Energy-efficient two-stage chain protocol routing algorithm for wireless sensor networks

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(1)UNIVERSITI PUTRA MALAYSIA ENERGY-EFFICIENT TWO-STAGE CHAIN PROTOCOL ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS. HUSAM KAREEM FARHAN. FK 2014 77.

(2) PM. IG. H. By. T. U. ENERGY-EFFICIENT TWO-STAGE CHAIN PROTOCOL ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS. ©. C. O. PY. R. HUSAM KAREEM FARHAN. Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Master of Science May 2014.

(3) PM. All material contained within the thesis, including without limitation text, logos, icons, photographs and all other artwork, is copyright material of Universiti Putra Malaysia unless otherwise stated. Use may be made of any material contained within the thesis for non-commercial purposes from the copyright holder. Commercial use of material may only be made with the express, prior, written permission of Universiti Putra Malaysia.. ©. C. O. PY. R. IG. H. T. U. Copyright © Universiti Putra Malaysia.

(4) PM. DEDICATION. ©. C. O. PY. R. IG. H. T. U. I would like to dedicate my thesis to my beloved parents and siblings because of all the wonderful things they have done to me and supporting me all the way. ii.

(5) Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment of the requirement for the degree of Master of Science. ENERGY-EFFICIENT TWO-STAGE CHAIN PROTOCOL ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS. PM. By HUSAM KAREEM FARHAN. U. May 2014. T. Chair: Shaiful Jahari Hashim, PhD. H. Faculty: Engineering. O. PY. R. IG. Wireless sensor nodes are mostly used in extreme environments, especially at remote and hostile areas including battlefield, volcanoes and underwater. Thus, it is difficult to replenish the energy source of the sensor node once it is installed. One of the factors that plays very important role in extending the wireless sensor networks lifetime is routing algorithm. In order to extend the lifetime of the nodes, we propose a new routing algorithm that can achieve significant energy conservation in WSNs, known as Two Stage Chain Routing Protocol (TSCP). The main objective of TSCP is to increase the network lifetime of wireless sensor networks. The new TSCP routing algorithm is compared with other similar grid-based routing algorithms namely Chain-Cluster based Mixed routing (CCM) and Chain-Chain Based Routing Protocol (CCBRP).. ©. C. TSCP algorithm divides the sensor network into multiple chains and work within two stages. The first stage is dividing the nodes to horizontal chains that include all sensor nodes within the same row and one of the nodes in the chain will act as a head for other members. The second stage is involving the formation of vertical chain that includes all chain heads, one of the nodes in the vertical chain that includes all chain heads and selecting one of the nodes to act as a main head for the entire network. Simulation results show that TSCP algorithm outperforms CCM and CCBRP algorithms in terms of four performance metrics namely Network lifetime, FND and LND (First Node and Last Node Died), network stability period, and energy consumption. The main concern for TSCP algorithm is to extend the lifetime of wireless sensor network. As a result, TSCP could achieve an iii.

(6) ©. C. O. PY. R. IG. H. T. U. PM. improvement over CCM and CCBRP algorithms in about 25% and 8% respectively for small network size (3 X 3) sensor nodes configuration. At the same time, TSCP could achieve about 29% and 9% improvement in comparison with CCM and CCBRP respectively using medium network size, (9 X 9) sensor nodes configuration. When we carried on the comparison using large network size, which is (15 X 15) sensor nodes configuration, TSCP shows an improvement about 25% and 18% over CCM and CCBRP algorithms respectively.. iv.

(7) Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk Ijazah Sarjana Sains ALGORITMA PROTOKOL PENGHALAAN RANTAIAN CEKAP TENAGA DWI-PERINGKAT UNTUK RANGKAIAN PENDERIA TANPA WAYAR. PM. Oleh HUSAM KAREEM FARHAN. U. May 2014. T. Pengerusi: Shaiful Jahari Hashim, PhD. H. Fakulti: Kejuruteraan. C. O. PY. R. IG. Nod penderia tanpa wayar (WSN) kebanyakannya digunakan di persekitaran yang melampau, terutama di kawasan pendalaman termasuk lapangan perang, gunung berapi dan di dalam air. Justeru itu ianya amat sukar untuk menambah semula sumber tenaga nod penderia apabila ianya telah dipasang. Salah satu faktor utama yang memainkan peranan penting di dalam melanjutkan jangka hayat nod penderia tanpa wayar ialah algoritma penghalaan. Demi melanjutkan jangka hayat nod tersebut, kami mencadangkan algoritma penghalaan baharu yang boleh mencapai penjimatan tenaga yang ketara di dalam WSN yang dikenali sebagai algoritma Penghalaan Protokol Rantaian Dua Peringkat (TSCP). Objektif utama TSCP adalah untuk menambah jangka hayat rangkaian nod penderia tanpa wayar. Algoritma penghalaan TSCP yang baharu dibandingkan dengan algoritma penghalaan berdasarkan grid lain yang serupa iaitu Rantaian-Kluster Campuran (CCM) dan Protokol Penghalaan RantaianRantaian (CBRP).. ©. Algoritma TSCP membahagikan rangkaian penderia kepada beberapa rantaian dan berfungsi di dalam dua peringkat. Peringkat yang pertama adalah membahagikan nod kepada rantaian melintang yang merangkumi semua nod penderia di dalam barisan yang sama dengan salah satu daripada nod di setiap rantai akan dipilih untuk menjadi ketua untuk ahli nod yang lain. Peringkat yang ke dua adalah melibatkan penubuhan rantaian menegak dan memilih salah satu daripada nod sebagai ketua utama untuk keseluruhan rangkaian. Hasil simulasi menunjukkan algoritma TSCP menewaskan algoritma CCM dan CCBRP di dalam empat metrik prestasi iaitu jangka hayat rangkaian, v.

(8) ©. C. O. PY. R. IG. H. T. U. PM. FND dan LND (Kematian Nod Pertama dan Terakhir), jangka masa rangkaian stabil dan penggunaan tenaga. Keprihatinan yang utama untuk algoritma TSCP adalah untuk melanjutkan jangka hayat rangkaian penderia tanpa wayar. Sebagai hasilnya, TSCP boleh mencapai peningkatan berbanding algoritma CCM dan CCBRP, masing-masing sebanyak 25% dan 8% untuk saiz rangkaian kecil dengan nod penderia berkonfigurasi (3 X 3). Pada masa yang sama, TSCP boleh mencapai peningkatan, masing-masing sebanyak 29% dan 9% berbanding CCM dan CCBRP dengan menggunakan saiz rangkaian nod penderia sederhana berkonfigurasi (9 X 9). Apabila kami menjalankan perbandingan dengan menggunakan saiz rangkaian yang besar, iaitu nod penderia berkonfigurasi (15 X 15), TSCP menunjukkan peningkatan masing-masing sebanyak 25% dan 18% berbanding algoritma CCM dan CCBRP.. vi.

(9) ACKNOWLEDGEMENTS The student is greatly thankful to Allah for everything have happened and will happen in his life, especially, enabling the author to accomplish this project successfully.. PM. Many thanks to my mother who was always praying for me to overcome the obstacles of the life and stay focus on my study to be the successful person that can be proud of him.. ©. C. O. PY. R. IG. H. T. U. The student is gratefully acknowledges the guidance, advice, support, motivation and encouragement received from his supervisor Dr. Shaiful Jahari Hashim and co-supervisors Assoc.prof.Dr. Aduwati Sali and Assoc.prof.Dr. Shamala Subramaniam.. vii.

