Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net

Mobile Networks and Applications - Several recent research has centered on maximizing Internet of Things (IoT) devices’ lifetime by deploying data reduction techniques on IoT nodes to reduce...     

Coverage and Connectivity-Based 3D Wireless Sensor Deployment Optimization

The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% target coverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.

     

Energy-Efficient Resource Allocation in Underlay D2D Communication using ABC Algorithm

Wireless Personal Communications - Due to the limited resources of Internet of Things (IoT) nodes, routing protocols for these networks should be designed in such a way that not only reduce the...     

Future IoT‐enabled threats and vulnerabilities: State of the art,challenges, and future prospects

In the recent era, the security issues affecting the future Internet‐of‐Things (IoT) standards has fascinated noteworthy consideration from numerous research communities. In this view, numerous assessments in the form of surveys were proposed highlighting several future IoT‐centric subjects together with threat modeling, intrusion detection systems (IDS), and various emergent technologies. In contrast, in this article, we have focused exclusively on the emerging IoT‐related vulnerabilities. This article is a multi‐fold survey that emphasizes on understanding the crucial causes of novel vulnerabilities in IoT paradigms and issues in existing research. Initially, we have emphasized on different layers of IoT architecture and highlight various emerging security challenges associated with each layer along with the key issues of different IoT systems. Secondly, we discuss the exploitation, detection, and defense methodologies of IoT malware‐enabled distributed denial of service (DDoS), Sybil, and collusion attack capabilities. We have also discussed numerous state‐of‐the‐art strategies for intrusion detection and methods for IDS setup in future IoT systems. Third, we have presented a brief classification of existing IoT authentication protocols and a comparative analysis of such protocols based on different IoT‐enabled cyber attacks. For conducting a real‐time future IoT research, we have presented some emerging blockchain solutions. We have also discussed a comparative examination of some of the recently developed simulation tools and IoT test beds that are characterized based on different layers of IoT infrastructure. We have also outlined some of the open issues and future research directions and also facilitate the readers with broad classification of existing surveys in this domain that addresses several scopes related to the IoT paradigm. This survey article focuses in enabling IoT‐related research activities by comparing and merging scattered surveys in this domain.     

A Survey of IoT Key Enabling and Future Technologies: 5G,Mobile IoT,Sematic Web and Applications

The Internet of Things (IoT) is the communications paradigm that can provide the potential of ultimate communication. The IoT paradigm describes communication not only human to human (H2H) but also machine to machine (M2M) without the need of human interference. In this paper, we examine, review and present the current IoT technologies starting from the physical layer to the application and data layer. Additionally, we focus on future IoT key enabling technologies like the new fifth generation (5G) networks and Semantic Web. Finally, we present main IoT application domains like smart cities, transportation, logistics, and healthcare.     

An Efficient Hybrid Computing Environment to Develop a Confidential and Authenticated IoT Service Model

Wireless Personal Communications - The applications and scope of the Internet of Things (IoT) goes on increasing when cloud computing combines with IoT. Cloud enriches the capacity of IoT in...     

Modeling and verifying based on timed automata of Internet of things gateway security system

The Internet of things (IoT) is a multiple heterogeneous network,and its perception layer is often faced with various security threats.As the bridge between the perception layer and the network layer,the IoT gateway should have the security management function to prevent the security issue from spreading to the upper layer.According to the current security deficiencies in IoT gateway,a universal IoT gateway security system was proposed based on the IoT gateway middleware technology.Various security protocols or algorithms can be embedded in IoT gateway security system,and the modeling and analysis can help the design and implementation of IoT gateway.The formal modeling and verification of the IoT gateway security system was performed by timed automata.The results show that the IoT gateway security system satisfies the security properties of confidentiality,availability,authenticity,robustness,integrity and freshness.     

Deep learning for the internet of things: Potential benefits and use-cases

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic amounts of data for facilitating a wide range of applications. Deep Learning (DL) would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT. In this regard, exploring the potential of DL for IoT data analytics becomes highly crucial. This paper begins with a concise discussion on the Deep Neural Network (DNN) and its different architectures. The potential benefits that DL will bring to the IoT are also discussed. Then, a detailed review of DL-driven IoT use-cases is presented. Moreover, this paper formulates a DL-based model for Human Activity Recognition (HAR). It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques. Apart from enlightening the potential of DL in IoT applications, this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.     

