Internet Of Things Data Processing Research Articles

Machine learning based low-rate DDoS attack detection for SDN enabled IoT networks

The software-defined network (SDN) enabled internet of things (IoT) architecture is deployed in many industrial systems. The ability of SDN to intelligently route traffic and use underutilised network resources, enables IoT networks to cope with data onslaught smoothly. SDN also eliminates bottlenecks and helps to process IoT data efficiently without placing a larger strain on the network. The SDN-based IoT network is vulnerable to DDoS attack in a sophisticated usage environment. The SDN-based IoT network behaviours are different from traditional networks, which makes the detection of low-traffic DDoS attacks more difficult. In this paper, we propose a learning-based detection approach that deploys learning algorithms and utilizes stateful and stateless features from Openflow packages to identify attack traffics in SDN control and data planes. Our prototype approach and experiment results show that our system identified the low-rate DDoS attack traffic accurately with relatively low system performance overheads.

Secure Data Sharing and Search for Cloud-Edge-Collaborative Storage

Cloud-edge-collaborative storage (CECS) is a promising framework to process data of the internet of things (IoT). It allows edge servers to process IoT data in real-time and stores them on a cloud server. Hence, it can rapidly respond to the requests of IoT devices, provide a massive volume of cloud storage for IoT data, and conveniently share IoT data with users. However, due to the vulnerability of edge and cloud servers, CECS suffers from the risk of data leakage. Existing secure CECS schemes are secure only if all edge servers are trusted. In other words, if any edge server is compromised, all cloud data (generated by IoT devices) will be leaked. Additionally, it is costly to request expected data from the cloud, which is linear with respect to the number of edge servers. To address the above problems, we propose a new secure data search and sharing scheme for CECS. Our scheme improves the existing secure CECS scheme in the following two ways. First, it enables users to generate a public-and-private key pair and manage private keys by themselves. In contrast, the existing solution requires edge servers to manage users' private keys. Second, it uses searchable public-key encryption to achieve more secure, efficient, and flexible data searching. In terms of security, our scheme ensures the confidentiality of cloud data and secure data sharing and searching and avoids a single point of breakthrough. In terms of performance, the experimental results show that our scheme significantly reduces users' computing costs by delegating most of the cryptographic operations to edge servers. Especially, our scheme reduces the computing and communication overhead for generating a search trapdoor compared with the existing secure CECS scheme.

Toward Big Data Processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System

Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

An effective approach of latency-aware fog smart gateways deployment for IoT services

Fog computing has become a new cutting-edge technology to bring cloud applications closer to physical IoT devices at the edge of the network. In Fog computing use case scenarios latency is the main challenge that must be addressed since mission-critical environments are mostly delay sensitive. To achieve this goal, the fog nodes placement is needed to minimize the delays in the service access layer. There is neither a common fog computing architecture nor a generally accepted concept of fog nodes, how and where to deploy fog nodes or how it supports the real-time Internet of Things (IoT) service execution. Edge devices such as the switch, router, gateway, mobile phones, smart car etc., are the candidates for deployment of fog nodes but the deployment differs according to the application. In this work, we have taken gateways as candidates for fog node deployment. The gateway collects data from smart sensors, but it does not have any pre-processing or decision-making capabilities. Therefore, the gateway is made smarter with Fog capabilities and named as Fog Smart Gateway (FSG). The processing of IoT traffic is taken care of by Virtual Machines (VMs) facilitated by distributed Fog nodes. We optimized the number of fog nodes for deployment to reduce the total latency induced by traffic aggregation and processing. Our results show that the optimal deployment of fog nodes in the IoT network could yield a reduction in latency compared to processing IoT data in a conventional cloud system.

From Cloud Down to Things: An Overview of Machine Learning in Internet of Things

With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to meet the requirement of all IoT applications. The limited computation and communication capacity of the cloud necessitate the edge computing, i.e., starting the IoT data processing at the edge and transforming the connected devices to intelligent devices. Machine learning (ML) the key means for information inference, should extend to the cloud-to-things continuum too. This paper reviews the role of ML in IoT from the cloud down to embedded devices. Different usages of ML for application data processing and management tasks are studied. The state-of-the-art usages of ML in IoT are categorized according to their application domain, input data type, exploited ML techniques, and where they belong in the cloud-to-things continuum. The challenges and research trends toward efficient ML on the IoT edge are discussed. Moreover, the publications on the “ML in IoT” are retrieved and analyzed systematically using ML classification techniques. Then, the growing topics and application domains are identified.

Secure Phrase Search for Intelligent Processing of Encrypted Data in Cloud-Based IoT

Phrase search allows retrieval of documents containing an exact phrase, which plays an important role in many machine learning applications for cloud-based Internet of Things (IoT), such as intelligent medical data analytics. In order to protect sensitive information from being leaked by service providers, documents (e.g., clinic records) are usually encrypted by data owners before being outsourced to the cloud. This, however, makes the search operation an extremely challenging task. Existing searchable encryption schemes for multikeyword search operations fail to perform phrase search, as they are unable to determine the location relationship of multiple keywords in a queried phrase over encrypted data on the cloud server side. In this paper, we propose P3 , an efficient privacy-preserving phrase search scheme for intelligent encrypted data processing in cloud-based IoT. Our scheme exploits the homomorphic encryption and bilinear map to determine the location relationship of multiple queried keywords over encrypted data. It also utilizes a probabilistic trapdoor generation algorithm to protect users’ search patterns. Thorough security analysis demonstrates the security guarantees achieved by P3 . We implement a prototype and conduct extensive experiments on real-world datasets. The evaluation results show that compared with existing multikeyword search schemes, P3 can greatly improve the search accuracy with moderate overheads.

A Remote Monitoring and Diagnosis Method Based on Four-Layer IoT Frame Perception

In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.

Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

Internet of Things: information security challenges and solutions

Keeping up with the burgeoning Internet of Things (IoT) requires staying up to date on the latest network attack trends in dynamic and complicated cyberspace, and take them into account while developing holistic information security (IS) approaches for the IoT. Due to multiple vulnerabilities in the IoT foundations, many targeted attacks are continuing to evolve. This survey of related work in the very specialized field of IS assurance for the IoT develops a taxonomy of typical attacks against IoT assets (with special attention to IoT device protection). Based on this taxonomy, the key directions for countering these attacks are defined. According to the modern demand for the IoT and big IS-related data processing, we propose applying the Security Intelligence approach. The results obtained, when compared with the related work and numerous analogues, are based on the following research methodology: view the IoT as a security object to be protected, leading to understanding its vulnerabilities and possible attacks against the IoT exploiting these vulnerabilities, and from there approaches to protecting the IoT. A few areas of the future research, among which the IoT operational resilience and usage of the blockchain technology seem to us the most interesting, are indicated.

Event driven and semantic based approach for data processing on IoT gateway devices

Internet of things (IoT) applications rely on networks composed of a set of heterogeneous sensors and smart devices, which have the capability to constantly monitor the surroundings and gather data. This heterogeneity is reflected in raw data collected by such type of systems. Additionally, these data are continuously streaming; thus leading to huge volumes of heterogeneous data, which are further transferred to centralized platforms for processing. Consequently, two main challenges have arisen. First, the heterogeneity aspect of IoT data makes high-level IoT applications’ task of interpreting such data and detecting events in the real world more complex. Second, sending sensory data to a centralized platform leads to some issues, such as extensive consumption of IoT devices’ limited resources, network traffic overloading, and latency, which might negatively impact the response time especially in systems that were designed to handle critical situations. In this paper, we propose a decentralized approach for IoT data processing, by delegating this task to distributed edge devices (Gateways) taking into consideration their limited resources and network bandwidth. To accomplish this, we proposed a two-layer data processing approach that employs a hyped model encompassed of complex event processing (CEP) and semantic web (SW) techniques. While the first is proposed for performing aggregation and classification tasks, we use the latter for performing semantic filtering and annotation tasks. We have evaluated the feasibility of our approach to process sensory data in the context of Air Quality Monitoring scenario using an experimentation involving established ontologies. Several benchmarks are considered such as overall runtime, data size, and response time.

Fog Computing and the Internet of Things: A Review

With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

LeanBench: comparing software stacks for batch and query processing of IoT data

Companies produce data in large volumes and in multiple varieties. This trend is intensifying with the deployment of Internet of Things (IoT) devices. Companies need to process data more efficiently and at the edge of the network if they are to remain capable of making timely business decisions based on data. Apache Hadoop and Hive are two widely used data processing systems. However, they rely on complex software stacks that cannot run in a typical IoT gateway device, i.e., a computer with low hardware specifications. An approach to solve this problem is to replace the software with a leaner system with the same functionality. This approach is the value proposition of Unicage, a data processing system based on Unix shell scripting, that promises better performance by using the operating system directly for execution and inter-process communication.In this paper, we benchmark data processing systems with workloads that compare Unicage with Hadoop and with Hive. The results show that the complexity of the software stack is indeed a significant bottleneck in data processing.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Research on key technologies of data processing in internet of things

The data of Internet of things (IOT) has the characteristics of polymorphism, heterogeneous, large amount and processing real-time. The traditional structured and static batch processing method has not met the requirements of data processing of IOT. This paper studied a middleware that can integrate heterogeneous data of IOT, and integrated different data formats into a unified format. Designed a data processing model of IOT based on the Storm flow calculation architecture, integrated the existing Internet security technology to build the Internet security system of IOT data processing, which provided reference for the efficient transmission and processing of IOT data.

A collaborative resource management for big IoT data processing in Cloud

These days, handling large amounts of data generated from Internet of Things (IoT) applications in the Cloud has turned into a powerful solution for fulfilling Quality of Service requests from clients. However, to save on costs, the union of cloud providers, known as a cloud confederation, can be a promising methodology because this organization helps cloud suppliers to overcome the restrictions of physical assets in handling Big IoT Data. Nonetheless, the key challenge is to discover appropriate cloud collaborators to form a confederation that will achieve the required level of services characterized in service level agreements. In this paper, to execute heterogeneous Big IoT Data handling demands from clients, we build a cloud confederation model that determines ideal choices for target cloud providers. In addition, we present a multi-objective (MO) optimization model of collaborator selection among different clouds. To solve the MO optimization model, a general structure for a multi-objective genetic algorithm is also developed. The proposed model is tested through various test assessments.

Adaptive Clustering for Dynamic IoT Data Streams

The emergence of the Internet of Things (IoT) has led to the production of huge volumes of real-world streaming data. We need effective techniques to process IoT data streams and to gain insights and actionable information from real-world observations and measurements. Most existing approaches are application or domain dependent. We propose a method which determines how many different clusters can be found in a stream based on the data distribution. After selecting the number of clusters, we use an online clustering mechanism to cluster the incoming data from the streams. Our approach remains adaptive to drifts by adjusting itself as the data changes. We benchmark our approach against state-of-the-art stream clustering algorithms on data streams with data drift. We show how our method can be applied in a use case scenario involving near real-time traffic data. Our results allow to cluster, label, and interpret IoT data streams dynamically according to the data distribution. This enables to adaptively process large volumes of dynamic data online based on the current situation. We show how our method adapts itself to the changes. We demonstrate how the number of clusters in a real-world data stream can be determined by analyzing the data distributions.

Skyline Preference Query Based on Massive and Incomplete Dataset

Personalized recommendation and the processing of real-time data exemplify the processing of massive data which in the field of Internet-of-Things (IoT) received a great extent of attention in recent literature. The incompleteness of massive data in the IoT is widespread. Obtaining personalized information from the incomplete data set is still puzzled by searching efficient and accurate methods at present. Skyline query is a widely used data processing method, especially in the field of multi-objective decision analysis and data visualization. To eliminate the negative effects on massive data processing in IoT, a novel skyline preference query strategy based on massive and the incomplete data set is proposed in this paper. This strategy simply separates and divides massive and incomplete data set into two parts according to dimension importance and executes skyline query, respectively. The strategy mainly resolves the problem of extracting personalized information from massive and incomplete data set and improves the efficiency of skyline query on massive and incomplete data set. First, this paper presents a skyline preference query strategy based on strict clustering and implements it on dimensions that have higher importance. Second, a skyline preference query strategy based on loose clustering is implemented on dimensions that have lower importance. Finally, integrating local skyline query results, this paper calculates global skyline query results by using information entropy theory. The efficiency and effectiveness of Skyline Preference Query (SPQ) algorithm have been evaluated in terms of response time and result set size through the comparative experiments with ISkyline algorithm and sort-based incomplete data skyline algorithm. A large number of simulation results show that the efficiency of SPQ algorithm is higher than that of other common methods.

Large-Scale Real-Time Semantic Processing Framework for Internet of Things

Nowadays, the advanced sensor technology with cloud computing and big data is generating large-scale heterogeneous and real-time IOT (Internet of Things) data. To make full use of the data, development and deploy of ubiquitous IOT-based applications in various aspects of our daily life are quite urgent. However, the characteristics of IOT sensor data, including heterogeneity, variety, volume, and real time, bring many challenges to effectively process the sensor data. The Semantic Web technologies are viewed as a key for the development of IOT. While most of the existing efforts are mainly focused on the modeling, annotation, and representation of IOT data, there has been little work focusing on the background processing of large-scale streaming IOT data. In the paper, we present a large-scale real-time semantic processing framework and implement an elastic distributed streaming engine for IOT applications. The proposed engine efficiently captures and models different scenarios for all kinds of IOT applications based on popular distributed computing platform SPARK. Based on the engine, a typical use case on home environment monitoring is given to illustrate the efficiency of our engine. The results show that our system can scale for large number of sensor streams with different types of IOT applications.

Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

Managing Computing Infrastructure for IoT Data

Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, deriving values and insights from big data using rich analytics becomes important for achieving competitiveness, success and leadership in every field. The Internet of Things (IoT) is causing the number and types of products to emit data at an unprecedented rate. Heterogeneity, scale, timeliness, complexity, and privacy problems with large data impede progress at all phases of the pipeline that can create value from data issues. With the push of such massive data, we are entering a new era of computing driven by novel and ground breaking research innovation on elastic parallelism, partitioning and scalability. Designing a scalable system for analysing, processing and mining huge real world datasets has become one of the challenging problems facing both systems researchers and data management researchers. In this paper, we will give an overview of computing infrastructure for IoT data processing, focusing on architectural and major challenges of massive data. We will briefly discuss about emerging computing infrastructure and technologies that are promising for improving massive data management.