(10) PM. I certify that a Thesis Examination Committee has met on (DATE OF VIVIA) to conduct the final examination of Husam Kareem Farhan on his thesis entitles “ENERGY EFFICIENT TWO STAGE CHAIN ROUTING PROTOCOL (TSCP) FOR WIRELESS SENSOR NETWORKS” in accordance with the Universities and University Colleges Act 1971 and the Constitution of the Universiti Putra Malaysia [P.U(A) 106] 15 March 1998. The Committee recommends that the student be awarded the degree of Master of Science.. U. Members of the Thesis Examination Committee were as follows:. H. PY. R. IG. Associate Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner 1). T. Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman). C. O. Associate Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner 2). ©. (External Examiner). SEOW HENG FONG, PhD Professor and Deputy Dean School of Graduate Studies Universiti Putra Malaysia Date:. viii.

(11) ix. ©. T. H. IG. R. PY. O. C. PM. U.

(12) This thesis was submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfilment of the requirement for the degree of (Master of Science). The members of the Supervisory Committee were as follows:. PM. Shaiful Jahari Hashim, PhD Senior Lecturer Faculty of Engineering Universiti Putra Malaysia (Chairman). U. Aduwati Sali, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Member). ©. C. O. PY. R. IG. H. T. Shamala Subramaniam, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Member). _________________ BUJANG KIM HUAT, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia Date:. x.

(13) DECLARATION Declaration by Graduate Student. PY. R. IG. H. T. U. PM. I hereby confirm that:  this thesis is my original work;  quotations, illustrations and citations have been duly referenced;  this thesis has not been submitted previously or concurrently for any other degree at any other institutions;  intellectual property from the thesis and copyright of thesis are fullyowned by Universiti Putra Malaysia, as according to the Universiti Putra Malaysia (Research) Rules 2012;  written permission must be obtained from supervisor and the office of Deputy Vice-Chancellor (Research and Innovation) before thesis is published (in the form of written, printed or in electronic form) including books, journals, modules, proceedings, popular writings, seminar papers, manuscripts, posters, reports, lecture notes, learning modules or any other materials as stated in the Universiti Putra Malaysia (Research) Rules 2012;  there is no plagiarism or data falsification/fabrication in the thesis, and scholarly integrity is upheld as according to the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia (Research) Rules 2012. The thesis has undergone plagiarism detection software.. Date: __________________. C. O. Signature: _______________________. ©. Name and Matric No.: ______________________________________. xi.

(14) Declaration by Members of Supervisory Committee. U. PM. This is to confirm that:  the research conducted and the writing of this thesis was under our supervision;  supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) are adhered to.. Signature: _______________________. Name of Chairman of Supervisory Committee: _______________________. Name of Member of Supervisory Committee: ______________________. PY. R. IG. H. T. Signature: ________________________. Signature: ________________________. Name of Member of Supervisory Committee: _______________________. Name of Member of Supervisory Committee: _______________________. ©. C. O. Signature: ______________________. xii.

(15) TABLE OF CONTENTS. Page. U T. CHAPTER. H. INTRODUCTION 1.1 Wireless Sensor Networks 1.2 Classification of Routing Algorithms in Wireless Sensor Networks 1.2.1 Data Centric Protocols 1.2.2 Location Based Protocols 1.2.3 Hierarchical protocols 1.2.4 Network Flow and QoS-aware Protocols 1.3 Routing Difficulties and Design Challenges in WSNs 1.3.1 Node Deployment 1.3.2 Energy Consumption 1.3.3 Scalability 1.3.4 Data Aggregation 1.3.5 Nodes Heterogeneity 1.4 Problem Statement 1.5 Scope of the Thesis 1.6 Research Objectives 1.7 Thesis Organization. C. O. PY. R. IG. 1. Ii iii v vii viii xi xvi xvii xix. PM. DEDICATION ABSTRACT ABSTRAK ACKNOWLEDGEMENTS APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS. ©. 2 LITERATURE REVIEW 2.1 Hierarchical Routing Algorithms for WSNs 2.1.1 LEACH 2.1.2 PEGASIS 2.2 Recent Works Based on Hierarchical Routing Protocols 2.2.1 Chain Based Routing Algorithms 2.2.2 Cluster Based Routing Algorithms 2.2.3 Hybrid Hierarchical Routing Algorithms 2.3 Routing Algorithms Employed in the Research xiii. 1 1 2 2 2 2 3 3 3 4 4 4 4 5 5 5 6 7 7 7 8 8 8 13 16 19.

(16) 2.4. Chain Cluster Based Mixed Routing (CCM) 2.3.1.1 Network Model 2.3.1.2 Stage 1 (Chain based routing) 2.3.1.3 Stage 2 (Cluster based routing) 2.3.2 Chain - Chain Based Routing Protocol (CCBRP) 2.3.2.1 Network Model 2.3.2.2 Stage 1 (Selecting the leader for each chain) 2.3.2.3 Stage 2 (Forming a chain includes all chain heads) Summary. ©. C. O. PY. R. IG. H. T. U. 3 RESEARCH METHODOLOGY 3.1 The Aim of Study 3.2 Network Assumption Definitions 3.3 Preliminary Work 3.3.1 MATLAB 3.3.2 Network Assumptions 3.4 Radio Model used in the System 3.5 Energy Consumption for Data Gathering 3.5.1 Data Aggregation in Cluster Based Approach 3.5.2 Data Aggregation in Chain Based Approach 3.6 Two Stage Chain Routing Protocol (TSCP) 3.6.1 Stage 1 (Horizontal chains formation) 3.6.2 Stage 2 (Vertical chain formation) 3.6.3 Methods that can be used for Chains Formation in TSCP Algorithm 3.7 Design Steps for the Proposed Algorithm (TSCP) 3.8 Optimization of TSCP Algorithm 3.9 Routing Algorithms Employed in the Research 3.9.1 CCM Algorithm 3.9.2 CCBRP Algorithm 3.10 Performance Metrics 3.10.1 Network Lifetime 3.10.2 First Node and Last Node Died (FND and LND) 3.10.3 Network Stability 3.10.4 Energy Consumption 3.11 Summery 4. 19 20 20 20 21 21 21 21. PM. 2.3.1. RESULTS AND DISCUSSION 4.1 Introduction 4.2 Why we Anticipate our Algorithm will Show Better Performance than CCBRP Algorithm. 4.3 Performance Evaluation 4.3.1 Scenario One – Using 9 Sensor Nodes 4.3.1.1 Network Lifetime 4.3.1.2 First Node and Last Node Died xiv. 21 25 26 27 27 27 28 28 30 31 31 31 32 33 34 34 38 39 39 40 42 42 42 42 42 43 45 45 45 47 47 48 49.

(17) 50 50 51 52 53 53 54 55 55 56 57 58 58 59 61 62 63 65 66 69 69 69. 71 76 107 108. IG. 5 CONCLUSION AND FUTURE WORK 5.1 Research Conclusion 5.2 Future Work. R. 4.4. H. T. U. PM. 4.3.1.3 Network Stability 4.3.1.4 Energy Consumption 4.3.2 Scenario Two – Using 81 Sensor Nodes 4.3.2.1 Network Lifetime 4.3.2.2 First Node and Last Node Died 4.3.2.3 Network Stability 4.3.2.4 Energy Consumption 4.3.3 Scenario Three – Using 225 Sensor Nodes 4.3.3.1 Network Lifetime 4.3.3.2 First Node and Last Node Died 4.3.3.3 Network Stability 4.3.3.4 Energy Consumption 4.3.4 Scenario Four – Optimized TSCP (O-TSCP) 4.3.4.1 Network Lifetime 4.3.4.2 First Node and Last Node Died 4.3.4.3 Network Stability 4.3.4.4 Energy Consumption 4.3.5 Scenario five – Investigating FND /LND versus network scalability Summary. ©. C. O. PY. REFERENCES APPENDICES BIODATA OF STUDENT LIST OF PUBLICATION. xv.

