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Improving delay-sensitive data routing in wireless sensor networks

Number of pages: 110 File Format: word File Code: 31043
Year: 2014 University Degree: Master's degree Category: Computer Engineering
Tags/Keywords: Clustering algorithm - Methods of collecting information - mobile well - Routing - Sensor networks - Wireless sensor networks
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  • Summary of Improving delay-sensitive data routing in wireless sensor networks

    Dissertation for M.Sc degree

    Trend: Software

    Abstract

    One of the challenges in the field of sensor networks is how to route and collect information from network nodes. Since these networks are limited in terms of energy and processing resources, they need special methods for routing and transmitting information with low energy consumption.

    To make the issue clearer, in normal sensor networks, there is a sink node in the middle of the network, and the information sensed by it is directed to the sink, but the low lifetime of the network due to the loss of nodes around the sink and the high end-to-end delay of the node due to the transmission of information through a relatively large number of nodes to reach the sink, are two basic problems in sending information in conventional wireless sensor networks. The two mentioned problems are considered as one of the most hot scientific topics today, and so far many works have been done to improve and increase efficiency in the field of information collection in this field. One of the proposed methods in this field is the method of using a moving well, in which information is collected from sensor nodes by moving the well in the entire network. This method forms the basis of many subsequent methods to solve the problems raised in the field of routing and information gathering in sensor networks, which have been discussed and investigated in this thesis, and finally, they have been compared with each other in terms of capabilities, weaknesses and strengths. let's show The presented method is based on the movement of the mobile well along the network, where the network is clustered and takes into account the priority of packets in sending information to the well. The obtained results show the improvement of the two mentioned parameters.

    Keywords:

    wireless sensor networks, mobile well, information collection methods, clustering, routing

    Chapter One:

    Research Overview

    1-1 Introduction

    Today, the discussion of remote control and monitoring systems is one of the most challenging topics in the field of electronic and computer science. Therefore, researchers are always looking for a solution to meet specific conditions and expected expectations; Under the same work conditions and quality, the lower the ratio of cost to efficiency, the more popular that method will be.

    In order to be aware of the changes in the surrounding environment or the condition of each set, we need a series of equipment known as sensors. The sensors provide the intended changes (physical or chemical changes) in the form of a response, in order to measure the amount of changes or the existence of changes. After collecting the required information, other operations can be performed based on the provided answer. 

    Recent advances in the field of electronics and wireless communication have made it possible to have multifunctional sensor nodes with low power consumption and low cost, which are very small in terms of size and can communicate with each other for short distances. According to the theory of sensor networks, these small sensor nodes have sensing, data processing and communication equipment. The main difference between sensor networks and other networks is their data-centric nature, as well as their very limited energy and processing resources, which has made the proposed methods for data transmission in other networks and even networks that have a structure similar to sensor networks to a large extent, such as ad-hoc networks, not applicable in these networks. The development process of these networks is such that these networks will certainly play an important role in our daily lives in the near future. Among the applications that are currently proposed for sensor networks and their number is increasing day by day, we can mention applications such as tracking in wide geographical areas, security systems, monitoring large structures, monitoring patients with sensitive conditions, as well as monitoring environmental parameters in areas where human presence is dangerous and many other applications. autonomously and in cooperation with other nodes pursue a specific goal.The nodes are close to each other, and each node can communicate with another node and provide its information to another node, and finally the status of the monitored environment is reported to a central node.

    Factors such as the economy of the system, the capabilities to be published, the large number of nodes have caused each node to have a series of hardware limitations. These limitations should be considered in the implementation of different systems in such networks. Some of the limitations of such networks are:

    -         Low cost: the final system should be economically viable. Because the number of nodes is very large and the estimated cost of each node is multiplied by a large number (amounting to several hundreds of thousands), so the more the cost of each node is reduced, the overall level of the network will be greatly saved and the cost of each node will be reduced to less than one dollar. Therefore, the lower this ratio is, the higher the efficiency, and on the other hand, in most cases, in order for the nodes not to attract attention or to be placed in some places, they need to have a very small volume.

    -         Low power consumption: the power supply in the nodes is limited and in practice it is not possible to replace or recharge it, so the available energy should be used in the best possible way.

    -         Low bit rate: due to the existence of other limitations, Practically, the rate of information transfer and processing in the nodes is relatively low.

    - Autonomy: Each node should be independent from other nodes and be able to perform its tasks according to its own diagnosis and conditions.