基于扩展传染病模型和马尔可夫链的物联网可用度评估方法

为反映恶意程序传播环境下物联网可用的状态,基于扩展的SEIRD传染病模型和马尔可夫链提出一种物联网可用度评估方法。根据物联网节点的实际状态,扩展经典传染病模型SIR建立SEIRD物联网节点状态转换模型。由物联网节点各个状态之间的动态变化过程,构建物联网节点处于5种状态的概率动力学方程,得到反映各状态转换的马尔可夫矩阵,进一步得到物联网节点的可用度计算方法。以典型的星形和簇形物联网拓扑结构为例,给出整个物联网可用度的评估方法。通过实验,为管理员如何合理部署正常工作节点数、路由数提供建议。研究成果对提高物联网可用度、促进物联网成功应用具有理论指导意义。     

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

Computer-empowered detection of possible faults for Heating, Ventilation and Air-Conditioning (HVAC) subsystems, e.g., chillers, is one of the most important applications in Artificial Intelligence (AI) integrated Internet of Things (IoT). The cyber-physical system greatly enhances the safety and security of the working facilities, reducing time, saving energy and protecting humans’ health. Under the current trends of smart building design and energy management optimization, Automated Fault Detection and Diagnosis (AFDD) of chillers integrated with IoT is highly demanded. Recent studies show that standard machine learning techniques, such as Principal Component Analysis (PCA), Support Vector Machine (SVM) and tree-structure-based algorithms, are useful in capturing various chiller faults with high accuracy rates. With the fast development of deep learning technology, Convolutional Neural Networks (CNNs) have been widely and successfully applied to various fields. However, for chiller AFDD, few existing works are adopting CNN and its extensions in the feature extraction and classification processes. In this study, we propose to perform chiller FDD using a CNN-based approach. The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods. First, the CNN-based approach does not require the feature selection/extraction process. Since CNN is reputable with its feature extraction capability, the feature extraction and classification processes are merged, leading to a more neat AFDD framework compared to traditional approaches. Second, the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.     

Integration of IoT and DRAGON-lab in cloud environment

Distributed research & academic gigabits open network lab (DRAGON-lab) is the only test-bed for research purpose related to next generation internct (NGI) which based on the confederation network using...     

Home IoT resistance: Extended privacy and vulnerability perspective

Home Internet of Things (IoT) services are expected to augment the efficiency and comfort of users’ daily lives; however, this expectation is eclipsed by concerns regarding privacy and vulnerability. While these concerns critically impact the acceptance of IoT services for the home, they have been discussed primarily from a traditional point of view; academic discussions of privacy and vulnerability in the current environment are lacking. This study extends existing privacy and vulnerability theories to demonstrate the importance of physical privacy and user vulnerability protections in home IoT environments. To validate the proposed research model, an empirical analysis was conducted on 265 samples with a partial least squares structural equation modeling technique. The differences in vulnerability factors, along with privacy concerns and resistance to home IoT services, were also compared by gender, experience, and type of housing. Results show that user vulnerability has the strongest impact on home IoT privacy concerns and resistance to home IoT environments. Additionally, this study found that personal factors appear differently across vulnerabilities, privacy concerns, and home IoT resistance. This study extends the traditional concepts of privacy and vulnerability to the home IoT environment.     

A Self-Optimizing QoS-Based Access for IoT Environments

Nowadays, providing Internet of Things (IoT) environments with service level guarantee is a challenging task. Moreover, IoT services should be autonomous in order to minimize human intervention and thus to reduce the operational management cost of the corresponding big scale infrastructure. We describe in this paper a service level-based IoT architecture enabling the establishment of an IoT Service Level Agreement (iSLA) between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). The proposed iSLA specifies the requirements of an IoT service, used in a specific application domain (e-health, smart cities, etc.), in terms of different measurable Quality of Service (QoS) parameters. In order to achieve this agreement, several QoS mechanisms are to be implemented within each layer of the IoT architecture. In this context, we propose an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism to provide different IoT services with QoS guarantee. Our proposal called QBAIoT (QoS-based Access for IoT) creates different Contention Access Periods (CAP) according to different traffic types of the IoT environment. These CAPs are QoS-based and enable traffic differentiation. Thus, a QoS CAP is configured with several slots during which only IoT objects belonging to the same QoS class can send their data. Furthermore, we specify a self-management closed control loop in order to provide our IoT architecture with a self-optimizing capability concerning QoS CAPs slots allocation. This capability takes into account the actual usage of QoS CAPs as well as the characteristics of the corresponding traffic class.