(18) LIST OF TABLES Table 2.1. Simulation Software, Performance Metric(s), and Routing Technique used in each Algorithm. 3.1. PM. Stability period in small size network. 4.3. U. Percentage of dead nodes over sensing rounds in small network. 4.2. Percentage of dead nodes over sensing rounds in medium network. T. 4.4. Stability period in medium size network. H. 4.5. Percentage of dead nodes over sensing rounds. 4.6. IG. Stability period in large size network. 4.7. Network stability period using (3×3) sensor nodes. R. 4.8. Network stability period using (9×9) sensor nodes. PY. Network stability period using (15×15) sensor nodes Summary of significant findings. ©. C. O. 4.10. 35. Topology Matrix. 4.1. 22 30. Notations. 3.2. 4.9. Page. xvi. 49 50 53 54 56 57 62 63 63 67.

(19) LIST OF FIGURES. Figure. Page The components of typical sensor node. 2.1. Sample for chain routing technique scenario. 2.2. Sample for cluster routing technique scenario. 2.3. Sample for hybrid routing technique scenario. 3.1. Process flow diagram. 3.2. Smart building concept. 3.3. First order radio model. 3.4. Flowchart of the first stage in TSCP. 33. 3.5. Flowchart of the second stage in TSCP. 34. 3.6. Sample of a common case scenario of TSCP. 38. 3.7. Data transmission in CCM algorithm. 40. 4.1. Worst-case scenario in CCBRP algorithm. 46. 4.2. Best-case scenario of CCBRP algorithm. 46. 13 16. U. 19. PY. R. IG. H. T. 25. O. 4.3. 1. PM. 1.1. 26 29. Network topology for (3×3) sensors. 48. Number of alive nodes versus the sensing rounds using small size network FND and LND using small size network. 48. 4.6. Total energy consumption using small size network. 51. 4.7. Network topology for (9 X 9) sensors. 52. 4.8. Number of alive nodes versus the sensing rounds using medium size network FND and LND using medium size network. 52. C. 4.4. ©. 4.5. 4.9. xvii. 49. 53.

(20) 4.10. Total energy consumption using medium size network. 54. 4.11. Network topology for (15×15) sensors. 55. 4.12. 56. 4.13. Number of alive nodes versus the sensing rounds using large size network FND and LND using large size network. 4.14. Total energy consumption using large size network. 58. 4.15. Network lifetime using (3 ×3) sensor nodes. 59. 4.16. Network lifetime using (9 × 9) sensor nodes. 4.17. Network lifetime using (15 × 15) sensor nodes. 60. 4.18. Network lifetime using different networks size. 60. 4.19. FND and LND using (3×3) sensor nodes. 61. 4.20. FND and LND using (9×9) sensor nodes. 61. 4.21. FND and LND using (15×15) sensor nodes. 62. 4.22. Total energy consumption using (3×3) sensor nodes. 64. 4.23. Total energy consumption using (9×9) sensor nodes. PM. U. T. H. IG. R. 59. 64. Total energy consumption using (15×15) sensor nodes. 65. FND and LND using (25×25) sensor nodes. 65. ©. C. O. 4.25. PY. 4.24. 57. xviii.

(21) LIST OF ABBREVIATIONS. PM. U. T. H. IG. O. CBERP. R. IECBSN ACK BEC MCHRP CFDASC. Wireless Sensor Network Base Station Quality of Service Sensor Protocols for Information via Negotiation Internet Protocol Global Positioning System Minimum Energy Communication Network Geographic Adaptive Fidelity Low-Energy Adaptive Clustering Hierarchy Power-Efficient GAthering in Sensor Information Systems Threshold sensitive Energy Efficient sensor Network protocol Sequential Assignment Routing Chain –Chain Based Routing Protocol Chain –Cluster based Mixed routing Two Stage Chain Routing Protocol Energy-Based selective Cluster-Head Rotation Safe and Power –Efficient Routing Algorithm Energy Efficient Chain Based Routing Protocol Improved Energy Efficient Based Protocol Identification Energy Efficient Chain Based Network Energy-Based Re-transmission Algorithm Mobile sink Improved Energy-Efficient PEGASIS-Based routing protocol Improved Energy Efficient Chain Based Sensor Network Acknowledgement Based Energy Clustering Multiple Cluster-Heads Routing Protocol Chain-based Fast Data Aggregation Algorithm Based on Suppositional Cells Cluster Based Energy Efficient Routing Protocol for Wireless Sensor Network Energy Efficient Cluster-Chain based Protocol Reliable and Energy-efficient Chain-cluster Based Routing Protocol Chain-Clustered Communication Protocol Matrix Laboratory Pico-Joule Meter First Node died Last Node Died. PY. WSN BS QoS SPIN IP GPS MECN GAF LEACH PEGASIS TEEN SAR CCBRP CCM TSCP EBSCR SPER EECB IEEPB ID ECBSN EBRT MIEEPB. C. ECCP REC+. ©. CCCP MATLAB pJ M FND LND. xix.

(22) CHAPTER 1 INTRODUCTION 1.1 Wireless Sensor Networks. U. PM. Generally, any device that has the ability to sense the surrounding environment can be considered as a sensor node. A wireless sensor network (WSN) is a group of sensor nodes that cooperate with each other, it may contain few numbers of sensors or it can consists of thousands of sensors, these nodes are capable of doing many tasks such as data processing and environmental sensing. WSNs can be used in wide range of applications such as measuring the temperature, the humidity, the pressure, noise level, monitoring the vehicular movement, military applications and health applications [1].. IG. H. T. The typical node in a WSN contains four main parts, which are sensing unit, computing unit, communication unit, and power unit. Figure 1.1 shows the basic sensor node architecture. These nodes mostly deployed in areas that have the least direct human interaction. The typical WSN consists of hundred to thousand nodes, those nodes have the ability to connect with each other, and it can directly be connected to the base station (BS) [2].. ©. C. O. PY. R. The number of the nodes in a WSN depends on the application and the geographical area that represents the field of interest. In other words, a greater number of sensor nodes are needed for larger area considering the level of accuracy that demanded from that area. One of the main crucial issues can face the operation and the surveillance of WSNs is energy consumption. Energy in WSNs mostly powered by battery and the greatest share of this energy is consumed during Data transmission [3]. Many researches have been done to solve this problem or at least find a solution to decrease this consumption. One of those solutions is using efficient routing algorithm. Figure 1.1 demonstrates the main components in a typical sensor node.. Figure 1.1: The components of typical sensor node [4]. 1.

(23) 1.2 Classification of Routing Algorithms of WSNs. PM. Since there are numerous applications for WSNs, this leads to propose many new algorithms to be compatible with different applications and different network structure. These routing algorithms have considered in their design WSNs characteristics, we summarized some of them earlier. All types of routing algorithms can be classified within four categories, which are data centric protocols, location based protocols, hierarchical protocols, and network flow and QoS-aware protocols [5]. 1.2.1 Data Centric Protocols. 1.2.2 Location Based Protocols. IG. H. T. U. In most applications of WSNs, it is unfeasible to use global identifiers for the deployed sensor nodes. If we consider the random deployment of sensor nodes along with the lack of global identifiers, it will be difficult to choose a specific group of sensor nodes to be queried. So that, when data transmitted from sensor nodes within specific field would have significant redundancy then it will consume more energy. Because of this problem, they come up with data centric protocols. In this type of routing algorithms, the base station send query to specific area and wait for receiving data from the sensor nodes located in that area. The base station specifies the properties of data using attribute-based naming. The first proposed data centric protocol is Sensor protocols for information via negotiation (SPIN)[5].. O. PY. R. Generally, in many applications for WSNs, the position of sensor node is required. The reason that in some cases it is necessary to know the location of sensor nodes is to estimate the energy dissipation between nodes in the sensing area. Moreover, sensor nodes location information can be used efficiently for energy conservation. For example, if the region needed to be sensed were known, by using the location information for the nodes, the query would be sent only to the demanded region, this would lead to significant reduction in the number of transmissions.. ©. C. They mostly use GPS to updates their location information, so that they are also applicable for mobile sensor nodes. There are many location based routing algorithms but some of them are not applicable for WSNs because they are not energy aware. In order to stay with the theme of WSNs we will mention some examples which can be used as routing algorithms for WSNs like Minimum energy communication network (MECN) and Geographic adaptive fidelity (GAF) [5]. 1.2.3 Hierarchical Protocols In hierarchical algorithms sensor nodes are grouped within clusters or chains and one of those nodes act as the cluster head or chain head then all other nodes send their sensed data to their own head. The head will act as a 2.

