Research On Multi-modal Data Based Event Monitoring In Cyber-physical Systems

Cyber-Physical System(CPS)is a large-scale, heterogeneous and distributive systemcollaborating sensors, actuators, controllers and network components. Computation pro-cess and physical world are tightly integrated in cyber-physical systems. The research incyber-physical system which has caused extensive concern by the attentions of academicand the business community is becoming more and more important in real-world appli-cations. The key issue of applying cyber-physical systems is to observe and cognize thephysical world accurately and comprehensively. CPS obtains the information of physicalworld by analyzing the data generated by sensors. CPS is an integration of heterogeneousnetworks(such as wireless sensor networks, 3G, bluetooth, etc.). Various types of data aregenerated by different sensor components from heterogeneous networks. In the researchof cyber-physical systems, it is important to study the methods of fusion multi-modal da-ta to accurately describe the state of the physical world. The event can effectively reflectthe state condition of the physical world. This dissertation will study the event monitor-ing problem in the cyber-physical systems in the multi-modal data based event model,multi-modal data based event coverage problem, multi-modal data based event detection,multi-modal data based event scheduling and so on.First, in order to abstract and describe the event in cyber-physical systems, this thesisstudies the problem of multi-modal data based event model. In CPS, the informationabout physical world is obtained by sensors deployed in the system area. Generally, aCPS is composed by several heterogeneous wireless sensor networks. Heterogeneoussensor nodes in these networks have different capabilities in terms of sensing, computingand communication. Jointly processing the multi-modal data generated by heterogeneoussensors is an important problem. This dissertation proposes multi-modal data based eventmodel, which interstates multi-modal data by events. The definitions and descriptions ofatomic event and composite event, the composition rules of the event are proposed in thisdissertation. The multi-modal data based event model with confidence is also devised.The minimum amount of data describing a state of the physical world or the informationof an object is defined as an atomic event, with only one type of data involved. Severalatomic events can be composed into a composite event using predefined combinationrules. The composite event generated by multi-modal data is beneficial to comprehendthe physical world. The confidence which indicates the support rating for the occurrenceof the composite event is introduce in the event model. Thus the model can provideflexible and e?cient approximate event processing service.Second, in order to ensure the accuracy and the integrity of the event monitoring, thisthesis studies the problem of multi-modal data based event coverage in Cyber-PhysicalSystems. Event coverage is an essential requirement for event monitoring and is the keyto success of accurate and rapid event detection. The quality of coverage reflects how wella system is monitored. An atomic event only involving single-mode data for a specificproperty is the primary focus of traditional converge problems. However, the monitoredphenomena is always complicated and the occurrence of an event cannot be determinedpurely based on single-mode raw data. Thus, the composite event coverage problem isfirstly formulated and studied in this thesis, where a composite event is a combination ofseveral atomic events and its occurrence is jointly determined by many atomic events ormulti-modal data. The goal of composite event coverage is to determine the best budgetallocation policy to specify the amount of different types of nodes so that the monitoringsystem achieves the optimal event monitoring performance under the budget constraint.The optimal coverage quality problem is formulated and the complexity is analyzed. Twoexact algorithms and an approximate algorithm are proposed to solve the coverage qualityoptimization problem. The exact algorithms are applicable to the applications with smallnumber of types or demanding more accurate results. The approximate algorithm is suit-able for the applications with large number of types and numerous nodes. Moreover, thee?ciency of the exact algorithms and the approximate ratio of the approximate algorithmare also studied.Third, in order to collect event information rapidly, this thesis studies the problem ofmulti-modal data based event detection. Event detection is one of the fundamental tasksin monitoring systems. The complex relations among different types of data make com-posite event detection a tough task. Traditional methods need to collect all the event data,which causes huge energy consumption. However, the multi-modal data of the compositeevent are not totally independent. The occurrence of each atomic event provides supportor can be seen as the evidence for the occurrence of a composite event. By exploiting thecorrelations between different types of data, the complex composite event can be detectedapproximately by partial data. In order to reduce the energy consumption, we formu-late the optimal transmitting scheme problem and the goal of which is to decide whichtypes of atomic event should be transmitted to the base station such that the confidenceof the composite event by merging all the received atomic events satisfies the confidencethreshold at the same time the total transmitting cost is minimized. The complexity ofthe optimal transmitting scheme problem is analyzed and a dynamic programming basedalgorithm as well as a greedy based algorithm are proposed to solve this problem fordifferent scenarios.Forth, In order to extend the service cycle of event monitoring, this thesis studiesthe scheduling problem for multi-modal data based event. The sensor nodes are usuallypowered by a battery. Thus, it is essential to reduce the energy consumption and prolongthe lifetime of the system as well as guarantee the monitoring quality. By exploiting theredundancy of the sensing data, node scheduling provides an effective method for solvingthe energy bottleneck problem. On the basis of the approximate event detection, multi-modal data based event scheduling problem is proposed which aims at developing optimalnode scheduling policy to reduce the energy consumption and maximize the lifetime of thesystem to ensure the sustainability of event monitoring. The complexity of the schedulingproblem is studied in this thesis, and the problem is proved to be NP-complete. Theapproximate algorithm is devised to solve event scheduling problem and the performanceguarantee is proved.