||Grid-based hole recovery mechanism in hybrid wireless sensor networks
||Department of Computer Science and Information Engineering
hybrid sensor network
||無線感測器網路(Wireless Sensor Networks, WSNs)其技術可廣泛應用在許多領域中，尤其是環境監測。然而，由於部署無線感測器節點時的不平均，或有障礙物例如湖和山丘的存在，或感測器節點的電量耗盡與被外力破壞等因素，進而造成無線感測器網路中存在著空洞(Hole)，而這些空洞會使無線感測器網路的效能降低。因此，如何找出這些空洞的位置，並利用這些空洞位置所獲得的資訊進行空洞修復，提升無線感測器網路之效能，是一個相當重要的研究議題。
||In wireless sensor networks, the nodes are typically empowered with scarce energy resource and limited computing power. The network can not get fully connectivity due to the randomly deploy static sensor nodes which may cause the hole problem. However, the network performance could be improved by the high coverage ratio because of saving the energy for transmitting. Hence, the hole problem is one of the important issues in wireless sensor networks.
We proposed a hole recovering mechanism based on grid architecture with mobile sensor nodes. The virtual force theory is used for determining which mobile sensor node should recover the hole. The proposed mechanism could efficiently maintain high coverage ratio and prolong the entire network lifetime. The simulation results demonstrate that our mechanism indeed recover routing holes and prolong the network lifetime.
List of Figures V
List of Tables VI
1. Introduction 1
2. Related Works 4
2.1 Wireless Sensor Networks 4
2.1.1 Hardware Components 6
2.1.2 Charateristic Requirements 9
2.1.3 Salient Features of Sensor Networks 11
2.1.4 Common Design Problems in WSNs 14
2.2 Coverage and Connectivity Issues in Wireless Sensor Networks 17
2.2.1 Mathmatical Frameworks 18
22.214.171.124 Sensing Model 18
126.96.36.199 Communication Model 19
188.8.131.52 Coverage Model 21
2.2.2 Coverage based on Exposure Paths 23
184.108.40.206 Minimal exposure path: Worst-case coverage 23
220.127.116.11 Maximal exposure path: Best-case coverage 26
18.104.22.168 Maximal breach path: Worst-case coverage 27
22.214.171.124 Maximal support path: Worst-case coverage 28
2.2.3 Coverage based on Sensor Deployment Strategies 29
126.96.36.199 Imprecise detection algorithm (IDA) 29
188.8.131.52 Potential field algorithm (PFA) 30
184.108.40.206 Distributed self-spreading algorithm (DSSA) 32
220.127.116.11 Bidding Protocol (BIDP) 33
18.104.22.168 Energy-efficient Coverage Hole Self-repair in Mobile Sensor Networks (DSEPA) 33
22.214.171.124 On-demand Deployment Algorithm for a Hybrid Sensor Network (On-demand) 34
3. Grid-based hole recovery mechanism 35
3.1 Virtual Force Formulation 39
3.2 Network Initiation Phase 40
3.2.1 Griding Phase 40
3.2.2 Hole Detecting Phase 47
3.2.3 Hole Recovering Phase 53
3.3 Network Maintaining Phase 57
4. Simulation and Results 61
4.1 Simulation Environment 61
4.2 Simulation Results and Analysis 63
5. Conclusion and Future Works 68
Appendix A. Publication List 75
Appendix B. “Grid-based Mobile Target Tracking Mechanism in Wireless Sensor Networks” JOURNAL OF COMMUNICATIONS 79
Appendix C. “QPPS : Qos Provision Packet Scheduling Algorithm in High Speed Downlink Packet Access” JOURNAL OF WSEAS TRANSACTIONS ON COMMUNICATIONS 87
List of Figures
Figure 2-1: Main sensor node hardware components 6
Figure 2-2: Example of probabilistic sensing model 19
Figure 2-3: Example of communication model 20
Figure 3-1: System framework 37
Figure 3-2: Example of system environment 38
Figure 3-3: Relation between R and d 41
Figure 3-4: Example of numbering grid 42
Figure 3-5: Example of gridding network scenario 44
Figure 3-6: Procedures of gridding phase 45
Figure 3-7: Procedures of message forwarding 47
Figure 3-8: Procedures of hole detecting 51
Figure 3-9: Example of completing hole detecting phase 52
Figure 3-10: Procedures of selecting MNs 55
Figure 3-11: Procedures of network maintaining phase 58
Figure 4-1: Message complexity 64
Figure 4-2: Coverage ratio 65
Figure 4-3: Energy consumption 66
Figure 4-4: System lifetime 67
List of Tables
Table 3-1: Grid Information Table 43
Table 3-2: Det_Hole_Message Format 48
Table 3-3: Det_Hole_Ack Format 49
Table 3-4: Hole Grid Information Table 53
Table 4-1: Simulation parameters 62
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