(24) 1.2.4. PM. gateway between sensor nodes and base station. The main aim that motivates researchers to come up with this kind of routing technique is scalability and robustness. Since hierarchical algorithms using multi-hop technique to transmit their data from sensor nodes to the head then to the base station it will reduce the energy consumed by data transmission in long distance and as a result for this reduction, it will increase the network lifetime. The main hierarchical routing algorithms for WSNs are Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient GAthering in Sensor Information Systems (PEGASIS) and Threshold Sensitive Energy Efficient Sensor Network protocol (TEEN) [6-8]. Network Flow and QoS-aware Protocols. H. T. U. In this category of routing algorithms, there must be a balance between energy consumption and one or more other factors such as (end-to-end delay, bandwidth, throughput, etc.) when they send the data to base station. The first algorithm that consider as a QoS approach is Sequential Assignment Routing (SAR) [2]. SAR introduced as a routing algorithm that can obtain fault tolerance and energy efficiency together. Many other algorithms proposed to provide a specific QoS and network flow for instance SPEED algorithm provides soft real-time end-to-end guarantees [2].. IG. 1.3 Routing Difficulties and Design Challenges in WSNs. O. PY. R. Although the numerous applications of WSNs, these networks have many limitations, such as limited computing abilities, limited energy resources and limited bandwidth [2]. One of the main important goals to design a routing algorithm for WSNs is to carry out data communication while trying to extend the network lifetime and prevent connectivity degradation by employing aggressive energy management techniques. Many factors can act as challenges in design a routing algorithm for WSN and in order to achieve an efficient communication in WSNs, these challenges must be overcome. We summarize some of the challenges that can affect the design of routing algorithm in WSNs. Nodes Deployment. ©. C. 1.3.1. Deployment of nodes in WSNs depends on the application of the network under consideration, it can be randomized or deterministic. In random deployment, the nodes are randomly scattered in the target area. Random deployment, in practice, is faster than deterministic method and more feasible. On the other hand, it costs more sensor nodes to get more data accuracy [9]. In deterministic deployment, the nodes are manually installed according to predetermined distance. Although, it is time consuming to deploy the sensor nodes manually but it can use fewer nodes in comparison with random deployment with same accuracy level. An example of deterministic deployment is grid deployment. 3.