    - Adaptability: During the monitoring of the environment, the conditions may change and evolve at any time. For example, some nodes are damaged. Therefore, each node should be able to adapt its status to the newly created conditions.

  • Contents & References of Improving delay-sensitive data routing in wireless sensor networks

    List:

    Abstract..1

    Chapter One: General Research

    1-1 Introduction..4

    1-2 Statement of the Problem..11

    1-3 The Importance and Necessity of Research.14

    1-4 Research Questions:..16

    1-5 Hypotheses Research..16

    Chapter Two: A review of the research literature

    2-1 WSn with a fixed well.19

    2-1-1 The problem of energy exhaustion in the nodes around the well.19

    2-1-2 Improvement of the fixed well mode.20

    2-2 WSN with a mobile well.21

    2-2-1 Advantages of the mobile well. 22

    2-2-2 Disadvantages of using the mobile well. 23

    2-2-3 types of movement of the mobile well. 23

    2-2-3-1 Random movement. 23

    2-2-3-2 Fixed mobile networks. 25. 2-3 Sending delay-sensitive data. 29. 2-4 Using fixed and mobile wells at the same time. 29. 2-5. Method presented in EEQR. 32. 2-5-1 Introduction of the problem of blind spots. 33. 2-6 Routing in wireless sensor networks. 33. 2-6-1 Objectives. Routing. 33

    2-6-2 Optimal route determination criteria. 34

    2-6-3 Routing in wireless networks. 34

    2-6-3-1 Distance vector routing. 35

    2-6-3-2 Connection mode routing. 36

    2-6-3-3 Origin routing.

    2-7 methods of information dissemination.36

    2-7-1 Flooding method.37

    2-7-2 gossiping method.38

    2-7-3 SPIN method..40

    2-7-4 SPIN messages.40

    2-7-5 SPIN-1 is a three-step hand-waving method. 41

    2-7-6 opportunistic data aggregation. 43

    2-7-7 greedy data aggregation. 43

    2-7-8 nested query. 44

    2-8 clustering algorithm. 44

    2-8-1 criteria of clusters' desirability. 45

    2-8-2 Features of a suitable clustering algorithm. 46

    2-8-3 Disadvantages of clustering method. 46

    2-8-4 types of clustering. 46

    2-8-5 kmeans algorithm. 47

    2-8-5-1 Work steps. 47. 2-8-6 data preprocessing. 48

    2-8-7 types of features in clustering. 48

    2-8-8 main reasons for data preprocessing. 48

    2-8-9 main operations of data preprocessing. 49

    2-8-10 contaminations in clustering. 49

    2-8-11 methods used in pre-processing.50

    2-8-12 method (Low-Energy Adaptive Clustering Hierarchy).50

    2-8-12-1 details of LEACH algorithm.52

    2-8-12-2 advertising phase.52

    2-8-12-3 phase of group formation.53

    2-8-12-4 phase of program formation.53

    2-8-12-5 data transfer phase.54

    Chapter three: research method

    3-1 introduction..56

    3-2 first phase: initial establishment.58

    3-2-1 initial development..58

    3-2-2 clustering ..58

    3-2-2-1 Using the BSK-Means method for clustering nodes. 61

    3-2-3 Routing..63

    3-2-3-1 Routing to connect to the cluster head node. 64

    3-2-3-2 Routing the cluster head to the upper node. 64

    3-2-3-3 Routing to the mobile well.65

    3-2-3-4 Routing and creating a private channel between supernodes.65

    3-3 The second phase: Life and continued life of the network.66

    3-3-1 Prioritizing information.66

    3-3-2 Deciding to send data.67

    3-3-3 Deciding on the movement of the moving well. 68

    3-3-4 How the supernode connected to the moving well knows, to other supernodes and the station. 70

    3-3-5 Indirectly sending information of the moving well. 70

    3-4 Comparison of the presented methods. 71

    3-4-1 Well method fixed.71

    3-4-2 moving well method.72

    3-4-3 using fixed and moving well simultaneously (DualSink).73

    3-4-4 presented method.74

    3-5 plan and map..75

    3-6 advantages of using this method compared to other methods.76

    Chapter Fourth: Data analysis and performance evaluation 4-1 Performance evaluation 78 4-1-1 Simulation details 78 4-1-2 node energy consumption model 80 4-1-3 Comparison of energy consumed in the proposed method 4-1-4 The effect of the proposed method on packet loss rate 82.

    4-1-5 average number of steps taken to reach the well. 83

    Chapter five: conclusion and83

    Chapter five: conclusions and research proposals

    1-5 results. 86

    2-5 research proposals. 87

     

     

    Source:

    1. Energy efficient and QoS aware routing protocol for Clustered Wireless Sensor Network. Babar Nazir and Halabi Hasbullah. 2013, Computers and Electrical Engineering elsewhere.

    2. Static vs. Mobile sink: The in?uence of basic parameters on energy ef?ciency in wireless sensor networks. Majid I. Khan and Wilfried N. 2013, Computer Communications of ElSevier.

    3. Communication model and protocol based on multiple static sinks for supporting mobile users in wireless sensor networks. E. Lee and S. Park. s.l. : IEEE Transactions on Consumer Electronics, 2010.

    4. Novel service protocol for supporting remote and mobile users in wireless sensor networks with multiple static sinks. E. Lee, S. Park, J. Lee, S. Oh, S. Kim. s.l. : Wireless Networks 17, 2011.

    5. To Alleviate Congestion Using Hybrid Sink for Delay Sensitive Applications in Wireless Sensor Networks. K, Thanigaivelu and K, Murugan. 2010, Springer.

    6. Low energy adaptive clustering Hierarchy with deterministic cluster-head selection. M.J. Handy, M. Haase, D. Timmermann. s.l. : Proceeding of 4th International Workshop on Mobile and Wireless Communications, Network, 2002.

    7. Energy efficient hierarchical clustering for sensor networks. S.V.Manisekaran, R.Venkatesan. s.l. : Proceedings of international conference on computing, 2010.

    8. Efficient data collection in wireless sensor networks with path-constrained mobile sinks. S. Gao, H. Zhang, S. Das. s.l. : IEEE International Symposium on World of Wireless, Mobile and Multimedia, Networks, 2009.

    9. interdependence of congestion and contention. Vuran, M., Gungor, V., Akan. s.l. : ACM SenMetrics, San Diego, CA,, 2005.

    10. Mobile data collector strategy for delay-sensitive applications over wireless sensor networks. Richard W. N. Pazzi and Azzedine Boukerche. 2008, Elsevier.

    11. Sink mobility protocols for data collection in wireless sensor networks. I. Chatzigiannakis, A. Kinalis, S. Nikoletseas. s.l. : International Workshop on Mobility Management and Wireless Access, 2006.

    12. A dynamic multi agent-based local update strategy for mobile sinks in wireless sensor networks. J. Yu, E. Jeong, G. Jeon, D. Seo, K. Park. s.l. : International conference on computational science and applications, 2011.

    13. Congestion avoidance based on lightweight buffer management in sensor networks. Chen, S., Yang, N. s.l. : IEEE Transactions on Parallel and Distributed Systems, 2006.

    14. an autonomous moving strategy for mobile sinks in data-gathering sensor networks. Y. Bi, L. Sun, J. Ma, N. Li, I. A. Khan, C. Chen. s.l. : EURASIP Journal On Wireless Communications and Networking, 2007.

    15. Controlled sink mobility for extending wireless sensor networks lifetime. S. Basagni, A. Carosi, E. Melachrinoudis, C. Petrioli, Z. M. Wang,. s.l. : Journal of Wireless Networks, 2008.

    16. an autonomous moving strategy for mobile sinks in data-gathering sensor networks. Y. Bi, L. Sun, J. Ma, N. Li, I. A. Khan, C. Chen. s.l. : EURASIP Journal on Wireless Communications and Networking, 2007.

    17. Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications. Sang, Young and Xia, Ye. 2010, IEEE TRANSACTIONS ON MOBILE COMPUTING.

    18. Exploiting Sink Mobility for Maximizing Sensor Network Lifetime. Z.M. Wang, S. Basagni, E. Melachrinoudis,. s.l. : 38th Hawaii Int'lConf. System Science, 2005.

    19. A New MILP Formulation and Distributed Protocols for Wireless Sensor Networks Lifetime Maximization. S. Basagni, A. Carosi, E. Melachrinoudis, C. Petrioli. s.l. : IEEE Int Conf.Comm, 2006.

    20. Controlled Sink Mobility for Prolonging Wireless Sensor Networks Lifetime. S. Basagni, A. Carosi, E. Melachrinoudis, C. Petrioli, and Z. M. Wang. s.l. : Wireless Networks, vol.14, no.6, pp.831-858,, 2007.

Improving delay-sensitive data routing in wireless sensor networks
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