Extending Traditional Algorithms for Cyber-Physical Systems

1. Extending Traditional Algorithms for Cyber-Physical Systems Sumeet Gujrati and Gurdip Singh Kansas State University

2. Problem Statement • Define mutual exclusion, predicate detection, and global state recording problems over physical systems (PhyS), and implement the solutions over a cyber-infrastructure (CyS) which monitors the PhyS (thus forming a CPS), and maintains the state of PhyS. • Define a spatial model of PhyS, and then define the model of CPS. • Define an event model for CPS. • Device techniques so that CyS maintains a consistent state of PhyS, as inconsistencies may arise due to sensing delays, missing sensor data, and limited sensing range of sensors. • Device an approach to model user behaviors, as users are not passive entities. • Perform detailed simulation of proposed algorithms.

3. Related Work • The field of CPS is inspired by WSN. The facts that the algorithm must adapt itself depending on user’s behavior, and the algorithm manipulating user’s behavior (by helping him) are missing. • The problem of resource detection (e.g., detecting free parking spaces [6]) has been widely studied, with their own limitations. • Space models such as [3] are suitable for our work with few modifications. Space and time aware predicates have been studies, such as in [3,4,1], but they have their own shortcomings. • [5] discusses constructing snapshots for a smart construction site. It is more focused towards interpreting missing sensor data.

4. Proposed Idea • We define an hierarchical spatial model of the PhyS. • We define a graph based model of CPS. • We define user behaviors in terms of their capabilities. • We define an event model for CPS. • We define mutual exclusion and predicate detection algorithms (which are implemented in CyS) for different types of user behavior. For example, a disciplined user will always follow CyS instructions, whereas, a complete random user will act on his own. • The algorithms we propose make sure that they adapt themselves to the user behavior, and at the same time, try to manipulate user behavior.

5. Initial Results We have performed extensive simulation study [2] of our proposed mutual exclusion algorithms. We came up with three types of user behaviors: 1) disciplined, 2) random, 3) random with CyS helping. In the figure NoCS corresponds to the results when there is no CyS support. Users act on their own. SPRA-1-2-3 represents three user behaviors. Np is the number of users. AT is acquire time (difference between time when the user send a request, and the time at which the user acquires the resource). This experiment models physical system as a grid of 8X8 locations, and number of resources are constant (3).A user takes 3 seconds to move from a location to other.

6. Future Plans • Immediate goal is to simulate predicate detection and global state recording algorithms.

7. Open Problems • Currently, we investigated CPSs which operate inside a physical system such as a hospital or a factory. One can study CPSs which are combination of multiple such CPSs, such as, a series of hospitals. Middleware to coordinate among multiple CPSs is required to be studied. • The mutual exclusion problem we studied required CyS to locate one resource for a user. In a more complex scenarios, users may need to reserve multiple resources, and in that case, CyS should be able to reserve those resources such that the users movements are minimized. • The simulation we developed is very naïve. Though it adequately simulates our algorithms, it can be made more robust by providing functionality, so that one can design the layout of PhyS, place users in desired locations, change capabilities of users, algorithms on CyS and so on.

8. References [1] A. D. Kshemkalyani. Immediate detection of predicates in pervasive environments. Journal of Parallel and Distributed Computing, 72(2):219 – 230, 2012. [2] S. Gujrati and G. Singh, “Mutual exclusion in cyber-physical systems,” in 1st International Conference on Sensor Networks, February 2012. [3] S. Chandran and J. Joshi, “Lot-rbac: A location and time-based rbac model,” in Web Information Systems Engineering WISE 2005, vol. 3806, pp. 361–375. [4] C. A. Ardagna, M. Cremonini, E. Damiani, S. D. C. di Vimercati, and P. Samarati, “Supporting location-based conditions in access control policies,” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security. ACM, 2006, pp. 212–222. [5] V. Rajamani and C. Julien, “Blurring snapshots: Temporal inference of missing and uncertain data,” in Proceedings of the IEEE International Conference on Pervasive Computing and Communications, 2010. [6] Lee, S.; Yoon, D.; Ghosh, A.; , "Intelligent parking lot application using wireless sensor networks," Collaborative Technologies and Systems, 2008. CTS 2008. International Symposium on , vol., no., pp.48-57, 19-23 May 2008

9. Following slides are backup slides.

10. TDS vs CPS

11. 2. CyS finds nearest WC Fig. 1: CyS superimposed on a hospital. 1. N1 requests for a WC. 3. CyS sends path to WC1. 4. N1 moves to WC1. CyS Path to WC1 Search Motivational Example : Mutual Exclusion Request

12. 1. N1starts moving patient to Room3. 2. N1moves away from patient. 3. CyS generates alert. CyS Alert Motivational Example : Predicate Detection – Patient moved to other location without a nurse being accompanied.

13. Mutual Exclusion Algo WC1: Free, WC2: Free, WC3: Free Res 1. N1 sends a request to search WC. 2. CyS finds and reserves WC1. 3. CyS sends path to WC1 to N1. 4. N1 moves towards WC1. 4. N6 takes WC1 to some other location. Path to WC1 Challenges Where is WC? Request

14. Global State Recording NS1.N={N1,N2} ICU1.N={} ICU1.P={P1} R3.WC={WC2} Where are WC1 and N1? Challenges

15. Spatial Model • Logical vs physical areas • Fine grained vs coarse grained areas • Contains relation • Parent child relation • Reachability edges • Path between two areas • Shortest path between two areas • Distance between two areas • PAT: a tree structure of the physical system

16. Event Model