Process Model to Predict Nondeterministic Behavior of IoT Systems - PowerPoint PPT Presentation

Process Model to Predict Nondeterministic Behavior of IoT Systems Yeongbok Choe and Moonkun Lee 31 October 2018 Chonbuk National University Republic of Korea

Contents 1. Overview 2. dTP-Calculus 3. SAVE 4. Example 5. Analysis 6. Conclusions & Future Research 7. Q&A 2

1. Overview

Internet of Things Definition  Network of physical devices, vehicles, buildings and other items  that enable these objects to collect and exchange data 1 Applications  Media  Healthcare systems  Industry 4.0  …  Challenges  Safety  Security  Correctness  Complex dependencies  Cyber-Physical-Systems  Requirements  Specification  2 Verification  1 https://en.wikipedia.org/wiki/Internet_of_things 4 2 https://www.mobinius.com/industry-4-0-iot/

Motivation  Probability in IoT  Nondeterministic behavior of each devices  Complex  Non-predictable  Analysis and design are difficult 1 1 https://www.acmicpc.net/problem/13405 5

Motivation  Probability in IoT  Probability  In design step, statistics data is used  Error occurred  Route selection  Probability is used to analyze  Risk management  Behavior prediction 1 1 https://blog.storagecraft.com/business-continuity-statistics-tech/ 6

Approach  Process algebra  Specify IoT systems  Analyze IoT systems  Specify probability density function  Probabilistic analysis 7

2. dTP-Calculus

dTP-Calculus Geographical Space - Processes - Actions δ - Interactions - Space Movements - Time - Probability τ ρ Communication Control Synchrony 9

Syntax 10

Syntax  Probability  Probabilities specification  Discrete distribution  Probability density function specification  Normal distribution  Exponential distribution  Uniform distribution 11

Syntax  Probabilistic choice  Discrete distribution  𝑄 𝑑 + 𝐸 𝑅 𝑑  𝑑 : Probability  Example  𝑄 0.2 + 𝐸 𝑅 0.5 + 𝐸 𝑆{0.3} 1 1 https://en.wikipedia.org/wiki/Probability_distribution 12

Syntax  Probabilistic choice  Normal distribution  𝑄 𝑑 + 𝑂(𝜈,𝜏) 𝑅 𝑑  𝑑 : Variable area (Condition)  𝜈 : Mean  𝜏 : Standard deviation  Example  𝑄 𝑤 > 52 + 𝑂(50,5) 𝑅{𝑤 ≤ 52} 1 1 https://en.wikipedia.org/wiki/Normal_distribution 13

Syntax  Probabilistic choice  Exponential distribution  𝑄 𝑑 + 𝐹(𝜇) 𝑅 𝑑  𝑑 : Variable area (Condition)  𝜇 : Frequency  Example  𝑄 𝑤 > 2.5 + 𝐹(0.33) 𝑅{𝑤 ≤ 2.5} 1 1 https://en.wikipedia.org/wiki/Exponential_distribution 14

Syntax  Probabilistic choice  Uniform distribution  𝑄 𝑑 + 𝑉(𝑚, 𝑣) 𝑅 𝑑  𝑑 : Variable area (Condition)  𝑚 : Lower bound  𝑣 : Upper bound  Example  𝑄 𝑤 > 5 + 𝑉(3,7) 𝑅{𝑤 ≤ 5} 1 1 https://en.wikipedia.org/wiki/Uniform_distribution_(continuous) 15

Syntax  Probabilistic choice  Summation of the probabilities should be 1  Discrete distribution  𝑄 1 𝑑 1 + 𝐸 𝑄 2 𝑑 2 + 𝐸 ⋯ + 𝐸 𝑄 𝑜 {𝑑 𝑜 } 𝑜  𝑑 𝑗 = 1 𝑗=1  Normal distribution, uniform distribution  𝑄 1 𝑑 1 + 𝐺 𝑄 2 𝑑 2 + 𝐺 ⋯ + 𝐺 𝑄 𝑜 {𝑑 𝑜 } 𝑜  𝑑 𝑗 = ℝ 𝑗=1  ∀𝑗, 𝑘 ∈ 𝑦 𝑦 ∈ ℕ, 1 ≤ 𝑦 ≤ 𝑜 𝑢ℎ𝑓𝑜 𝑑 𝑗 ∩ 𝑑 𝑘 = ∅ (𝑔𝑝𝑠 𝑗 ≠ 𝑘)  Exponential distribution  𝑄 1 𝑑 1 + 𝐺 𝑄 2 𝑑 2 + 𝐺 ⋯ + 𝐺 𝑄 𝑜 {𝑑 𝑜 } 𝑜  𝑑 𝑗 = 𝑦 𝑦 ≥ 0, 𝑦 ∈ ℝ} 𝑗=1  ∀𝑗, 𝑘 ∈ 𝑦 𝑦 ∈ ℕ, 1 ≤ 𝑦 ≤ 𝑜 𝑢ℎ𝑓𝑜 𝑑 𝑗 ∩ 𝑑 𝑘 = ∅ (𝑔𝑝𝑠 𝑗 ≠ 𝑘) 16

Syntax  Probabilistic choice  Error  Summation of the probabilities is less than 1  𝑄 𝑑 1 + 𝐺 𝑅 𝑑 2 Undefined area 𝑑 1 𝑑 2 17

Syntax  Probabilistic choice  Error  Summation of the probabilities is greater than 1  𝑄 𝑑 1 + 𝐺 𝑅 𝑑 2 Overlapped area 𝑑 1 𝑑 2 18

Syntax  Probabilistic choice  Error  Summation of the probabilities is greater than 1  𝑄 𝑑 1 + 𝐺 𝑅 𝑑 2 𝑄 𝑑 1 𝑄 𝑑 1 ∥ 𝑅 𝑑 2 𝑅 𝑑 2 𝑑 1 𝑑 2 19

3. SAVE

SAVE  SAVE  Specification, Analysis, Verification, and Evaluation  Based on ADOxx meta-modeling platform 21

Specification - Visualization  Graphic language  System View  In-the-Large (ITL) Model  Representation  Interactions of each processes  Process View  In-the-Small (ITS) Model  Representation  Detailed actions of each processes 22

System View 23

System View 24

Process View 25

Process View 26

Analysis  Analysis  Path analysis  All possible execution path  Generate simulation model  Simulation  Simulate the system based on a path 27

Simulation model 28

Verification  Verification  Behavior analysis  Analyze the system behavior  Generate geo-temporal space model  Requirements Verification  Verify a set of system requirements 29

Analysis model 30

4. Example

Example  Smart Emergency Evacuation System  Sensors  Fire detection  Control System  Evacuation alarm  Rescue request  Evacuation route notification 1 1 http://www.arpel.com/business-services/fire-detection/ 32

Example Control System Building Sensor A Sensor B 33

Example  Textual Specification 34

Example  Textual Specification 35

Example  Textual Specification 36

Example  Textual Specification 37

Example  Probabilistic choice  Building  𝑇𝐵 𝐺𝑗𝑠𝑓 0.5 + 𝐸 𝑇𝐶 𝐺𝑗𝑠𝑓 {0.5}  Discrete distribution  P1  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,3 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  Normal distribution  P2  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,8 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  Normal distribution 38

Example  Probabilistic choice  Building  𝑇𝐵 𝐺𝑗𝑠𝑓 0.5 + 𝐸 𝑇𝐶 𝐺𝑗𝑠𝑓 {0.5}  Discrete distribution  P1  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,𝟒 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  Normal distribution  P2  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,𝟗 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  Normal distribution  P1 and P2 have same type’s choice, but parameters are different. 39

5. Analysis

Analysis  Execution path  8 paths 41

Analysis  Execution path  8 paths  Fire: The location where the fire occurred  Stay: P1 or P2, confined in Building  Out: P1 or P2, escaped from Building Path 1 Path 2 Path 3 Path 4 Path 5 Path 6 Path 7 Path 8 Fire Stair A Stair A Stair A Stair A Stair B Stair B Stair B Stair B P1 Stay Stay Out Out Stay Stay Out Out P2 Stay Out Stay Out Stay Out Stay Out 42

Analysis  Probability  Mathematical analysis  Calculate the probability  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,𝟒 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  ∅ ⋅ … ⋅ 0.2023 + 𝐸 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 0.7977  ∅ ⋅ … ⋅ 𝑤 < 2.5 + 𝑂 5,𝟗 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 𝑤 ≥ 2.5  ∅ ⋅ … ⋅ 0.3773 + 𝐸 𝑝𝑣𝑢 2𝑜𝑒 ⋅ … ⋅ 0.6227 Path 1 Path 2 Path 3 Path 4 Path 5 Path 6 Path 7 Path 8 % 3.82 6.3 15.05 24.83 3.82 6.3 15.05 24.83 43

Analysis  Simulation  Simulate the system based on the probabilities 44

Analysis  Simulation  Simulate the system based on the probabilities Probability Number of Simulation Path 1 Path 2 Path 3 Path 4 Path 5 Path 6 Path 7 Path 8 1,000 3.5 6.2 14.2 25 3.5 7.5 14.4 25.7 1,000,000 3.79 6.32 15.04 24.86 3.82 6.32 14.98 24.87 45

Analysis  Simulation  Simulate the system based on the probabilities Probability Number of Simulation Path 1 Path 2 Path 3 Path 4 Path 5 Path 6 Path 7 Path 8 1,000 3.5 6.2 14.2 25 3.5 7.5 14.4 25.7 1,000,000 3.79 6.32 15.04 24.86 3.82 6.32 14.98 24.87 Simulation results Path 1 Path 2 Path 3 Path 4 Path 5 Path 6 Path 7 Path 8 % 3.82 6.3 15.05 24.83 3.82 6.3 15.05 24.83 Mathematical analysis 46

Analysis  Prediction of occurrence probability  In complex system, mathematical analysis is difficult.  Through the simulation, paths and probabilities are derived.  Automation  SAVE tool  Specify and analyze the system  Generate all possible paths  Simulation based on probability 47

6. Conclusions

Approach Geographical Space - Processes - Actions δ - Interactions - Space Movements - Time - Probability τ ρ Communication Control Synchrony 49

Approach  Various probability specification  Discrete distribution  Normal distribution  Exponential distribution  Uniform distribution 50

SAVE 51

SAVE  Simulation 52