Compressed sensing signal and data acquisition in wireless sensor networks and internet of things

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinearreconstruction algorithm and random sampling on a sparsebasis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment

Video-based evidence analysis and extraction in digital forensic investigation

As a result of the popularity of smart mobile devices and the low cost of surveillance systems, visual data are increasingly being used in digital forensic investigation. Digital videos have been widely used as key evidence sources in evidence identification, analysis, presentation, and report. The main goal of this paper is to develop advanced forensic video analysis techniques to assist the forensic investigation. We first propose a forensic video analysis framework that employs an efficient video/image enhancing algorithm for the low quality of footage analysis. An adaptive video enhancement algorithm based on contrast limited adaptive histogram equalization (CLAHE) is introduced to improve the closed-circuit television (CCTV) footage quality for the use of digital forensic investigation. To assist the video-based forensic analysis, a deep-learning-based object detection and tracking algorithm are proposed that can detect and identify potential suspects and tools from footages

Blockchain enabled industrial Internet of Things technology

The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past a few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper we address the integration of blockchain and IIoT from the industrial prospective. A blockchain enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain enabled IIoT

Blockchain based digital forensics investigation framework in the internet of things and social systems

The decentralised nature of blockchain technologies can well match the needs of integrity and provenances of evidences collecting in digital forensics across jurisdictional borders. In this work, a novel blockchain based digital forensics investigation framework in the Internet of Things (IoT) and social systems environment is proposed, which can provide proof of existence and privacy preservation for evidence items examination. To implement such features, we present a block enabled forensics framework for IoT, namely IoT forensic chain (IoTFC), which can offer forensic investigation with good authenticity, immutability, traceability, resilience, and distributed trust between evidential entitles as well as examiners. The IoTFC can deliver a gurantee of traceability and track provenance of evidence items. Details of evidence identification, preservation, analysis, and presentation will be recorded in chains of block. The IoTFC can increase trust of both evidence items and examiners by providing transparency of the audit train. The use case demonstrated the effectiveness of proposed method

Computational intelligence-enabled cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies

Human-centric artificial intelligence enabled digital images and videos forensic triage

Digital forensics and incident response (DFIR) involve huge volume of data collected from digital systems that requires investigators to quickly sift through and prioritise relevant evidence. The forensics investigator team faces many challenges when analysing the processes to keep them on target and improve them. A lack of a systematic approach to data analysis can lead to slower decision-making. This work aims to enhance the effectiveness and efficiency in forensics analysis using human-centric artificial intelligence (HAI) enabled data triage. Specifically, an image and video triage was proposed that can significantly speed up the investigation and identify highly related evidence items from huge volume of images and videos

Image Source Identification Using Convolutional Neural Networks in IoT Environment

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy

Live power generation predictions via AI-driven resilient systems in smart microgrids

The 5G technology can significantly benefit smart consumer devices powered by microgrids in several ways, enhancing their efficiency, reliability, and overall performance, which play a pivotal role in advancing consumer electronics by providing a more reliable, efficient, and sustainable source of power for these devices. The growing environmental awareness and emergence of new technologies have made smart microgrids a good renewable and resilient power to serve consumer electronics. This work developed a secure AI-driven predictable and resilient power generation system for efficient microgrid energy use and management. Specifically, we first developed an intelligent power generation forecasting model based on a joint distribution of power generation and weather data; then, a resilient eXtreme Gradient Boosting (XGBoost) power generation forecast model was proposed that allows incorporating the weather intermittency in the joint distribution. The scheme has been validated using real-time power generation data together with weather data. The experimental results show that the proposed scheme can provide a more accurate and robust prediction of the microgrid against weather intermittency

Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks

Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users' next check-in location or predicting their future check-in location at a given time with coarse granularity. A unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still remains challenging. Inspired by the recent success of word embedding in natural language processing, in this paper, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user's next check-in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time-efficient due to being parallelizable

MIAEC: Missing data imputation based on the evidence Chain

© 2013 IEEE. Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis. Therefore, it is necessary to implement the imputation of missing value in the phase of data preprocessing to reduce the possibility of data missing as a result of human error and operations. The performances of existing imputation approaches of missing value cannot satisfy the analysis requirements due to its low accuracy and poor stability, especially the rapid decreasing imputation accuracy with the increasing rate of missing data. In this paper, we propose a novel missing value imputation algorithm based on the evidence chain (MIAEC), which first mines all relevant evidence of missing values in each data tuple and then combines this relevant evidence to build the evidence chain for further estimation of missing values. To extend MIAEC for large-scale data processing, we apply the map-reduce programming model to realize the distribution and parallelization of MIAEC. Experimental results show that the proposed approach can provide higher imputation accuracy compared with the missing data imputation algorithm based on naive Bayes, the mode imputation algorithm, and the proposed missing data imputation algorithm based on K-nearest neighbor. MIAEC has higher imputation accuracy and its imputation accuracy is also assured with the increasing rate of missing value or the position change of missing value. MIAEC is also proved to be suitable for the distributed computing platform and can achieve an ideal speedup ratio