Power Management for Wireless Data Transmission Using Complex Event Processing

Mobile devices are increasingly being used in edge and fog computing environments to process contextual data collected by sensors. Although complex event processing (CEP) is a suitable approach for realizing context-aware services on mobile devices in these environments, existing mobile CEP engines do not leverage the full potential of modern mobile hardware/software architectures. In this paper, we present multimodal CEP, a novel approach to process streams of events on-device in user space (user mode), in the operating system (kernel mode), on the Wi-Fi chip (Wi-Fi mode), and/or on a sensor hub (hub mode), providing significant improvements in terms of power consumption and throughput. Multimodal CEP automatically breaks up CEP queries and selects the most adequate execution mode for the involved CEP operators. Filter, aggregation, and correlation operators can be expressed in a high-level language without requiring system-level domain-specific knowledge. Multimodal CEP enables developers to efficiently detect user activities, collect environmental conditions, or interpret operating system and network events. Furthermore, it facilitates novel context-aware services, demonstrated by a use case for gathering and analyzing mobility data by Wi-Fi probe request tracking.

Due to complex interdependencies and feedback loops between network layers and nodes, the development of adaptive applications is difficult. As networking applications respond nonlinearly to changes in the environment and adaptations, defining concrete adaptation rules is nontrivial. In this paper, we present the offline learner Fossa, which uses genetic programming to automatically learn suitable Event Condition Action (ECA) rules. Based on utility functions defined by the developer, the genetic programming learner generates a multitude of rule sets and evaluates them using simulations to obtain their utility. We show, for a concrete example scenario, how the genetic programming learner benefits from the clear model of the ECA rules, and that the methodology efficiently generates ECA rules which outperform nonadaptive and manually tuned solutions.

With the increasing demands of the data transmission system of information, wireless data transmission systems has been widely studied and applied. Especially the limited and specified environment, take the underground mine environment for example, has a high demand of flexibility. The paper describes the wireless video image data transmission system which is formed on the basis of DSP and RF chips, and provides the general systematic design and functional design. Furthermore, on this basis of the previous study, the paper has also completed the hardware and software design of the modules which consist two parts: data capture and compression modules; wireless data transfer and communication module. Regarding the further improvement of the use of DSP embedded technology in exploring the real-time wireless data transmission and communication system as well as its application level, there is a certain reference.

Goal of Complex Event Processing (CEP) is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible. Traditional CEP frameworks do not have built-in security features. Any intruder may enter into the system and may send incorrect data to CEP engine. It causes to miss some patterns or it may lead to wrong prediction of patterns. Traditional CEP systems can be exploited through one or the other way. CEP system malfunctions when it is exploited. There is no built in procedure to check the health status of traditional CEP systems. Traditional CEP systems do not have procedures to take appropriate actions when system is affected or going to affect. When CEP system identifies some patterns, it will alert corresponding personnel about the occurrence of the event. Sometimes alert message may be undelivered or unnoticed by the corresponding people. Traditional CEP systems will not provide delivery assurance of alert message. These are few security challenges to existing CEP systems. In this paper Secure Complex Event Processing Framework is proposed to address above security challenges. Proposed framework is generic and it is pluggable into any existing CEP systems.

Complex event processing is a key technology for analyzing event patterns in real-time data streams. It has been widely applied in IoT monitoring, financial risk control, smart manufacturing and other time-sensitive fields. Query patterns serve as the core matching mechanism in complex event processing systems. These patterns are primarily defined through selection policies, predicate constraints and timestamps to establish complex event detection rules. The selection policy is particularly important as it determines the sequential relationships between events. This directly affects the system's event matching efficiency and accuracy. However, in practical applications, due to different business requirements for event sampling frequencies, event streams often contain numerous consecutive events of the same type representing identical states. While all these events participate in complex event matching, traditional processing methods generate large numbers of semantically redundant partial matches. Analysis shows that only a very small proportion of these intermediate results eventually form complete matches, creating serious computational redundancy issues. This redundancy not only significantly increases system resource consumption but also reduces overall processing efficiency due to ineffective computations. To address this critical problem, this paper innovatively proposes a cache-based optimized selection policy. The core concept of this policy is to effectively reduce redundant matching results by extracting representative events from continuous event sequences. Experimental results show that this policy can significantly decrease the redundant matching rate while alleviating system load, providing new insights for optimizing complex event processing systems.

In-network computing using programmable networking hardware is a strong trend in networking that promises to reduce latency and consumption of server resources through offloading to network elements (programmable switches and smart NICs). In particular, the data plane programming language P4 together with powerful P4 networking hardware has spawned projects offloading services into the network, e.g., consensus services or caching services. In this paper, we present a novel case for in-network computing, namely, Complex Event Processing (CEP). CEP processes streams of basic events, e.g., stemming from networked sensors, into meaningful complex events. Traditionally, CEP processing has been performed on servers or overlay networks. However, we argue in this paper that CEP is a good candidate for in-network computing along the communication path avoiding detouring streams to distant servers to minimize communication latency while also exploiting processing capabilities of novel networking hardware. We show that it is feasible to express CEP operations in P4 and also present a tool to compile CEP operations, formulated in our P4CEP rule specification language, to P4 code. Moreover, we identify challenges and problems that we have encountered to show future research directions for implementing full-fledged in-network CEP systems.

Complex Event Processing (CEP) is a branch of stream data mining, which is usually used to discover event patterns. Generally, a complex event processing engine uses nonprocedural declaration language and state machine to define event patterns. It generates synopses of universe data by sliding window model, therefore could identify event patterns which users care about from rapidly changing and potentially infinite event stream in a relatively short time. Among all the data streams, monitoring data flow is easy to describe by events, so CEP is suitable for supervision or decision control for business system. However, CEP does not support backtracking to eliminate invalid data, which limits CEP in supervision or decision control applications. The traditional complex event processing try to solve this problem by defining more complicated event patterns to avoid invalid results, the side effect of which would be heavier workload onto the engine. For this problem, we propose an optimized complex-event-processing model OCM. OCM will decompose complicated event patterns into small ones according to their relations, and distribute these patterns to more than one CEP engines to hierarchically filter invalid data. It can eliminate invalid data with better performance than the traditional approaches. We have also implemented the prototype and designed experiments to test the effectiveness of OCM. We have applied this model into Aviation Application in EU FP6 project Bridge.

With increasing demands for agility and reactivity, scientific circles and leading ICT companies have recognized the need for EDA(event-driven architecture) and complex event processing in factory automation, signal processing, data acquisition, manufacturing, andother applications of industrial electronics. We propose an EDA framework for defining and processing complex events based onontologies. The main novelty of our approach is introduction of semantic aspects into EDA. Our framework enables highly expressivesemantic definitions of complex events and a higher level of semantic integration of real-time events with the domain and eventontologies. The main component of the framework is the complex event service that acts as an event sink for member events and as anevent source for inferred complex events. Our approach is complementary to existing approaches and can enhance their support forcomplex events that require higher expressivity and semantic descriptions. Ill. 4, bibl. 22, tabl. 1 (in English; abstracts in English andLithuanian).http://dx.doi.org/10.5755/j01.eee.113.7.618

Event-Condition-Action (ECA) rules offer a flexible, adaptive, and modular approach to realizing business processes. This article discusses the use of ECA rules for describing business processes in an executable manner. It investigates the benefits one hopes to derive from using ECA rules and presents the challenges in realizing business processes. These constitute a list of requirements for an (executable) business process description language, and we take them as a basis to investigate suitability of the concrete ECA rule language XChange in realizing a business process from the EU-Rent Case Study.

In recent years there is a huge increase in real-time data, which cannot be processed efficiently. Complex event processing has become a very important method to get meaningful information. However, supporting complex event detection in multiple sources environments is a challenging problem. To allow for inferring high level information from vast amounts of continuous arriving data. In this paper, we present a complex event processing system based on a novel distributed computing platform Storm, which goes further than distributing queries and achieves better scalability by parallelizing event detection, and also higher efficiency through the use of some optimizations. The experimental shows that the event processing system is effective and better scalability.

Mobile systems which include sensor networks, generate a continuous unmatched volume of primitive event streams with various properties where the interior semantic information of these events is generally very limited. Moreover, forwarding all these events to a central processing entity is not favored for limited-resource systems, it would deplete nodes' resources e.g., the energy. The goal of this work is to address these issues by extracting useful information locally, transmitting only this information and avoiding transmission of unnecessary low-level data between nodes. Therefore, an In-Network Distributed Complex Event Processing (INDCEP) based solution is proposed. INDCEP technology is used to perform the processing within the network by pushing complex event processing into network nodes. This research aims to develop a robust and high performance Distributed Complex Event Processing Engine for Mobile Systems (CEPEMS) based on aforementioned technology. It focuses on distributed event detection via disseminating distributed plans into a network of sensor nodes to perform complex event tasks. Furthermore, we present an optimization technique for the distributed queries and cost model to decide which query portions can be executed whether inside sensor nodes or at local CEP engine instance in a centralized point.

Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of only flat sequence queries. In this paper, we introduce our nested CEP query language NEEL for expressing nested queries composed of sequence, negation, AND and OR operators. Thereafter, we also define its formal semantics. Subtle issues with negation and predicates within the nested sequence context are discussed. An E-Analytics system for processing nested CEP queries expressed in the NEEL language has been developed. Lastly, we demonstrate the utility of this technology by describing a case study of applying this technology to a real-world application in health care.

With the increasing adoption of an event-based perspective in many organizations, the demands for automatic processing of events are becoming more sophisticated. Although complex event processing systems can process events in near real-time, these systems rely heavily upon human domain experts. This becomes an issue in application areas that are rich in specialized domain knowledge and background information, such as clinical environments. We utilize a framework of four techniques to enhance complex event processing with domain knowledge from ontologies to address this issue. We realize this framework in our novel approach of ontology-supported complex event processing, which stands in contrast to related approaches and emphasizes the strengths of current advances in the individual fields of complex event processing and ontologies. Experimental results from the implementation of our approach based on a state-of-the-art system show its feasibility and indicate the direction for future research.

Cyber-Physical System (CPS) is an emerging intelligent systems, which is considered as a combination of computers and physical world. Events are usually as a natural way for interactions and communications, which occur in the physical world and can be responded in the cyber world. Additionally, using complex event processing (CEP) technology, some complex events can be processed that are better suited for complicated and intelligent CPS. At first, a component-model framework is proposed for modeling CPS architecture based on event-driven pattern operational semantics, which provides a tool to understand the transition based on event-driven pattern in CPS. In order to improve the immense workload on centralized CEP, a mixed master-slaves CEP is proposed. Meanwhile, an architecture of CPS with master-slaves CEP is constructed and its execution mechanism is introduced in detail.

The ultimate goal of froth flotation is to increase the concentration of concentrate. The traditional flotation process basically relies on manual experience to adjust the flotation variables. The control of key factors is neither accurate nor timely, and it is difficult to guarantee the production efficiency. Most of the current researches on froth flotation are limited to image processing, and it is difficult to achieve the ideal index of production by relying solely on image detection technology. Faced with this situation, this paper proposes a research method of foam flotation modeling based on complex events for the one-sidedness of image processing. By constructing a complex event processing model of froth flotation, the traditional discrete froth flotation process could be effectively systematic by the event-driven approach, and the separated sensor data and image acquisition data are aggregated into the same dimension. The complex event processing technology is used to process the data associated with the two in parallel, which could optimize the operation of the froth flotation process more comprehensively.