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Dependability and Performance of Cyber Physical Systems Using UAV Relay WSNs

초록/요약

As cyber physical system (CPS) is often used in safety critical areas, any failure on its components could result in a degradation of the physical state, which then causes major harm to life and/or property. Since the concept of dependence leads to that of trust, the components of the CPS should be dependable to each other to deliver the intended services as specified without failing during its operation. In this study, Markov chain is applied to model and analyze the component dependability of the CPSs. Recovery techniques are also proposed to guarantee a high level of dependability to take care of assuring the continuity of system operation. To avoid unexpected failures of the CPS, due to degradation of its physical component, maintenance could be utilized as a recovery technique to extend its life time. In relation with maintenance as a recovery technique, the effect of involving maintenance is demonstrated during the deterioration state of the physical entity on the availability of the CPS. In addition to that, since reliability and economic factors are of equal importance in maintaining an equipment, the optimum time of the constant-interval for preventive replacement policy is computed as it is suited for complex systems in a manner that minimizes cost. CPSs with unmanned aerial vehicles(UAVs) could be taken as mobile sensors, and are extensively used for remote sensing in various applications, such as watering plants, agricultural and environmental monitoring. In such kinds of applications, in which WSN technology is an integral component of the CPS design, prolonging the lifetime of the network by reducing sensor nodes power that might be wasted due to data transmission is a challenge as sensor nodes are battery powered and are often difficult to be recharged. Since UAVs replace the multi-hop communication among nodes, they can be utilized as a solution to prolong the life time of the WSNs. However, the network lifetime is extended in exchange for higher data acquisition latency. Heuristic algorithms, such as, Nearest Neighbor heuristic TSP algorithm (NN), have been proposed for reducing the data acquisition latency due to the NP-hardness of the TSP whose computational complexity increases exponentially with the increment of number of sensor nodes. In this study, efficient schemes that modify the previous NN scheme are proposed to gain a reduction in the data acquisition latency with no significant change in computational time. Analytical and simulation results have demonstrated that the proposed schemes outperform the previous NN scheme up to 78.64% in reducing the data acquisition latency. Among the proposed schemes, the directional NN scheme directed to the next nearest node (DDNN) attains the shortest tour distance. However, the DDNN scheme does not consider the reliability of the system in case of node or link failures. To collect the sensing data rapidly and reliably, the DDNN scheme should be able to react to node or link failures and manage the data transmissions effectively in the network. And hence, an extension of the DDNN scheme, fault tolerable DDNN scheme (FT-DDNN) is proposed to enhance the fault tolerant capability while reducing the data acquisition latency of the UAV. The Performance analyses have demonstrated that the proposed scheme tolerates fault in case of malfunctions of sensors due to node/link failures and improves the detection rate of the DDNN scheme up to 34.93% at the cost of a little bit distance.

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목차

1. General Introduction 1
1.1 Background 1
1.2 Contributions 5
1.3 Organization of the document 7
2. Dependability Analysis of Cyber Physical Systems 9
2.1 Introduction 9
2.2 Related works 9
2.3 Method 16
2.4 Basic dependability analysis of CPS 19
2.4.1 Systems availability as a function of error and recovery rates of the SW entity of the CPS 20
2.4.2 Systems availability as a function of error and recovery rates of the HW entity of the CPS 22
2.4.3 Systems availability of the system as a function of the error and recovery rates of the physical entity of the CPS 23
2.5 Recovery techniques to assure a high level of dependability 24
2.6 Summary 26
3. Optimum Maintenance Interval for the Physical Component of CPSs 27
3.1 Introduction 27
3.2 Effects of involving maintenance during the deterioration state 29
3.2.1 Involving maintenance action only on the failed state 29
3.2.2 Involving maintenance action in both the degradation and the failed states 31
3.2.3 Percentage increment of availability due to maintenance in the deterioration state 34
3.2.4 Sensitivity of the recovery rates to the availability of the system 35
3.3 Optimizing Maintenance Period 37
3.3.1 Weibull distribution 37
3.3.2 Preventive maintenance replacement models 38
3.3.3 Numerical example 41
3.4 Summary 43
4. Reducing Data Acquisition Latency of UAV Relay WSN for CPSs 44
4.1 Introduction 44
4.2 Related works and problem description 47
4.3 The proposed algorithms 50
4.3.1 By-passing of nodes in the NN algorithm (PNN) 50
4.3.2 The directional NN algorithm (DNN) 53
4.3.3 The directional NN algorithm directed to the next nearest node (DDNN) 55
4.4 Performance evaluation and results 57
4.4.1 Analytical demonstration 57
4.4.2 Simulation results 60
4.5 Summary 68
5. Fault Tolerable DDNN Scheme for Reducing Data Acquisition Latency of UAV Relay WSN in CPSs 70
5.1 Introduction 70
5.2 Related works and problem description 72
5.3 The proposed algorithm: The fault tolerable DDNN algorithm (FT-DDNN) 76
5.4 Performance analysis of FT-DDNN 82
5.5 Performance evaluation and results 87
5.5.1 Comparison of distances covered by the UAV among the NN, DDNN and FT-DDNN algorithms 87
5.5.2 Comparison of detection rates of the NN, DDNN and FT-DDNN algorithms 91
5.6 Summary 95
6. Conclusion and Future Works 96
References 99

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