A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

The design and implementation of fuzzy query processing on sensor networks

Sensor nodes and Wireless Sensor Networks (WSN) enable observation of the physical world in unprecedented levels of granularity. A growing number of environmental monitoring applications are being designed to leverage data collection features of WSN, increasing the need for efficient data management techniques and for comparative analysis of various data management techniques. My research leverages aspects of fuzzy database, specifically fuzzy data representation and fuzzy or flexible queries to improve upon the efficiency of existing data management techniques by exploiting the inherent uncertainty of the data collected by WSN. Herein I present my research contributions. I provide classification of WSN middleware to illustrate varying approaches to data management for WSN and identify a need to better handle the uncertainty inherent in data collected from physical environments and to take advantage of the imprecision of the data to increase the efficiency of WSN by requiring less information be transmitted to adequately answer queries posed by WSN monitoring applications. In this dissertation, I present a novel approach to querying WSN, in which semantic knowledge about sensor attributes is represented as fuzzy terms. I present an enhanced simulation environment that supports more flexible and realistic analysis by using cellular automata models to separately model the deployed WSN and the underlying physical environment. Simulation experiments are used to evaluate my fuzzy query approach for environmental monitoring applications. My analysis shows that using fuzzy queries improves upon other data management techniques by reducing the amount of data that needs to be collected to accurately satisfy application requests. This reduction in data transmission results in increased battery life within sensors, an important measure of cost and performance for WSN applications

MLDS: A Flexible Location Directory Service for Tiered Sensor Networks

Many emergent distributed sensing applications need to keep track of mobile entities across multiple sensor networks connected via an IP network. To simplify the realization of such applications, we present MLDS, a Multi-resolution Location Directory Service for tiered sensor networks. MLDS provides a rich set of spatial query services ranging from simple queries about entity location, to complex nearest neighbor queries. Furthermore, MLDS supports multiple query granularities which allow an application to achieve the desired tradeoff between query accuracy and communication cost. We implemented MLDS on Agimone, a unified middleware for sensor and IP networks. We then deployed and evaluated the service on a tiered testbed consisting of tmote nodes and base stations. Our experimental results show that, when compared to a centralized approach, MLDS achieves significant savings in communication cost while still providing a high degree of accuracy, both within a single sensor network and across multiple sensor networks

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor