by
Eylem İlker Oyman
B.S. in Computer Engineering, Boğaziçi University, 1993 B.S. in Mathematics, Boğaziçi University, 1993 M.S. in Computer Engineering, Boğaziçi University, 1996
Submitted to the Institute for Graduate Studies in Science and Engineering in partial fulfillment of
the requirements for the degree of Doctor of Philosophy
Graduate Program in Computer Engineering Boğaziçi University
2004
MULTIPLE SINK LOCATION PROBLEM AND ENERGY EFFICIENCY IN LARGE SCALE WIRELESS SENSOR NETWORKS
APPROVED BY:
Prof. Cem Ersoy ...
(Thesis Supervisor)
Prof. M. Ufuk Çağlayan ...
Prof. Bülent Örencik ...
Assoc. Prof. Can Özturan ...
Assist. Prof. Murat Zeren ...
DATE OF APPROVAL ...
ACKNOWLEDGEMENTS
It has been a long story since this work had started and until it could receive an end.
Many valuable people have contributed to this thesis, not only academically but also emotionally.
First of all, I would like to thank to the professors in my thesis jury, Prof. M. Ufuk Çağlayan, Prof. Bülent Örencik, Assoc. Prof. Can Özturan, and Assist. Prof. Murat Zeren for their valuable comments and directions. Prof. Cem Ersoy, my thesis advisor, helped me to focus on the work and showed me the ways of being academically productive.
The long running PhD study had many bureaucratic obstacles. However, our dear secretary Sevgi Dikmen was always able to find a clean solution. Without her helps, I could not survive in the jungle. Many special thanks…
The people in the Netlab were always close, friendly and helpful. Their innovative ideas have raised the value of this work. Especially, Kaan Bür, my friend, roommate, neighbor, and travel-mate… I will never forget the taste of those repetitive lunches. In addition, Dr. Roy Küçükateş, being my partner since the stone-age, was really patient in the business and also helpful, especially in the early stages of my Opnet work.
Finally, I want to thank to my family. I felt their blessing, support, encouragement and love always with me. And, my Esra, my dear wife… She is my light in the darkness, my oasis in the desert, my rescue island in the ocean. Having found her, the life has become a meaning.
ABSTRACT
MULTIPLE SINK LOCATION PROBLEM AND ENERGY
EFFICIENCY IN LARGE SCALE WIRELESS SENSOR NETWORKS
Energy is the most critical resource in the life of a wireless sensor node. Therefore, its usage must be optimized to maximize the network life. Besides using power adjustable transmitter circuitry, usage of multi-hop communication links should be considered to save energy. Moreover, in large-scale networks with a large number of sensor nodes, multiple sink nodes should be deployed, not only to increase the manageability of the network, but also to reduce the energy dissipation at each node. In this thesis, we introduce problems that are related with locating multiple sink nodes in the sensor network area. We give a framework consisting of new formulations and definitions for the multiple sink sensor networks.
Then, we investigate the use of multi-hop communication links and compare the amount of energy gain upon alternative routes using analytical techniques. We show that employing multi-hop links does not always result in energy gain, and try to quantify situations when it is advantageous. We also show that neglecting the overhead energy and overemphasizing the importance of power adjustable transmitter circuitry could result in considerable energy loss. The analytical results are validated using simulations on different scenarios.
Then, we focus on the multiple sink location problems in large-scale wireless sensor networks. We propose a mathematical formulation for sensor networks to calculate the energy dissipation throughout the network. Then we state different problems depending on the design criteria. Finally, we consider locating sink nodes to the sensor environment, where we are given a time constraint that states the minimum required operation time. We use simulation techniques to test our solution.
ÖZET
TELSİZ DUYARGA AĞLARINDA BİRDEN FAZLA MERKEZ YERLEŞTİRME PROBLEMİ VE ENERJİ VERİMLİLİĞİ
Kablosuz algılayıcı aygıtlarının ömürleri açısından, enerji en önemli kaynaktır. Bu yüzden, ağın ömrünü en üst düzeye çıkarabilmek için, enerjinin kullanımı en iyi şekilde yönetilmelidir. Enerji tasarrufu için, çıkış gücünün ayarlanabildiği verici devrelerini kullanmanın yanı sıra, çok zıplamalı konuşma hatları kullanılmalıdır. Bununla birlikte, çok fazla sayıda algılayıcıdan oluşan büyük ölçekli algılayıcı ağlarında, veri toplamak için birden fazla merkez kurulmalıdır. Bu sayede, hem ağ daha kolay yönetilebilecek, hem de her bir algılayıcının enerji harcaması azaltılmış olacaktır. Bu tezde, birden fazla merkez düğümün algılayıcı ağı alanına yerleştirilmesi ile ilgili problemleri ortaya çıkardık. Birden fazla merkezli algılayıcı ağları için yeni ifadeler ve tanımlar içeren bir çerçeve oluşturduk.
Daha sonra, çok hoplamalı konuşma hatlarının kullanılmasının enerji harcamasındaki etkisini inceleyerek, alternatif rotalardaki kazançları analitik tekniklerle karşılaştırdık. Bu sırada, çok hoplamalı konuşma hatlarının kullanılmasının her zaman enerji kazancını sağlamadığını gösterdik. Bunun yanı sıra, her hoplamada harcanan fazla enerjinin göz ardı edilmesi ve çıkış gücünün ayarlanabildiği verici devrelerinin öneminin gereğinden fazla önemsenmesi durumunda büyük enerji kayıplarının oluştuğunu gösterdik. Analitik sonuçları, değişik senaryolar üzerinde çalıştırdığımız benzetim yöntemleriyle karşılaştırdık.
Daha sonra, büyük ölçekli algılayıcı ağlarındaki birden fazla merkez yerleştirme sorunlarını inceledik. Ağ üzerindeki enerji harcamalarını hesaplayabilmek için matematiksel bir formülasyon önerdik. Daha sonra, tasarım kriterlerine göre değişebilecek farklı sorunları listeledik. Son olarak, algılayıcı ağı için verilecek en az çalışma süresi kısıtını sağlayacak en az sayıda merkezin ağa yerleştirilmesi sorunu incelendi. Çözüm önerisi benzetim yöntemleriyle sınandı.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ... iii
ABSTRACT...iv
ÖZET...v
LIST OF FIGURES...x
LIST OF TABLES ...xiv
LIST OF SYMBOLS/ABBREVIATIONS ...xv
1. INTRODUCTION...1
1.1. Contribution of the Thesis...2
1.2. Structure of the Thesis ...3
2. WIRELESS SENSOR NETWORKS...5
2.1. Current Sensor Motes...5
2.2. Sample Scenarios ...7
2.3. Location Awareness...9
2.4. MAC Layer Interface ...9
2.5. Routing Technique...11
2.6. Packet Structure ...12
2.7. Energy Model...14
2.7.1. Transmitter Power Model ...15
2.7.2. Energy Consumption ...16
3. MULTIPLE SINK SENSOR NETWORK DEFINITIONS AND FORMULATIONS.19 3.1. Motivation...19
3.2. Formulation of the Multiple Sink Network Design Problem...20
3.2.1. Preliminaries ...20
3.2.2. Routing ...24
3.2.3. Path Length...26
3.2.4. Branch Nodes...28
3.2.5. Energy Dissipation...31
3.2.6. Counting the Packets ...33
3.2.7. Node Lifetime...37
3.2.8. Investment Cost ...38
3.3. Summary ...41
4. QUANTIFYING SAVED ENERGY BY MULTI-HOPPING ...42
4.1. Network Model ...43
4.1.1. Assumptions ...43
4.1.2. Multi-Hop Links ...44
4.2. Energy Saving...44
4.2.1. 1-D Communication Links ...45
4.2.2. Isosceles Triangular Communication Links ...48
4.2.3. Arbitrary Triangular Communication Links...50
4.2.4. Generalization...51
4.3. Simulations on the Energy Savings by Multi-hopping ...52
4.3.1. Simulation Setup...52
4.3.2. Results...54
4.4. Conclusions on the Energy Savings by Multi-hopping ...59
5. THE EFFECT OF OVERHEAD ENERGY TO THE NETWORK LIFETIME ...61
5.1. Motivation for Overhead Energy Considerations ...61
5.2. Simulations on the Effect of Overhead Energy ...63
5.2.1. Simulation Setup...64
5.2.2. Results...65
5.3. Conclusions on the Effect of Overhead Energy...68
6. MULTIPLE SINK SENSOR NETWORK DESIGN PROBLEM ...70
6.1. Design Criteria ...70
6.1.1. Number of Sinks ...70
6.1.2. Network Lifetime...71
6.1.3. Routing ...72
6.1.4. Cluster Members...73
6.1.5. Location of Sinks...73
6.1.6. Data Generation Rate...74
6.1.7. Energy Model ...74
6.2. Routing Decisions ...75
6.2.1. Minimum Energy Tree ...75
6.2.2. Minimize the Maximum Energy Dissipation at Sensor Nodes ...76
6.2.3. Minimize the Maximum Energy Path...77
6.2.4. Maximum Residual Energy Path ...78
6.3. Redeployment Scenarios...78
6.3.1. Random Redeployment ...79
6.3.2. Neighborhood Redeployment...79
6.3.3. Replacement ...79
6.3.4. Redundant Deployment ...79
6.4. Sink Location Problems ...80
6.4.1. Find the Best Sink Locations (BSL)...80
6.4.2. Minimize the Number of Sinks for a Predefined Minimum Operation Period (MSPOP) ...80
6.4.3. Find the Minimum Number of Sinks while Maximizing the Network Life (MSMNL) ...81
6.5. Differences with Concentrator Location Problem ...82
6.6. A Solution Technique for the MSPOP Problem ...83
6.6.1. Deployment of the Sensor Nodes ...83
6.6.2. Finding Location Information...84
6.6.3. Collecting the Location Information from the Field...84
6.6.4. Finding the Best Location for K Sink Nodes...84
6.6.5. Estimating the Network Lifetime ...84
6.7. Computational Experiments on Multiple Sink Sensor Network Problems...85
6.7.1. Simulation Setup...85
6.7.2. Demonstrative Example for the BSL Problem ...87
6.7.3. Application of the Solution Technique to the MSPOP Problem ...93
6.7.4. Conclusion for the Computational Experiments...97
7. CONCLUSION AND FUTURE WORK...99
7.1. Conclusion ...99
7.2. Future Work ...100
APPENDIX A: OPNET IMPLEMENTATION DETAILS ...101
A.1. Wireless Sensor Network...101
A.2. Node Model...102
A.3. Network Layer Process Model...104
A.4. Data Link Layer Process Model...105
A.5. Packet Structure ...107
REFERENCES...109
LIST OF FIGURES
Figure 2.1. Berkeley/Crossbow Mica motes compared with a US quarter (25 mm)...5
Figure 2.2. Smart Dust Motes (5 mm) ...6
Figure 2.3. General architecture of a sensor node ...6
Figure 2.4. Data delivery from source to the sink using intermediate nodes...12
Figure 2.5. Basic link layer packet structure...12
Figure 3.1. A large-scale sensor network with three clusters ...19
Figure 3.2. A path from the sensor i to the sink s through intermediate nodes j and k...21
Figure 3.3. (a) A sensor network graph, (b) Corresponding minimum energy tree ...22
Figure 3.4. The set of relay nodes of the path Pi→s ...26
Figure 3.5. The set of branch nodes of the relay node j...29
Figure 3.6. The packet generation interarrival times Zi( )n for the initiator node i ...35
Figure 4.1. Radio transmission with different power levels result in different transmission range...43
Figure 4.2. Using multi-hop links in routing decisions ...44
Figure 4.3. Routing decision alternatives, (a) direct communication, (b) and (c) using an intermediate node ...45
Figure 4.4. Energy saving in 1-D communication scenario ...46
Figure 4.5. Effect of α on energy saving in 1-D communication scenario...47
Figure 4.6. Energy saving in isosceles triangular communication scenario ...49
Figure 4.7. Effect of α on energy saving in isosceles triangular communication scenario...50
Figure 4.8. Arbitrary triangular communication scenario ...50
Figure 4.9. Energy saving in arbitrary triangular communication scenario ...51
Figure 4.10. Generalization into a multi-hop path...52
Figure 4.11. Average hop count versus overhead energy τ (A = 200 m x 200 m, α = 3) .54 Figure 4.12. Average node energy versus overhead energy τ (A = 200 m x 200 m, α = 3) ...55
Figure 4.13. Average node energy versus average hop count (A = 200 m x 200 m, α = 3, only Pcont nodes are used)...56
Figure 4.14. Average node energy versus overhead energy τ (A = 400 m x 400 m, α = 3) ...56
Figure 4.15. Average hop count versus overhead energy τ (A = 200 m x 200 m, only Pcont nodes are used) ...57
Figure 4.16. Average node energy versus overhead energy τ (A = 200 m x 200 m, only Pcont nodes are used) ...58
Figure 4.17. Average hop count versus path loss exponent α
(A = 200 m x 200 m, τ = 20 mJ, only Pcont nodes are used)...59
Figure 5.1. A sample network representing different topology alternatives for different path loss exponent α and overhead energy τ values...62
Figure 5.2. Average packet delivery energy versus overhead energy ...66
Figure 5.3. Average node energy versus overhead energy ...67
Figure 5.4. Average hop count versus overhead energy...67
Figure 5.5. Network lifetime versus overhead energy...68
Figure 6.1. System design algorithm ...83
Figure 6.2. Sample sensor network with 200 sensors and three sinks...88
Figure 6.3. Energy and disconnected region maps, until the 60th day ...90
Figure 6.4. Exhausted nodes versus time...91
Figure 6.5. Unreachable nodes versus time ...91
Figure 6.6. Unreachable nodes versus time using rerouting...92
Figure 6.7. Exhausted nodes versus time using rerouting ...92
Figure 6.8. Percentage of exhausted nodes versus time, with different number of sinks.94 Figure 6.9. Percentage of unreachable nodes versus time, with different number of sinks...94
Figure 6.10. Comparison of random placement with k-means algorithm, with three
sinks...95
Figure 6.11. Change in the number of sinks for different network lifetime requirements .96 Figure A.1. Sample wireless sensor network scenario ...101
Figure A.2. Sensor node model ...102
Figure A.3. Process diagram for the network layer ...104
Figure A.4. Process diagram for the data link layer ...106
LIST OF TABLES
Table 2.1. Optimal packet size in link layer...13
Table 2.2. Length of binary BCH codes with different t...14
Table 2.3. Path loss exponents for different environments ...16
Table 4.1. Simulation parameters ...53
Table 5.1. Average energy dissipation at sensor nodes...63
Table 6.1. Simulation parameters ...86
Table 6.2. Expected network lifetime, with ρ =0.25...95
LIST OF SYMBOLS/ABBREVIATIONS
ajk Adjacency matrix of the minimum energy tree T min A Set of arcs in the sensor network
bj Branch size of the relay node j
B s Set of branch nodes of the sink node s
s
B j Set of branch nodes of the relay node j
c Speed of light
D
c r Cost of deployment action of the rth redeployment
N
c r Cost of a sensor node at the rth redeployment
P
cs Cost of placement of the sink node s
S
cs Cost of the sink node s
C Total investment cost
CD Total cost of deployment action CN Total cost of sensor nodes
N
C r Total cost of sensor nodes at the rth redeployment CP Total cost of sink node placements
CS Total cost of sink nodes
d Euclidean distance
dij Euclidean distance between two nodes having indexes i and j D S Budget dedicated for the total sink investment
eij Energy cost of the arc ( ji, )
s
ei→ Total energy dissipation for a data packet on the path Pi→s ej Relay energy load of the node j
( )t
ej Total energy dissipation of the node j during the time interval (0,t].
ex Energy required for task x
( )t
Ej Residual energy of the node j at a given time t
f Frequency
G Directed graph representing the sensor network
Gx Antenna gain
K s Service capacity of the sink s
l Length of a packet in bits
lx Length of field x of a packet in bits
s
li→ Path length of the path Pi→s
( )t
niG Number of packets generated by the initiator node i
( )
tnRj Number of packets going through the relay node j n r Number of sensor nodes in the rth redeployment
N Set of sensor nodes
N r Set of sensor nodes in the rth redeployment p Bit error rate of the radio channel
is
p jk Path matrix of the tree T min
s
Pi→ Path from a sensor node i to a sink node s
min s
Pi→ Minimum energy path from the sensor node i to the sink node s Px Power required for task x
r Number of redeployments in a sensor network
is
r j Relay matrix of the tree T min
s
Ri→ Set of relay nodes of the path Pi→s
S Set of sink nodes
t Error correcting capabilities in binary BCH codes
t Time
T min Minimum energy tree
V All possible nodes in the network
s
Vi→ Vertex set of the path Pi→s
min s
Vi→ Vertex set of the minimum energy path Pimin→s
( )t
Xi Number of packets generated during the time interval (0,t]
( )n
Zi nth interarrival time of packets
α Path loss exponent
δE Energy saving
η Energy efficiency
λ Wavelength of the signal
µ i Expected value of the interarrival time of packets
( )t
ρ Sensor measurement reliability function
Threshold
ρ Predefined threshold for sensor measurement reliability
τ Overhead energy
ADC Analog to Digital Converter
BCH Bose-Chaudhuri-Hocquenghem codes
BSL Best Sink Locations
CA “Consider” Algorithm
CLP Concentrator Location Problem
EAR Eavesdrop-And-Register FDMA Frequency Division Multiple Access FEC Forward Error Correction
FSM Finite State Machine GPS Global Positioning System
IA “Ignore” Algorithm
IEEE Institute of Electrical and Electronics Engineers ISM Industrial, Scientific and Medical
ISO International Standards Organization
MAC Medium Access Control sublayer MEMS Micro Electrical Mechanical Systems
MSMNL Minimization of the number of Sink nodes while Maximizing the Network Lifetime
MSPOP Minimization of the number of Sink nodes for a Predefined minimum Operation Period
OSI Open Systems Interconnection
SMACS Self-organizing Medium Access Control for Sensor networks TDMA Time Division Multiple Access
1. INTRODUCTION
Wireless sensor nodes are combining the wireless communication infrastructure with the sensing technology. Instead of transmitting the perceived data to the control center through wired links, ad hoc communication methods are utilized, and the data packets are transmitted using multi-hop connections [1, 2]. Through advances in Micro Electrical Mechanical Systems (MEMS) technology small, low-cost, low-power electronic devices coupled with sensing and wireless communication capabilities are constructed. These devices form a self-organizing ad hoc network to forward data packets towards sink nodes.
There are several survey papers providing with in-depth background research on sensor networks [3-6].
The self-organization feature of sensors makes it feasible to deploy them randomly over the region being observed. Without needing a previous exploration, sensors might be installed to the environment in a random way, like dropping them from an aircraft. In this manner, a large number of sensor nodes are spread over the environment without having a prior knowledge of where each sensor is being placed individually.
The most critical resource in the sensor network is the available energy of the sensor nodes. Whenever the sensor nodes are not coupled with some energy-scavenging tools, the only energy resource of them will be their installed battery, and the sensors with exhausted batteries cannot operate anymore. Moreover, since sensor nodes behave as relay nodes for data propagation of other sensors to sink nodes, network connectivity decreases gradually [7]. This may result in disconnected subnetworks of sensors, i.e., some portions of the network cannot be reachable at all. Therefore, the level of power consumption must be considered at each stage in wireless sensor network design.
Sensor nodes have a short transmission range due to their limited radio capabilities.
Therefore, the data must be relayed using intermediate nodes towards the sink. In addition, it may be more advantageous to use a multi-hop path to the sink node consisting of shorter links rather than using a single long connection.
_In some applications, several thousands of sensor nodes might be deployed over the _monitored region. For example, in agricultural scenarios, in environmental monitoring _applications, such large-scale sensor networks would be necessary. Moreover, the _diameter of the region might easily be several kilometers. In this case, scalability of the _network becomes a very important design issue. In order to obtain a scalable network, the _sensor nodes should be divided into clusters. The nodes within a cluster will then be _connected to the sink nodes dedicated for that cluster. Besides finding the best number of _sink nodes, their optimum placement within the field is also an important point.
1.1. Contribution of the Thesis
In this thesis, we have introduced the “multiple sink sensor network design problem.” We have given a framework consisting of new formulations for the multiple sink sensor networks. Starting with the definitions of the sensor network, we have provided an infrastructure that is independent from the routing algorithm, which has been used within the derivations of the problem.
Then, we have investigated the usage of an intermediate node forming multi-hop links, and its effect on energy gain. We have focused on uniformly deployed sensor nodes, each having identical communication capabilities. The sensor nodes are assumed to be able to adjust their transmission power. Therefore, each sensor consumes only the amount of energy that will suffice to reach for the transmitted radio waves to the destined receiver antenna. We have studied different multi-hop communication scenarios and calculated the energy saving in each scenario. We have also expanded these scenarios to general cases.
The generalization can be applied into any arbitrary triangle and can be used in energy optimized route calculations. We also tried to quantify the effect of path loss exponent α, and overhead energy τ on energy saving. It is shown that the sensor lifetime can easily be doubled using power adjustable transmitter circuitry.
Thereafter, we have shown that neglecting the overhead energy during routing decisions could result in suboptimal energy usage. The effect of overhead energy is usually ignored in traditional ad hoc networks, where the transmission energy is much higher than the overhead energy. However, in sensor networks, due to short communication ranges, we
have to include the overhead energy to the overall energy cost in the routing calculations.
We have investigated the use of multi-hop communication links in routing and compared the amount of energy gain acquired by correct energy calculations. We show that neglecting the overhead energy and overemphasizing the importance of power adjustable transmitter circuitry could result in considerable energy loss.
Finally, we have stated characteristic features of the multiple sink location problems in large-scale wireless sensor networks. Several design issues including different design criteria, routing alternatives and redeployment scenarios are presented. The effect of locating sink nodes on the sensor environment regarding the total network lifetime is analyzed. The predefined constraints stating the minimum required operational time for the sensor network is incorporated with the design problems. Solution techniques that are finding the best sink locations and the quantity of the sink nodes are presented. Using demonstrative examples and simulations, these solution techniques are evaluated.
1.2. Structure of the Thesis
In the next chapter, we give a brief introduction on wireless sensor networks. We introduce first the sensor devices, how they physically constructed. After that, we present the underlying network architecture, and the energy model.
In Chapter 3, multiple sink sensor network framework is introduced. A mathematical formulation of the sensor networks are given, which is later used to represent the routing tree, to define the communication paths and relay sets, moreover, to calculate the overall energy dissipation in the network.
In Chapter 4, we provide a formulation to quantify energy saving using multi-hop communication links. The results are compared with simulations. We show that multi-hopping is not always advantageous, and formulate whenever to use multi-hopping.
In Chapter 5, we analyze the effect of overhead energy to the network lifetime. We show that neglecting the overhead energy during routing calculations could result in suboptimal routing trees, which causes higher energy dissipation at sensor nodes.
In Chapter 6, we introduce the multiple sink sensor network design problem. Several design criteria and objectives are presented. The effect of routing decisions and redeployment scenarios of sensor nodes are stated. Together with the definitions of sink location problems, a solution technique is also given. After that, computational experiments are presented. The energy map of the network and the map of unreachable region through its lifetime are presented within the simulation results.
Finally, we conclude the thesis, and provide some future research directions.
2. WIRELESS SENSOR NETWORKS
Industrial sensors are responsible to perceive a physical phenomenon in the environment. Thereafter, the data gathered through the sensors has to be forwarded to a control center for further processing. Instead of transmitting this data through wired links, wireless sensor nodes employ wireless communication technologies for data propagation.
Advances in technology enabled construction of small, low-cost, low-power electronic devices coupled with sensing and wireless communication capabilities. These sensor elements can easily build a self-organizing network for information propagation [1, 2].
There are several survey papers providing with in-depth background research on sensor networks [3-6].
In this chapter, application specific design issues are discussed. Besides the sample scenarios that this work could be applied, network specific technical details are also mentioned.
2.1. Current Sensor Motes
Recent advances in MEMS technology enabled small sized electronic devices coupled with sensor and communication equipment. The main focus on this production cycle is to achieve very low-cost devices.
Figure 2.1. Berkeley/Crossbow Mica motes compared with a US quarter (25 mm) [8]
The Berkeley/Crossbow Mica Motes (see Figure 2.1) has a size of a US quarter (25 mm) coupled with a multi-channel transceiver, on-board temperature sensor, and a processing unit [8]. The transceiver is capable to work on 898/916 MHz or 433 MHz Industrial, Scientific and Medical (ISM) bands where the radio power is programmable.
Figure 2.2. Smart Dust Motes (5 mm) [9]
Another successful implementation is the Smart Dust Motes (see Figure 2.2). These devices are communicating using laser beams, and are imagined to become one cubic millimeter of size [9].
Sensing Unit Processing Unit
Power Unit Power
Generator Location Finding
System Mobilizer
Processor
Transceiver Sensor ADC
Storage
Figure 2.3. General architecture of a sensor node, redrawn from [6]
The general architecture of a sensor node is shown in Figure 2.3 (redrawn from [6]).
The major components are sensing unit, processing unit, transceiver, and power unit. The environmental information is retrieved using the sensor and converted with an analog to digital converter (ADC) to digital data. This data is forwarded to the processing unit to
become a data packet that is to be sent to the sink node for further examination. The communication between the sensor nodes are carried out with the transceiver. The power unit feeds all these components with the necessary operational power.
The optional units, such as the location finding system, mobilizer and power generator may be embedded to the node depending on the application. Most of the applications require some location information for the sensed data when they reach the sink node. Mobility might also be an application-specific requirement. Although most monitoring applications utilize only static sensor nodes, for some tracking scenarios mobility might be a major design criterion. Finally, in order to prolong the lifetime of a sensor node, a power scavenging tool such as solar cells can be attached to the node.
2.2. Sample Scenarios
Wireless sensor networks have many application areas mentioned in the literature. A detailed list can be found in [6]. Moreover, some applications require a more detailed analysis, since there might be some application specific constraints to be considered.
The self-organization feature of sensors makes it feasible to deploy them randomly over the region being observed. Without needing a previous exploration, sensors might be installed to the environment in a random way, like dropping them from an aircraft. In this manner, a large number of sensor nodes are spread over the environment without having a prior knowledge of where each sensor is being placed individually. These sensors are assumed to be distributed uniformly in the environment. Two other deployment strategies are mentioned in [10]. The sensors may be regularly placed with some geometric topology depending on the application, e.g., a grid. They can also be placed with a prior knowledge of the phenomenon to be observed, resulting in a biased installation. In places where the phenomenon is more likely to occur or appears more densely, a higher amount of sensors might be necessary for an investigation that is more precise. In order to reduce the cost of deployment, a path exposure method is proposed in [11].
Having deployed the sensors in the environment, they start to observe the phenomenon. Data from the sensors might be gathered in different ways. First, the sensors
might continuously send reports to the sinks with an application-dependent predefined interval. Second, they might be polled by the control unit. In this case, all the sensors might be under consideration or only a small portion lying on the suspected region might be queried as well. In the first case, the query is spread using broadcast methods to the network, whereas in the latter case multicast communication techniques must be employed to save resources. Third, sensors can decide to send data when they observe a specific event [12, 13].
In many environmental applications like forest fire detection, soil erosion monitoring, air pollution measurements, or monitoring the saltiness level of the field, sensors are distributed randomly in the considered environment. Due to the extreme size of the area and application’s complete coverage needs, a very large amount of sensors must be deployed. In this case, scalability becomes a crucial issue. Therefore, the complete sensor network should be divided into clusters to achieve a more stable system [14, 15], [16]. In this manner, not only the system will be easier to manage, but also the total network lifetime will increase resulting in a more economical investment.
Several biomedical applications can also make use of wireless sensor nodes through incorporation of sensing materials with wireless communication circuitry, such as a glucose level monitor or retina prosthesis [17]. When we consider wireless networking of human-embedded smart sensor arrays, the design constraints are very different. The solutions should be ultra-safe and reliable, work trouble-free in different geographical locations, and require minimal maintenance. Another interesting application is habitat monitoring. In [ 18 ], seabird nesting environment is monitored. This experiment is accomplished using 32 sensor nodes on a small island streaming live data onto the web. In [19], the concept of “smart kindergarten” is introduced where developmental problem- solving environments for early childhood education are incorporated with wireless sensor networks. Here, sensor-enhanced toys and classroom objects are connected with back-end middleware services and database techniques.
2.3. Location Awareness
Since sensor nodes are spread randomly over the field, they initially do not know their exact locations. Many applications, however, require location information to achieve the desired functionality. Extracting location information from a Global Positioning System (GPS) module attached to the sensor is not a feasible solution [20, 21]. First, these devices are physically large and energy sensitive. Second, in many applications, sensors antennae cannot be in line-of-sight of the satellites. In addition, they are still very expensive devices, producing a costly solution for location estimation.
Although GPS cannot be a solution for location estimation problem, current research on this topic provided good alternatives. In [22], a centralized method is proposed. Using convex position constraints, which have been derived from the connectivity information, the position estimation is performed relative to nodes, whose locations information are known a priori.
In [21], a radio frequency technique is used to estimate location. Each beacon periodically signals overlapping location information to the network. Depending on the connectivity metric, nodes localize themselves to the centroid of their nearby beacons.
A similar technique is given in [23], where a collaborative multi-lateration technique is presented. Using this method, ad-hoc deployed sensor nodes can estimate their locations by using beacon locations that are several hops away and distance measurements to neighboring nodes.
A different approach in [24] is based on an angle of arrival estimation technique. In this work, beacon nodes are equipped with a directional antenna, using which they can send directional beacon signals that are powerful enough to be heard by all sensor nodes.
2.4. MAC Layer Interface
For a careful design of wireless sensor networks, one should consider an appropriate MAC layer optimized for sensor communication. One should always consider that sensor
nodes are low-power devices, and they do not contain a strong computational unit.
Therefore, MAC layers designed for traditional ad hoc networks cannot be applied to wireless sensor networks. Several MAC layer alternatives are proposed in the literature.
For a detailed list, the reader could refer to [6].
Self-organizing Medium Access Control for Sensor networks (SMACS) is an infrastructure building protocol that forms a flat topology for sensor networks [25]. This is a distributed protocol that enables a collection of nodes to discover their neighbors and establish transmission/reception schedules for communicating with them without the need for any local or global master nodes. To reduce the likelihood of collisions, it requires each link to operate on a different frequency. This frequency band is chosen at random from a large pool of possible choices when the links are formed.
In order to provide continuous service to mobile sensor nodes, Eavesdrop-And- Register (EAR) algorithm is proposed [25]. This algorithm enables seamless interconnection of mobile nodes in the field of stationary wireless nodes, and represents the mobility management aspect of the SMACS protocol.
In [26], time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes are discussed. In TDMA, the transmission time is minimized, as the full bandwidth of the channel is allocated for a single sensor node. However, in this case, only one sensor can be actively transmitting. In order to enable simultaneous transmissions, FDMA scheme can be used where the bandwidth is divided into frequencies, which are assigned to different sensors. In this case, the transmission time is maximized. A hybrid scheme involving both TDMA and FDMA is also introduced.
This thesis is independent of the MAC layer. The sink location and related clustering mechanism can be applied into any MAC layer that the sensor’s hardware is employing.
Therefore, MAC layer is not considered as a fundamental part of the solution. On the contrary, the solution can be used with any MAC layer that will be found on the market.
2.5. Routing Technique
In order to utilize the sensor’s energy in the most beneficial manner, power-aware routing methods must be used. Since these equipments are limited on battery resources, the underlying routing protocol should pay attention to the power level of each sensor in the network. Data that is extracted from the environment should be forwarded to the sink nodes for further processing. During this phase, sensors constitute an ad hoc network infrastructure and data packets are routed to the sink node through intermediate nodes.
Each node generates a small data packet containing the knowledge gathered from the environment. This data packet is sent to the destination using the underlying routing method with the help of intermediate sensor nodes. Intermediate nodes have several alternatives. These alternatives are application dependent and may be chosen according to user needs.
(i) They can directly forward the packet to the next relay node or to the destination, if it was the last hop on the way to the destination.
(ii) They can delay the forwarding for a moment waiting for other sensors, which might as well be generating a packet sent to the same destination, so that all these packets can be merged into one larger packet.
(iii) Similar to (ii), but this time the data in each packet might be extracted and aggregated into a new result, and this result is forwarded to the destination.
(iv) An intermediate node can also add its own measurements to the packets, using methods described in either (ii) or (iii).
In Figure 2.4, sensor nodes i1 and i2 transmit data packets simultaneously. Their packets are routed to the sink node s through intermediate nodes. The underlying routing method may choose to merge the data packets into one packet on the way to the destination at the intermediate nodes. All the other nodes in the environment may stay idle during this communication.
i2
i1
s
idle node
intermediate node initiator node sink
Figure 2.4. Data delivery from source to the sink using intermediate nodes
In this routing mechanism, intermediate nodes that have enough residual power should be used as relays. The choice of intermediate nodes can be performed in a distributed manner at the node level, or centrally at the destination. In the latter case, a global knowledge of node status information is assumed. This data is not unrealistic to be captured. Sensor nodes are sending their measurements to the destinations. Supplementary information like their geographic location and battery level may be piggybacked to their data packets. As a result, the destination nodes may retrieve all the necessary information about the current network infrastructure and remaining resources from the field.
Furthermore, since these nodes are more powerful in the sense computational power and battery resources, they can perform extensive computations like centralized routing decisions easily.
2.6. Packet Structure
Data packets need to be carefully designed to carry the information gathered from the environment. Packets are originated from source sensors and are sent to intermediate nodes in order to be forwarded to the destination. In the previous section, alternative routing, merging and aggregating mechanisms are stated. Beside their effect on routing, these requirements also affect the underlying packet structure.
lh lp lt
Header Payload Trailer
Figure 2.5. Basic link layer packet structure, redrawn from [27]
The basic link layer packet structure is given in Figure 2.5, which is presented in [27]. Here, the packet is composed of header, payload and trailer parts, which are assumed to be of lh, lp and lt bits long respectively. The header field contains segment information corresponding to higher layer packets and source and destination identifiers. Whenever the application does not require the exact node identifier, a collection of event, location, and attribute identifiers could also easily replace the header information, resulting with a much shorter field of a few bytes. The payload contains information bits and the trailer part contains error control bits.
The size of the payload depends on the information that the packet contains. The data gathered from the phenomenon should be sent to the destination. For temperature, humidity or attribute sensors, only one or two bytes will be sufficient to code the information. Depending on the alternative routing and aggregating mechanism, this data will be replicated for each intermediate sensor in the routing tree.
For centralized power-aware routing methods, current battery level of the sensor should be sent to the destination nodes. Furthermore, again depending on the alternative routing and aggregating mechanism, this battery information contains data for each sensor in the routing tree. This information is extracted at the destination and used in route calculations.
Table 2.1. Optimal packet size in link layer [27]
FEC Method η Min max
Without FEC 0.70 100 500
BCH, t = 2 0.88 400 800
BCH, t = 4 0.93 1000 1500
BCH, t = 6 0.95 1500 3000
In [27], a detailed analysis is presented to estimate the optimum payload size considering energy efficiency (η). The payload size is found to lie between 50 and 500
bytes depending on the bit error rate of the channel when no error control mechanism is used. This size increases up to a minimum of 500 and maximum of 3000 bytes according to the error correcting capability that is employed. Here, binary BCH codes are used with different error correcting capabilities (t), i.e., the maximum number of bit errors that can be corrected seamlessly. Approximate results for raw bit error rate p = 10-3 are summarized in Table 2.1.
Table 2.2. Length of binary BCH codes with different t [28]
t Total packet size BCH code length Data size
2 63 12 51
2 255 16 239
2 511 18 493
2 1023 20 1003
4 63 24 39
4 255 32 223
4 511 36 475
4 1023 40 983
6 63 33 30
6 255 48 207
6 511 54 457
6 1023 60 963
Using BCH codes, however, adds extra error correcting bits to the data packets. A designer should consider this overhead during estimating the necessary packet size.
Examples for these overhead-bits are given in Table 2.2. For a detailed description of binary BCH codes, the reader may refer to [28].
2.7. Energy Model
Efficient energy consumption is one of the most important design constraints in wireless sensor network architecture [29]. The life of each sensor node depends on its
power dissipation. In applications where the sensors are not equipped with energy scavenging tools like solar cells, sensors with exhausted batteries cannot operate anymore.
Moreover, since sensor nodes behave as relay nodes for data propagation of other sensors to sink nodes, network connectivity decreases gradually [7]. This may result in disconnected subnetworks of sensors, i.e., some portions of the network cannot be reachable at all. Therefore, the level of power consumption must be considered at each stage in wireless sensor network design.
2.7.1. Transmitter Power Model
As mentioned before, the main concern in wireless sensor network design is power.
The underlying architecture must consider power efficiency as a major constraint. A good evaluation of the available techniques can be found in [30]. To start, consider the radio propagation model in a single-path free-space channel. The relationship between transmitted power Pt and received power Pr is given by
2
4
= G d P G
P
r t t r
π
λ (2.1)
where Gt and Gr are the transmitter and receiver antenna gains respectively, d is the distance between the transmitter and receiver, λ =c f is the wavelength of the transmitted signal, whereas f is its frequency, and c is the velocity of radio wave propagation in free space, which is equal to the speed of light. Using Equation 2.1, we derive
d2
Pt =ω (2.2)
where ω=(Pr GtGr)(4π λ)2. Equation 2.2 can be further generalized as
ωdα
Pt = (2.3)
where α >1 is known as path loss exponent. For free-space channel, we have seen in Equation 2.2 that α =2. Table 2.3 gives a list of typical path loss exponent values obtained in various radio environments [31]. In many sensor applications, it is assumed that α ranges between 2 and 4, since the sensors have short antennae, which are very close to the ground.
Table 2.3. Path loss exponents for different environments [31]
Environment α
Free space 2
Urban area cellular radio 2.7 to 3.5 Shadowed urban cellular radio 3 to 5 In building line-of-sight 1.6 to 1.8
Obstructed in building 4 to 6
Obstructed in factories 2 to 3
Power is defined by the rate of change in the energy with time [32]. Therefore, the amount of energy that is necessary to operate for time t consuming power P can be found as follows.
t P E = ∆
∆ (2.4)
2.7.2. Energy Consumption
Energy consumption in an arbitrary sensor node has in general the following components depending on the operations performed within the node:
(i) Sensing Energy: In order to activate sensing circuitry within the node, and gathering data from the environment, an amount of energy must be dissipated, which is called sensing energy, eS. The magnitude of this energy depends on the task that is assigned to the sensor. Different sensors require different level of energy during operation.
(ii) Transmitter Energy: Afterwards, this data must be transmitted towards the destination. Therefore, the transmitter circuitry must be operated. For this operation, the transmitter energy, eT must be consumed which depends on the transmitter power, Pt, size of the data packet, and the data transfer rate.
(iii) Receiver Energy: As a relay node, a sensor node is also in charge of forwarding data packets of other sensor nodes. For this operation, sensors must be able to receive those data packets. The receiver energy, eR, will be consumed during this operation, which is irrelevant of the distance between nodes. During reception, receiver power, Pr, will be spent during the reception of the data packet with the given data transfer rate.
(iv) Computation Energy: To operate these circuitries, sensor’s processing unit must be activated. Moreover, whenever data aggregation is performed additional computations must be realized. Compared to the previous items, computation energy, eC, is relatively low [33].
During the life cycle of a typical sensor node, each event or query will be followed by a sensing operation, performing necessary calculations to derive a data packet and transmitting this packet to the destination. In addition, sensor nodes often relay data packets received from other sensors. Thus, the total energy, eTotal, in an arbitrary active time frame can be presented as the sum of above energy requirements.
C S R T
Total e e e e
e = + + + (2.5)
Efficient sensing circuitries and computation algorithms help to reduce eS, and eC. The other two components eT, and eR are dependent on the communication architecture and underlying techniques. Therefore, power aware methods must be employed in order to reduce the energy consumption during communication [33].
Only the transmitter energy, eT, is related with the distance between the communicating sensor nodes. The other components of total energy remain constant with varying distance between communicating pairs. Therefore, we can rewrite Equation 2.5 as a function of d using Equation 2.3 and Equation 2.4 as follows.
( )d = dκ α +τ
eTotal (2.6)
where κ =ω∆t , with ∆t being the duration of packet transmission process, and
C S
R e e
e + +
τ = , the overhead energy, which is a constant value with varying d. Any other energy consuming activity in the sensor node can be added to the overhead energy component that do not depend on the transmission distance [34].
A similar energy model is proposed in [35] where the energy consumption for a message is measured as dα +τ, with a comparable argumentation. However, the important factor κ was missing.
3. MULTIPLE SINK SENSOR NETWORK DEFINITIONS AND FORMULATIONS
3.1. Motivation
The efficiency of the sensor network investment is directly related with the length of the reliable monitoring duration of the field. The better energy control mechanisms are used in the sensor nodes’ firmware and in the network management techniques, the longer the network will be serving their investors. Therefore, the limited battery resource of the sensors should be handled efficiently.
In some applications, several thousands of sensor nodes might be deployed over the monitored region. For example, in agricultural scenarios, in environmental monitoring applications, such large-scale sensor networks would be necessary. The diameter of the region might easily be as large as several kilometers. In this case, scalability of the network is a very important design issue. In order to obtain a scalable network, the sensor nodes should be divided into clusters. The nodes within a cluster will then be connected to the sink nodes dedicated for that cluster. Figure 3.1 shows such a sensor network with several nodes and three clusters with three sink nodes.
Figure 3.1. A large-scale sensor network with three clusters