB
(4.6a)
Scattering loss is logarithmic function of depth difference l given in following equation. Sc = k × 10 log(l)
(4.6b)
where k is spreading coefficient with value 1.5 for practical spreading. Total attenuation A(l,f) is the sum of Absorption coefficient (Abc ) and Scattering coefficient (Sc ) given by following formula.
A(l, f ) = Abc + Sc
(4.6c)
Ambient noise is calculated with adding the following four noises, turbulent noise Nt ( f ), shipping noise Ns ( f ) , wind noise Nw ( f ) and thermal noise Nth ( f ). These 45
noises are calculated using following formulas. 10 log(Nt ( f )) = 17 − 30 log( f )
(4.7a)
10 log(Ns ( f )) = 40 + 20(s − 0.5) + 26 log( f ) − 60 log( f + 0.03)
(4.7b)
10 log(Nw ( f )) = 50 + 7.5ω 1/2 + 20 log( f ) − 40 log( f + 0.04)
(4.7c)
10 log(Nth ( f )) = −15 + 20 log( f )
(4.7d)
N( f ) = Nt ( f ) + Ns ( f ) + Nw ( f ) + Nth ( f )
(4.8)
Passive sonar equation is used to find out Source Level SL to find intensity of transmitted signal IT using following formula: IT = 10SL/10 × 0.67 × 10−18
(4.9)
Transmission power of source is PT (d)
PT (d) = 2π × 1m × H × IT
(4.10)
Energy consumed in transmitting the k bits of packets is given as:
ET x (k, d) = 2PT (d) × TT x
(4.11)
where TT x is the transmission in seconds.
4.4.2
Monterey-Miami Parabolic Equation
Monterey-Miami Parabolic Equation is also an efficient and important formula to calculate the path losses in underwater communication environment. It consist of quite complex formulas mainly dependent on the depth, transmitted frequency and distance between two nodes. The basic formula of MMPE consist of sum of differ46
ent chunks and is given in equation 4.12.
PL(t) = m( f , d, ds , dr ) + ω(t) + n()
(4.12a)
Where parameters are defined below: PL(t): Propagation Loss between two nodes. m(): Propagation loss with arbitrary and recurring factor; found from retrogression of MMPE information. f: Transmitted signal frequency in kHz. ds : Depth of sender node in meters. dr : Depth of receiver node in meters. d: Distance between sender and receiver. ω(t):Signal loss estimate due to wave passage n(): Signal loss estimate due to arbitrary and random noise MMPE model estimates the propagation loss with random and regular, periodic components in the first slice of the equation. It performs nonlinear regression on the data to obtain A(n) coefficients. Therefore, m( f , d, ds , dr ) function computes the propagation loss as follows:
d A0 A9 A7((ds −dr )2 )A10 ! ( ) ds d + m( f , d, ds , dr ) = log 0.9 (d × dr )10A5
f2
! A1 41A1 d + + 0.002 + 0.003 × +A16 × dr + A8 × dA6 × dr + A8 × d 1 + f 2 4100 + f 2 914 (4.12b)
MMPE model approximates the losses due to wave mobility in the subsequent portion of the equation. It counts the sinusoidal mobility of the water molecules around the acoustic signal. The ω(t) computed in equation 4.12c function considers wavelength, wave period and scale factor along with wave effects to approximate the signal loss caused by the wave motion. It can be mathematically expressed as:
ω(t) = h(λω ,t, dr , hω , Tω )E(t, Tω ) 47
(4.12c)
h(s): scale factor. λω : Wavelength of ocean waves. hω : Height of ocean wave in meters. dr : Receiver’s depth in meters. Tω : Wave period in seconds. E(): Wave effects in nodes function.
Since we have undertaken the continuous node movement in aqueous environment, the effect on wave height and wavelength depicted in h(s) is due to movement of receiver node. Scale factor is estimated using equation in []. Movement of signal is also scaled by distance given in h(s).
h(Tω , λω ,t, hω , dr ) =
r (hω (1 − ( 2d λω )))
0.5
!
2π(mod(T )) ω × sin( ) Tω
The mathematical expression e() in the last slice of MMPE basic equation is for random background noise. Random noise function considers random Gaussian distribution. It depends on the ratio of distance between communicating nodes and source transmitter range. n() = 20(
d ) RT × NR
(4.12d)
n(): random noise function. d: Euclidean distance between sending node S and receiving node R in meters. TR : transmission range of source in meters. NR : random number from a gaussian distribution with 0 mean and 1 variance.
4.5
Improvements in Terms of Forwarding Function
Forwarding function is the criteria for a node to decide whether to forward the delay sensitive packets or not. Forwarding decision done by each node irrespective of its 48
own type. This means a normal node would not broadcast its own packet to sink but if a delay sensitive packet is received it would also be eligible for forwarding and forward the packet if it comes out to be the best forwarding node. Likewise iAMCTD forwarding function in ESDR is defined differently for different depth nodes. SQI based forwarding function is redefined to ensure the comparable values of forwarding function. SQI 0 =
log10 (LSNR) × Er × RT li
(4.13)
SQI based forwarding function range is always in interval [0 to 491.91]. Upper limit is far less than defined in previous schemes and is more comparable to the range of ECF and DDF based forwarding function. ECF based forwarding function FF is directly proportional to Er and inversely proportional to depth difference previous and current node depths D p − Dc is defined below: ECF 0 = [pv ×
RT ]Er D p − Dc
(4.14)
where RT is transmission range Er is remaining energy of a certain node. Forwarding function is directly dependent on remaining energy weighted by a factor given in square brackets whose range is described as follows: Neighbour selection process and depth threshold ensure that Dth < (D p − Dc ) < RT . Initially Dth is set to 60m and is subsequently changed to 40m and 20m after certain percentage of deaths, and RT is constant=100. So for above mentioned values factor in square brackets in equation 4.14 has range, from 1 to 1.66 for dth = 60, from 1 to 2.5 for dth = 40 and from 1 to 5 for dth = 60. This eventually gives value of forwarding function always between 0 to 5 × Eo which is comparable with the range of SQI’. DDF is also modified taking newly defined and linearized version of SQI. SQI × li DDF 0 = Di 49
(4.15)
SQI based forwarding function is employed when depth of nodes is less than D01 = 250m, ECF based forwarding decision is used if node is in between D01 = 250m and D02 = 750m. Nodes with depth more than D02 forward the packet using DDF based forwarding function.
4.6
Improvements in Terms of Holding Time
Holding time is given to nodes with lesser values of respective forwarding function. Holding time calculation is defined by the formula given in equation. 0
HT = (1 − FF )HT
(4.16a)
HT is the maximum value of holding time. It is defined in following equation as the ratio of consumed energy per unit power.
HT =
Eo − Er PT
(4.16b)
So if a certain node has less value of remaining energy it would have higher value of holding time and refrain to forward the packet.
4.7
Mobility of Sink
In conventional terrestrial wireless sensor networks mobile sinks are deployed to collect data from relatively farther nodes [78, 79]. This data collection reduces the forwarding load on intermediated nodes employed in multi hop communications It also provides variety of possibilities for routing in a network. The role of these data collecting devices is of prime importance otherwise all the data is to be forwarded in multi-hop fashion. Hence courier nodes and AUV are employed for data gathering applications so they are very crucial for underwater networks [80]. Courier 50
nodes can move vertically up and down and there is no facility of horizontal movement while AUV can be moved to any desired place with proper tour planning [81]. Anyhow there is no energy constraint on both of these devices. Mobility of courier nodes has not been exploited to its full potential in previous work. In previous scheme mobility of these nodes starts after certain percentage of death. Attaining the global knowledge of deaths and updating that to CNs itself has communication overhead. Additionally employing a resource in deep water and waiting for some certain level of calamity is not a wise option. Mobility of these nodes is needed to be exploited as per network requirements. So it is necessary to plan a better mobility pattern of CN to get full use of it. We have reconsidered the deployment and mobility of CNs. Moreover event segregation dictates and selects limited number of nodes to forward to CNs. Hence mobility of courier node is influenced by density of normal nodes and their rate of packet generation. In our proposed environment horizontal movement is not required while vertical movement of each courier node is explained in two different ways.
4.7.1
Synchronized and Uniform Mobility of AUV
In this type of mobility four courier nodes deployed on four different depths move in elliptical path. The motion is uniform in the sense that speed of each node remains same in its sojourn tour on elliptical paths. By the term synchronized mean that phase relation between any two courier remains same throughout the tour as shown in Figure 4.4a. Elliptical path of courier node i in yz plane is defined in following equation. (z − ki )2 (y − hi )2 + =1 a2 b2
(4.17a)
Elliptical path is planned in such a way that two ellipses have some overlapping region. This allows better coverage to the whole region and also facilitates to respective lower courier node to send its data to upper node. If the length of overlapping 51
50 AUV 1
AUV 3
AUV Sojourn AUV
0
AUV 2
AUV 4
-50 0
100
200
300
400
500
600
700
800
900
1000
Depth-(m)
(a) Uniform mobility of AUV 50 AUV 1
AUV 3
AUV Sojourn AUV Very Critical Node Critical Node Normal Node
0
AUV 2
AUV 4
-50 0
100
200
300
400
500
600
700
800
900
1000
Depth-(m)
(b) Uniform mobility of AUV with node deployed nodes
Figure 4.4: Synchronized mobility of AUV region on z axis is x vertex a is related with x by following equation: 3 a = x + 125 8
(4.17b)
This relation reveals that for x = 0 a becomes equal to 125m making length of major axis of the each ellipse to 2a = 250. x is subsequently changed to 20m and 40m making a = 133m and 140m. The resulting ellipses are shown in Figure 4.4. In this type of mobility vertex length a = 140m and co-vertices length b = 50m are used. In order to calculate time period of a node it is necessary to have knowledge of perimeter (circumference) of an ellipse. There is no straightforward formula to calculate this and following infinite series is utilized. 2 0.5 p = π(a + b) ∑ hn n n=1 ∞
52
(4.17c)
The parameter h is defined as follows:
h=
(a − b)2 (a + b)2
(4.17d)
We have employed the following formula by great Indian mathematician Srinavasan Ramanejan [82] to calculate the perimeter of the each elliptical.
p ≈ π[3(a + b) −
p (3a + b)(a + 3b)]
(4.18)
Time period of each nodes is dependent on uniform tangential speed of courier node during its sojourn tour. In synchronized and uniform mobility pattern speed of a node i is vi making time period:
Ti =
p vi
(4.19)
Mobility of CN is envisioned in such a way than CN1 is in phase with CN3 and CN2 is in phase with CN4 . Initially CN1 and CN3 are at bottom end of their respective elliptical paths and CN2 and CN4 are at the top end of their corresponding elliptical paths. Every node moves in clockwise direction and reaches to next destination after time Ti /4. In this way a node in its one revolution has four points to stop each after phase angle of θ /2. Their motion is synchronized in such a way that when i 1 2 3 4
hi 0 0 0 0
ki 140 380 620 860
Table 4.1: Centers of elliptical paths CN1 is about the top. Each node moves upward to their predefined destination, transfer data to next CN, and move back to initial stage. This motion pattern is synchronized in such a way that when a certain courier node is about to reach its destination next forwarder has just started its tour, first node find next CN to take its data to next CN.
53
4.7.2
Adaptive Mobility of Courier Nodes
In this mobility pattern node may accelerate or decelerate its motion on the basis of node density and data generated requirements. In this motion node moves up and down on a vertical line and its speed is influenced by node density and rate of traffic generated by normal nodes. Mobility of CN1 and CN3 are in same direction while CN2 and CN4 are in same direction. But both pairs of courier node move opposite to each other to ensure that these nodes must overlap and communicate at the overlapping area. Motion of a courier node c is governed by the following 50 AUV or CN Sojourn AUV or CN
0 CN1
CN2
CN3
CN4
-50 0
100
200
300
400
500
600
700
800
900
1000
Depth-(m)
(a) Adaptive mobility of Courier Nodes 50
AUV Sojourn AUV Very Critical Node Critical Node Normal Node
0 CN1
CN2
CN3
CN4
-50 0
100
200
300
400
500
600
700
800
900
1000
Depth-(m)
(b) Adaptive mobility of Courier Nodes with deployed nodes
Figure 4.5: Adaptive mobility of AUV/CN equation. Let a courier node c is at position yc ; its new position yt after time t can be determined by following equation. dy2c 1 dyc 1 + + yc = 2 × yt 2 dt t dt t
(4.20)
In this equation left term (second degree differential term) corresponds the acceleration of courier node c. If this value is set to zero motion becomes to unform. For 54
adaptive mobility of the nodes this factor is dependent node density in the upcoming coverage region of courier node c. Upcoming coverage regions is double of the transmission range of courier node. This process is explained as: Initially turns its transmission range to double of the present values and broadcast its arrival to its future region. Receiving nodes acknowledge with their depth information, rate of data generation and elapsed time since their previous transfer of data to any courier node. Courier node calculate the mean of the depth information of only those nodes which are currently not in its range. This provides that spatial information to estimate its next possible location in its sojourn tour. It also utilizes elapsed time data to calculate the allowed time to reach the estimated location. Using the depth difference between current depth and estimated depth; and remaining time node courier node decides new velocity to reach at next location. 1 Nd Di yt = Nd ∑ 1
(4.21)
where Nd represent total number of data generating nodes farther than current transmission range of courier node Node utilizes elapsed time and average traffic generation of all nodes Nd to determine remaining time tr to reach new location. This remaining time tr along with new location distance yt are utilized to estimate what the next courier node velocity vc would be optimum.
vc =
yt tr
(4.22)
Variations of vc during its sojourn tour make sure the adaptive and accelerated mobility of courier nodes for reaching at optimum new location in time.
55
CHAPTER 5
Performance Evaluation In this chapter we present the simulation setup and performance comparison of the ESDR with contemporary schemes. This chapter is summarized in the following two points. • In the first section simulation setup with necessary parameter is explained. • The second section of this chapter is dedicated for performance evaluation. Initially evaluation metrics are defined and then each one is compared with other protocols.
5.1
Simulation Setup
We have considered the deep underwater environment of volume 100×100×1000m3 in which 224 nodes are randomly deployed. Analytical calculations are programmed in MATLAB using channel model equations. Generally in UWSN nodes can be divided into two major groups on the basis of traffic generation. In some applications only a fraction of all deployed nodes are event sensing nodes which can generate data packets. Rest of the nodes are used to forward or broadcast those packets. In other group all the deployed nodes can sense and communicate at a time. Number of nodes in former group can be fix and preselected based on their depth, or can be varying and adaptively selected based on some threshold value of the sensed attribute. We have chosen the second group in our simulations in which all the deployed 224 nodes are data generating nodes and also act as forwarder to the received data packets. In simulation scenario each node is allowed to generate exactly one packet in one round. Simulation setup for random event generation of ESDR can be with many different 56
possibilities. What would be the type of a certain node and how many number of nodes would normal, very critical and critical. This number can be fixed or varying (as in present case we have chosen fixed number of node in each type). For example in this scenario nodes are selected randomly as critical, very critical and normal. This election is repeated after 100 rounds however it is ensured that total number of nodes in each type remains constant. So every time in a network ratio of very critical, critical and normal nodes is constant and it remains uniform for the whole lifetime of network. One can argue about varying number of delay sensitive nodes because who knows how much events can be delay sensitive. But here in order to simplify the analysis and simulation setup, so far constant ratio of delay critical nodes is used. We have compared the performance of ESDR with DBR, EEDBR and iAMCTD. In order to ensure fair comparison all the comparing schemes are implemented in same sensing volume. Necessary parameters with their values are given in Table 3.1.
5.2
Performance Evaluation Metrics
In order to compare the performance of underlaying schemes following evaluation metrics are considered. Stability period: Time from the start of network operation till the death of first nodes is termed as stability period, whereas time from the death of first node till the death of last node is termed as instability period. Halflife: Halflife of network is defined as the time till the death of half nodes. Lifetime: Lifetime of network is the time till the death of all nodes. Average end to end Delay Average end to end delay is mean of accumulated sum of propagation and transmission delays of all nodes. Throughput: Throughput is considered in terms of following two aspects: Packet sent to and received at sink: Number of packets sent to sink are depen57
dent on type of application. In event driven or threshold sensitive applications some selected nodes are packet generator nodes, while in other type all the nodes have equal probability to generate the packet after some regular interval and sent to sink. Number of packets successively received at sink is less than number of packets sent to sink. PDR: Packet Delivery Ratio: PDR is the ratio of number of packets received at sink to number of packets sent to sink. In ideal case where no packet is dropped PDR is equal 1, otherwise its value is less than 1 and greater than 0. Transmission LossTransmission loss is the average decrease in sound energy/intensity level as it propagates through water. Each metric is explained in details in the following subsections.
Table 5.1: Parameters values used in simulations Parameter Name Initial Energy (Eo ) Volume of the Network Data Packet Control Packet Number of nodes |N | Number of courier nodes |C | Maximum Depth (Dmax ) First Depth Margin (D01 ) Second Depth Margin (D02 ) First Death Margin (Nth1 ) Second Death Margin (Nth2 ) Depth threshold 1 (dth1 ) Depth threshold 2 (dth2 ) Depth threshold 3 (dth3 ) Transmission Range (RT ) Priority value for very critical nodes(pvc ) Priority value for critical nodes(pcr ) Priority value for normal nodes (pvc ) pvc pcr pno Speed of acoustic signal
5.2.1
Value 70J 100 × 100 × 1000× 64B 8B 224 4 1000m 250m 750m 75 150 60m 40m 20m 100m 2 1.5 1 0.111 0.222 0.666 1500m/s
Network Lifetime
Network lifetime is very crucial evaluation metric for the progress of any UWSN scheme. It is always required to prolong the lifetime with some efficient routing decisions considering the affecting parameters. Network lifetime of the underly58
ing schemes is given in Figure 5.1. Stability period, halflife and lifetime of all comparing schemes are also given in Figure 5.2. Four bars in three groups are for stability period, halflife and lifetime of their respective scheme. Stability period of ESDR is greater than iAMCTD after that iAMCTD shows somewhat stable behavior and there occurs rapid deaths in ESDR. This is due the difference of courier node mobility strategy between iAMCTD and ESDR. In iAMCTD courier nodes mobility is associated with the certain percentage of deaths. Hence courier nodes come into action as nodes have depleted a big portion of their energy and about to die out. Courier nodes data collection reduces large distance transmissions and bring somewhat stable behaviour in network. Anyhow this rescue mechanism could not prolong lifetime to very much extent. Halflife of both schemes is almost similar but after that iAMCTD nodes die out very rapidly whereas ESDR prolongs its lifetime to appreciable extent. Motion of courier nodes in ESDR is influenced by relative distribution and data generation of normal nodes. This motion is cyclic in nature in which, in each new cycle courier node utilizes record of its previous sojourn stay and estimates the better new position. This gradual improving mobility mechanism results into the deterrent behaviour of network. This type of mobility strategy may not stop the deaths but the lifetime of more important nodes (with higher rate of data generation) is prolonged while the death of lesser important nodes is tolerated for the overall benefit of network. iAMCTD nodes die out very early due to lack of network healing mechanism. Over all comparison proves that ESDR outperforms iAMCTD, EEDBR and DBR by considerable margin.
Since all the comparing
schemes have their own lifetime which is dependent on their network characteristics. Hence lifetime cannot serve as a fair independent quantity for comparison of remaining evaluation metrics. So in order to have a common independent quantity of same length we have taken certain but equal number of ascending samples of each respective metric, from the timeline of all the comparing scheme. Then these taken samples of that metric are plotted and compared. So samples in timeline of 59
250 DBR EEDBR iAMCTD ESDR
200
Nodes
150
100
50
0 0
0.5
1
1.5
2
2.5
3 4
Timeline
×10
Figure 5.1: Lifetime 3
×104
29941
DBR EEDBR iAMCTD ESDR
2.5
2
Rounds
17412
1.5 13059
12984
1097210552
1
1798
1646
1227
2999
5215
0.5
4099
0 Stability
Halflife
Lifetime
Figure 5.2: Stability Period, half-life and life-time following figures actually refers to the equally spaced and same number of samples of that evaluation metric taken in each scheme own lifetime. Stability period of 60
ESDR is 17.68% more than iAMCTD while halflife is 3% greater, and lifetime is 29.36% is greater than iAMCTD. Over all comparison proves that ESDR outperforms iAMCTD, EEDBR and DBR by considerable margin.
5.2.2
Average end to end delay
Average end to end delay is calculated using following procedure in each schemes. Two type of delay constitutes in operation of model. Transmission delay: Transmission delay caused by a node n to a packet of length l is given by following formula. l r
Dtn =
(5.1)
Where r is data rate with value 250000 bps. Propagation delay: Major part of delay is constituted by propagation delay given by following equation. Propagation delay of an acoustic signal moving with velocity v between a sender i and receiver j separated by distance si, j is given by:
Di,p j =
si, j v
(5.2)
Speed of acoustic signal is given in Table. Sum of both type of delay constitute in one hop delay than sum of all hops makes total delay in a packet transmission from a certain node it is also termed as end to end delay. i, j
Dni, j = Dt + Dip
(5.3)
Delay for by all nodes in a round are measured and stored in a vector and then mean of this gives average end to end delay. In this way average end to end delay in round is calculated and recorded for the whole network lifetime.
Dne2e
hmax
=
∑ Dh h=1
61
(5.4)
n
De2e =
max Dne2e ∑n=1 nmax
(5.5)
0.6
Delay (Seconds)
0.5
0.4
0.3
0.2 DBR EEDBR iAMCTD ESDR
0.1
0 2
4
6
8
10
12
14
16
18
20
Timeline (Samples)
Figure 5.3: Average end to end delay: Comparison From this vector of whole lifetime twenty samples are taken in ascending order and plotted is shown in Figure 5.3. It is clear from Figure that ESDR perform better than its counterparts in terms of average end to end delay. Packet segregation causes significant decrease in delay. ESDR put comparatively lesser forwarding overhead on nodes and hence these nodes causes less retransmission and delay. It is also important to note that delay in DBR is lesser than EEDBR as EEDBR selects forwarders on the base of energy so a node with lesser depth but more energy can become forwarder, and eventually involving more number of nodes in forwarding process, which eventually accumulates into larger transmission delays.
62
0.4 0.35
Delay (Seconds)
0.3 0.25 0.2 0.15 0.1 Very critical Critical Ordinary
0.05 0 2
4
6
8
10
12
14
16
18
20
Lifetime (Samples)
Figure 5.4: Delay comparison in ESDR
5.2.3
Throughput
Throughput of the entire network is measured in terms of total number of packets received at BS. The comparison for total number of packets successively received at BS is shown in Figure 5.5. ESDR outperforms its counterparts in terms of throughput by considerable margin. This is because packet loss in ESDR is minimum due to packet segregation strategy. Two third of generated packets are sent to courier nodes available in underwater environment. There is very minute packet loss in this transmission since it requires maximum one hop communication. Only one third of total generated packets are broadcasted to surface sink. In previous scheme holding time value was depending on forwarding function which itself was having inconsistent values. In ESDR Improved forwarding function results into optimum values of holding time which eventually ensures decreased collision and lesser packet loss. So overall comparison, depicted in Figure 5.5 results into increased throughput of ESDR. Another parameter to get the insight in terms of throughput is Packet De63
3
×10
6
2.5
Packets
2
1.5
1 DBR EEDBR iAMCTD ESDR
0.5
0 2
4
6
8
10
12
14
16
18
20
Timeline (Samples)
Figure 5.5: Throughput livery Ratio (PDR) shown in Figure 5.6. PDR throughout the lifetime is calculated down-sampled and plotted for all comparing schemes. Improved forwarding function and efficient adaptive mobility of courier node cause the increase PDR value for proposed scheme. Moreover since approximately 66% of the generated packets are transmitted to courier nodes which may cause delay but ensure reliable delivery of packets destinations resulting with improvement in PDR and throughput as in Figure 5.6.
5.2.4
Transmission Loss
Transmission loss is dependent on; number of transmissions that a packet undergo in multi-hop communication, attenuation loss of the signal, and bandwidth efficiency. It is calculated using thorp attenuation formula given in Equation 4.3. Major contribution to transmission loss is the spreading of the acoustic wave as it propagates away from the source for longer distances in underwater and cause 64
1 0.9 0.8 0.7
Ratio
0.6 0.5 0.4 0.3 0.2
DBR EEDBR iAMCTD ESDR
0.1 0 2
4
6
8
10
12
14
16
18
20
Timeline (Samples)
Figure 5.6: Packet Delivery Ratio greater transmission losses. Transmission loss for all comparing schemes is calculated and samples from whole lifetime taken in ascending order are plotted in Figure 5.7. ESDR performs best among all comparing scheme in terms of transmission loss as well. This is due the fact that a major part of data generated is transferred to patrolling sinks and is not suffered to long distances spreading losses. Moreover premature death of intermediate nodes in previous schemes cause increase in transmission loss for their respective protocols.
5.2.5
Path Loss
Path Loss in underwater is dependent on the distance or depth difference between two communicating nodes. Path Loss is affected due to wave movement and is calculated using MMPE model explained in previous chapter. Nodes in ESDR undergo comparatively lesser distance communication due to proper availability of courier nodes and efficient forwarding plan. Hence in ESDR path loss values are 65
80
Transmission Loss (dB)
70 60 50 40 30 DBR EEDBR iAMCTD ESDR
20 10 0 2
4
6
8
10
12
14
16
18
20
Timeline (Samples)
Figure 5.7: Transmission Loss comparatively lesser than its counter parts for the whole time line of each respective scheme as shown in Figure 5.8. On the other hand in DBR and EEDBR broadcasting load on intermediate nodes cause their premature death which result larger distance communication and eventually increase in path loss values. Path Loss is iAMCTD is more than ESDR because mobility of sinks in iAMCTD starts after certain percentage of deaths; before their motion node have to suffer longer distance communications. Adaptive mobility of sink ESDR ensure comparatively lesser distance communications making lesser values of path loss.
66
100 90 80
Path Loss (dB)
70 60 50 40 30 DBR EEDBR iAMCTD Proposed
20 10 2
4
6
8
10
12
Timeline
Figure 5.8: Path Loss
67
14
16
18
20
CHAPTER 6
Conclusion and Future Work In this work, we have proposed a novel and energy efficient approach to accommodate delay sensitivity requirements with increased network lifetime for Under Water Sensor Network. We have formulated a strategy in which three types of events with different probability of occurrence are generated and their efficient forwarding is planned. Forwarding functions are analyzed and redefined and holding time calculation process is also improved. However probability of occurrence for each type of event remains unchanged throughout the lifetime. In future we intend to implement event segregation approach with varying provability of occurrence of normal, critical and very critical events. In this work generated events type (and hence node type) is randomly selected but in future type of event selection is intended to be related with it depth, remaining energy or any other relevant parameter.
68
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Abbreviations UWSNs: Underwater Wireless Sensor Networks DBR: Depth Based Routing EEDBR:Eergy Efficient Depth Based Routing IAMCTD:Improved Adaptive Mobility of Courier nodes in Threshold-optimized DBR ESDR: Event Segregation based Delay sensitive Routing MANETs: Mobile Ad-hoc Networks LSNR: Localized free Signal to Noise Ratio LASR: Localized Aware Source Routing SQI: Signal Quality Index DDF: Depth Dependent Function ECF: Energy Cost Function AUV: Autonomous Underwater Vehicle CN: Courier Node MMPE: Monterey-Miami Parabolic Equation
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