     

HIoTPOT: Surveillance on IoT Devices against Recent Threats

Honeypot Internet of Things (IoT) (HIoTPOT) keep a secret eye on IoT devices and analyzes the various recent threats which are dangerous to IoT devices. In this paper, implementation of a research honeypot is presented which is used to learn the recent tactics and ethics used by black hat community to attack on IoT devices. As IoT is open and easy for accessing, all the intruders are highly attracted towards IoT. Recently Telnet based attacks are very famous on IoT devices to get easy access and attack on other devices. To reduce these kinds of threats, it is necessary to know in details about intruder, therefore the aim of this research work is to implement novel based secret eye server known as HIoTPOT which will make the IoT environment more safe and secure.     

Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.

     

Internet of Things: Applications and Challenges in Technology and Standardization

The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it—leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers, potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.     

Analysis of identifiers in IoT platforms

The Internet of Things (IoT) provides new opportunities for different IoT platforms connecting various devices together. The need to identify those devices is the foremost important to perform any kind of operation. Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms. Some existing industrial identification mechanisms can also be used in the IoT. There is no common Identification Scheme (IS) for the IoT as yet, because of the political and commercial differences amongst the standard bodies. The unavailability of a unified IS method makes the inter-working among IoT platforms challenging. This paper analyses and compares ISs used by several selected IoT platforms. This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.     

A survey on communication protocols and performance evaluations for Internet of Things

The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, IoT interacts with many application areas such as smart city, smart building, security, traffic, remote monitoring, health, energy, disaster, agriculture, industry. The IoT network in these scenarios comprises tiny devices, gateways, and cloud platforms. An IoT network is able to keep these fundamental components in transmission under many conditions with lightweight communication protocols taking into account the limited hardware features (memory, processor, energy, etc.) of tiny devices. These lightweight communication protocols affect the network traffic, reliability, bandwidth, and energy consumption of the IoT application. Therefore, determining the most proper communication protocol for application developers emerges as an important engineering problem. This paper presents a straightforward overview of the lightweight communication protocols, technological advancements in application layer for the IoT ecosystem. The survey then analyzes various recent lightweight communication protocols and reviews their strengths and limitations. In addition, the paper explains the experimental comparison of Constrained Applications Protocol (CoAP), Message Queuing Telemetry (MQTT), and WebSocket protocols, more convenient for tiny IoT devices. Finally, we discuss future research directions of communication protocols for IoT.     

Reliable and secure data transfer in IoT networks

With the rapid technological improvements in mobile devices and their inclusion in Internet of Things (IoT), secure key management becomes mandatory to ensure security of information exchange. For instance, IoT applications, such as smart health-care and smart homes, provide automated services to the users with less or no user intervention. As these application use user-sensitive data, ensuring their security and privacy should be paramount, especially during the key management process. However, traditional approaches for key management will not suit well in IoT environment because of the inherent resource constraint property of IoT devices. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The proposed scheme efficiently provides security to IoT devices by delegating most of the resource consuming cryptographic processing to a local entity. This entity coordinates with other peer entities to provide a distributed key as well as an authentication mechanism to network devices. In particular, the proposed scheme exploits the advantages of mobile agents by deploying them in different subnetworks as and when required: (1) to process the cryptography work for the IoT devices, and (2) to act as an local authenticated entity to perform fast authentication process. To verify the effectiveness and correctness of our proposed scheme, we have simulated it in a large IoT scenario and evaluated against relevant metrics that includes user mobility, certification generation time, and communication overhead.

     

Security of the Internet of Things: perspectives and challenges

Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This paper focuses on the security problems among all other challenges. As IoT is built on the basis of the Internet, security problems of the Internet will also show up in IoT. And as IoT contains three layers: perception layer, transportation layer and application layer, this paper will analyze the security problems of each layer separately and try to find new problems and solutions. This paper also analyzes the cross-layer heterogeneous integration issues and security issues in detail and discusses the security issues of IoT as a whole and tries to find solutions to them. In the end, this paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT.