Smart Data: A New Perspective of Tackling the Big Data Phenomena Leveraging a Fog Computing System

The management of Big Data is a very important issue in emerging IoT technologies. Conventional methods are not sufficient to deal with the ever-increasing amount of raw data originating from the sensors. In this paper we approach this problem from the data structure perspective. We design and develop a concept that we call “Smart Data”. Smart Data is an active and intelligent data structure using a fog computing system that facilitates the management of Big Data in IoT based applications. Such a data cell is initially very simple and lightweight, but it evolves (grows) when traveling through the hierarchical fog computing system towards the cloud, merging with other cells or vice-versa, if the data moves from the cloud towards the actuators. Using Smart Data, we aim to facilitate the preprocessing of data to reduce the load from cloud computing and improve the quality of service and energy efficiency in IoT applications. Our main targets for pre-processing of Big Data using Smart Data and fog computing platform include data filtering, aggregation, compression, and encryption. Moreover, our design goal is to reduce volume, velocity and increase value and veracity of Big Data considering other parameters such as energy efficiency, throughput, scalability and quality of service. </p

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.</p

An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart data concept to analyze the intrusion alerts. In the edge layer, we deploy our detectors in edge devices. Smart data is a very promising approach for enabling lightweight and efficient intrusion detection, providing a path for detection of silent attacks such as botnet attacks in IoT-based systems. </p

A Review on Fog Computing Systems

The current decade has witnessed a wide deployment of Internet of Things (IoT) technology in various application domains, and its pervasive role will continue to strengthen in the future. For dealing with a vast number of connected devices and the big data generated by them, an efficient computing platform is required. Fog computing has been proposed as a solution. It is a paradigm extending cloud computing and services to the edge of the network, thus reducing the latency of dynamic decision making and improving real-time performance in general. This paper provides a view on the current state-of-the-art research in the area of fog computing and internet of things (IoT) technology. </p

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%

Research and Practical Issues of Enterprise Information Systems: 11th IFIP WG 8.9 Working Conference, CONFENIS 2017, Shanghai, China, October 18-20, 2017, Revised Selected Papers

Fog networks have been introduced as a new intermediate computational layer between the cloud layer and the consumer layer in a typical cloud computing model. The fog layer takes advantage of distributed computing through tiny smart devices and access points. To enhance the performance of the fog layer we propose utilization of unused computational resources of surrounding smart devices in the fog layer. However, this will raise security concerns. To tackle this problem, we propose in this paper a novel method using a trust model and Role Based Access Control System to manage dynamically joining mobile fog nodes in a fog computing system. In our approach, the new dynamic nodes are assigned non-critical computing tasks. Their trust level is then evaluated based on the satisfaction rate of assigned tasks which is obtained through different computing parameters. As the result of this evaluation, untrusted nodes are dropped by the fog system and nodes with a higher trust level are given a new role and privileges to access and process categorized data.</p

An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart data concept to analyze the intrusion alerts. In the edge layer, we deploy our detectors in edge devices. Smart data is a very promising approach for enabling lightweight and efficient intrusion detection, providing a path for detection of silent attacks such as botnet attacks in IoT-based systems.peerReviewe