(25) 1.3.2 Energy Consumption. U. PM. During planning for the nodes' distribution, the process of designing the routing protocol is greatly affected by energy constraint. The energy consumption in wireless radio transmission is proportional to the square of the distance of transmission and sometimes higher than this in the presence of obstacles. To decrease the consequences of this issue, we have to choose one of two options, multi-hop routing or single-hop routing. Multi-hop routing is more energy conservation but it can cause more overhead for topology management [9]. On the other hand, single-hop routing has less overhead but it consumes much more energy than multi-hop because of the direct transmission to the sink especially, when the sensor nodes are relatively far from the sink. So that, using multi-hop routing, in most applications, become unavoidable. 1.3.3 Scalability. IG. H. T. The number of nodes deployed, in any sensing area, can reach hundreds or even thousands of sensors. Therefore, the routing scheme for such area should consider this amount of nodes and must be able to handle all nodes in the sensing field [2]. In addition, routing protocol should have the ability to handle any probable scaling for the network. 1.3.4 Data Aggregation. C. O. PY. R. Since sensors might generate redundant data, they implement a data aggregation to eliminate the duplicated data and consequently decrease the number of transmissions. The aggregated data is a combination of data collected from different sources and combined together in order to decrease the data packet size [5]. There are some functions can be used to aggregate the data such as minima, maxima, duplicate suppression and average. This technique is very efficient to improve the WSN lifetime, especially when multi-hop routing is applied in that network, because data aggregation can be very applicable technique when it is used along with multi-hop routing technique at the same algorithm. 1.3.5 Nodes Heterogeneity. ©. According to the assumption of many studies, sensors were assumed to be homogenous (they have the same ability in terms of energy, communication and computation) [2]. Sensor node can have different abilities depending on the required function and application. The reason that motivates most of the researchers to use homogenous nodes is that the usage of heterogeneous nodes could come up with many issues related to data routing. For example, some clustering algorithms use the nodes with more capabilities in energy and bandwidth to act as cluster heads during the network lifetime while some other protocols use homogeneous nodes and periodically select new cluster head to distribute the load more evenly among all nodes. 4.

(26) 1.4 Problem Statement. PM. Energy consumption in WSNs represents a key issue in the lifetime of such networks. Due to the limitation in power source in WSNs, that is mostly small battery, many researchers have been trying to minimize the energy consumption in such networks. Especially in those networks that used for hostile areas such as volcanoes, battlefields and underwater, because once they scattered the nodes, it would be impossible to replenish them again.. IG. H. T. U. One of the main techniques that can be very effective to minimize the energy consumption in such networks is the routing algorithm. In the recent years, a wide variety of routing algorithms have been proposed to solve the highenergy consumption and to extend the network lifetime of WSNs. Although there are many routing algorithms for WSNs, but until now there is no algorithm can be consider as a perfect and optimal algorithm that can be suitable for all WSNs. Each algorithm is proposed to be applicable for specific applications. The type of application will be used can decide which issues (i.e. energy consumption, delay, load balancing and etc.) will be taken in consideration during the design of routing algorithm. In this study, we proposed a routing algorithm that can work perfectly with grid-based WSNs and compare its performance with two existing algorithms that proposed to work with same network structure. The performance comparison will be based on four metrics, energy consumption, network lifetime, network stability and first node and last node died.. R. 1.5 Scope of the Thesis and limitations. The concern of the thesis will be within the network layer. The type of routing algorithms would be limited to hierarchical routing algorithms for WSNs. The structure of the network oriented on grid-based topology (2Dimentional array). We cannot use this algorithm in a random distributed sensor nodes because our algorithm is using the (x, y) coordinates in order to achieve horizontal-vertical approach.. O.  . PY. In this part, we clarify the scope of our study by explaining the type of routing algorithm, the network structure and the layer that we work on.. . ©. C. . 1.6 Research Objectives 1) To design, simulate, and analyze a new energy efficient routing algorithm for grid-based WSNs. 2) To investigate the impact of our proposed algorithm on horizontalvertical approach using different networks size (small, medium, and large) networks.. 5.

(27) 3) To investigate the impact of utilizing the distance between the node and the base station along with the residual energy of corresponding node to decide upon the chain head nodes and main head node. 1.7 Thesis Organization The rest of the thesis is divided as follows.. PM. Chapter 2 – The literature review of hierarchical routing protocols for WSNs.. Chapter 3 – The description of materials and methods utilized for performing the study including working precepts of CCBRP, CCM and the proposed algorithm TSCP.. U. Chapter 4 – The explanation of simulation results and their discussion.. ©. C. O. PY. R. IG. H. T. Chapter 5 – The conclusion.. 6.

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