Flexible Congestion Management for Error Reduction in Wireless Sensor Networks

The dissertation is concerned with the efficient resolution of data congestion on wireless sensor networks (WSNs). WSNs are of increasing relevance due to their applications in automation, industrial processes, natural-disaster detection, weather prediction, and climate monitoring. In large WSNs where measurements are periodically made at each node in the network and sent in a multi-hop fashion via the network tree to a single base-station node, the volume of data at a node may exceed the transmission capabilities of the node. This type of congestion can negatively impact data accuracy when packets are lost in transmission. We propose flexible congestion management for sensor networks (FCM) as a data-collection scheme to reduce network traffic and minimize the error resulting from data-volume reduction. FCM alleviates all congestion by lossy data fusion, encourages opportunistic fusion with an application-specific distortion tolerance, and balances network traffic. We consider several data-fusion methods including the k-means algorithm and two forms of adaptive summarization. Additional fusion is allowed when like data may be fused with low error up to some limit set by the user of the data-collection application on the network. Increasing the error limit tends to reduce the overall traffic on the network at the cost of data accuracy. When a node fuses more data than is required to alleviate congestion, its siblings are notified that they may increase the sizes of their transmissions accordingly. FCM is further improved to re-balance the network traffic of subtrees such that subtrees whose measurements have lower variance may decrease their output rates while subtrees whose measurements have higher variance may increase their output rates, while still addressing all congestion in the network. We verify the effectiveness of FCM with extensive simulations

Intelligent Embedded Vision for Summarization of Multi-View Videos in IIoT

Nowadays, video sensors are used on a large scale for various applications including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging to process such heterogeneous nature of Big Data in real time. Multi-view video summarization (MVS) enables us to suppress redundant data in distributed video sensors settings. The existing MVS approaches process video data in offline manner by transmitting it to the local or cloud server for analysis, which requires extra streaming to conduct summarization, huge bandwidth, and are not applicable for integration with industrial internet of things (IIoT). This paper presents a light-weight CNN and IIoT based computationally intelligent (CI) MVS framework. Our method uses an IIoT network containing smart devices, Raspberry Pi (clients and master) with embedded cameras to capture multi-view video (MVV) data. Each client Raspberry Pi (RPi) detects target in frames via light-weight CNN model, analyzes these targets for traffic and crowd density, and searches for suspicious objects to generate alert in the IIoT network. The frames of each client RPi are encoded and transmitted with approximately 17.02% smaller size of each frame to master RPi for final MVS. Empirical analysis shows that our proposed framework can be used in industrial environments for various applications such as security and smart transportation and can be proved beneficial for saving resources

Enhancing Big Data Security with Collaborative Intrusion Detection

As an asset of Cloud computing, big data is now changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. Its security and privacy, however, have always been a concern of the owners of the data. The security and privacy could be strengthened via securing Cloud computing environments. This requires a comprehensive security solution from attack prevention to attack detection. Intrusion Detection Systems (IDSs) are playing an increasingly important role within the realm of a set of network security schemes. In this article, we study the vulnerabilities in Cloud computing and propose a collaborative IDS framework to enhance the security and privacy of big data

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves