When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

VSMURF:A Novel Sliding Window Cleaning Algorithm for RFID Networks

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things (IoT) and is used in many areas, such as mobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, which makes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small. This paper proposes a novel sliding window cleaning algorithm called VSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag’s speed is low or high. In particular, if the velocity parameter is set to 2 m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks

A multi-agent based system RFID middleware for data and device management

Published ArticleRadio-frequency Identification (RFID) technology promises to revolutionize business processes. While RFID technology is improving rapidly, a reliable deployment of this technology is still a significant challenge impeding its widespread adoption. In this paper we provide a brief overview of some common fundamental characteristics of RFID data and devices, which pose significant challenges in the design of RFID middleware systems. In addition, the development of a multi-agent RFID middleware solution to address the RFID data and device management challenges is discussed

An Approach for Removing Redundant Data from RFID Data Streams

Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches

State-Slice: A New Stream Query Optimization Paradigm for Multi-query and Distributed Processing

Modern stream applications necessitate the handling of large numbers of continuous queries specified over high volume data streams. This dissertation proposes novel solutions to continuous query optimization in three core areas of stream query processing, namely state-slice based multiple continuous query sharing, ring-based multi-way join query distribution and scalable distributed multi-query optimization. The first part of the dissertation proposes efficient optimization strategies that utilize the novel state-slicing concept to achieve maximum memory and computation sharing for stream join queries with window constraints. Extensive analytical and experimental evaluations demonstrate that our proposed strategies is capable to minimize the memory or CPU consumptions for multiple join queries. The second part of this dissertation proposes a novel scheme for the distributed execution of generic multi-way joins with window constraints. The proposed scheme partitions the states into disjoint slices in the time domain, and then distributes the fine-grained states in the cluster, forming a virtual computation ring. New challenges to support this distributed state-slicing processing are answered by numerous new techniques. The extensive experimental evaluations show that the proposed strategies achieve significant performance improvements in terms of response time and memory usages for a wide range of configurations and workloads on a real system. Ring based distributed stream query processing and multi-query sharing both are based on the state-slice concept. The third part of this dissertation combines the first two parts of this dissertation work and proposes a novel distributed multi-query optimization technique

When things matter: A survey on data-centric Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed