Authentication-based Access Control and Data Exchanging Mechanism of IoT Devices in Fog Computing Environment

The emergence of fog computing has witnessed a big role in initiating secure communication amongst users. Fog computing poses the ability to perform analysis, processing, and storage for a set of Internet of Things (IoT) devices. Several IoT solutions are devised by utilizing the fog nodes to alleviate IoT devices from complex computation and heavy processing. This paper proposes an authentication scheme using fog nodes to manage IoT devices by providing security without considering a trusted third party. The proposed authentication scheme employed the benefits of fog node deployment. The authentication scheme using fog node offers reliable verification between the data owners and the requester without depending on the third party users. The proposed authentication scheme using fog nodes effectively solved the problems of a single point of failure in the storage system and offers many benefits by increasing the throughput and reducing the cost. The proposed scheme considers several entities, like end-users, IoT devices, fog nodes, and smart contracts, which help to administrate the authentication using access policies. The proposed authentication scheme using fog node provided superior results than other methods with minimal memory value of 4009.083 KB, minimal time of 76.915 s, and maximal Packet delivery ratio (PDR) of 76.

     

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.

     

Smart Prediction and Monitoring of Waste Disposal System Using IoT and Cloud for IoT Based Smart Cities

One of the prominent applications of Internet of Things (IoT) in this digital era is the development of smart cities. In IoT based smart cities, the smart objects (devices) are connected with each other via internet as a backbone. The sensed data by the smart objects are transmitted to the sink for further processing using multi hop communication. The smart cities use the analyzed data to improve their infrastructure, public utilities and they enhance their services by using the IoT technology for the betterment of livelihood of the common people. For IoT based smart cities, waste collection is a prominent issue for municipalities that aim to achieve a clean environment. With a boom in population in urban areas, an increasing amount of waste is generated. A major issue of waste management system is the poor process used in waste collection and segregation. Public bins begin to overflow for a long period before the process of cleaning starts, which is resulting in an accumulation of bacteria causing bad odors and spreading of diseases. In order to overcome this issue, in this paper an IoT based smart predication and monitoring of waste disposal system is proposed which utilizes off-the-shelf components that can be mounted to a bin of any size and measure fill levels. An Arduino microcontroller is employed in the proposed model to interface the infrared (IR), ultraviolet (UV), weight sensors, and a Global Positioning System (GPS) module is used to monitor the status of bins at predetermined intervals. The proposed system transmits the data using the cluster network to the master module which is connected to the backend via Wi-Fi. As data is collected, an intelligent neural network algorithm namely Long Short-Term Memory (LSTM) is used which will intelligently learn and predict the upcoming wastage from waste generation patterns. Moreover, the proposed system uses Firebase Cloud Messaging to notify the appropriate people when the bins were full and needed to be emptied. The Firebase Cloud Messaging (FCM) JavaScript Application Programming Interface (API) is used to send notification messages in web apps in browsers that provide service work support. Hence, the proposed system is useful to the society by providing facilities to the governments for enforcing stricter regulations for waste disposal. Additional features such as automated calibration of bin height, a dynamic web data dashboard as well as collation of data into a distributed real-time firebase database are also provided in the proposed system.

     

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

     

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.

     

Survey of Testing Methods and Testbed Development Concerning Internet of Things

The concept of Internet of Things (IoT) was designed to change everyday lives of people via multiple forms of computing and easy deployment of applications. In recent years, the increasing complexity of IoT-ready devices and processes has led to potential risks related to system reliability. Therefore, the comprehensive testing of IoT technology has attracted the attention of many researchers, which promotes the extensive development of IoT testing methods and infrastructure. However, the current research on IoT testing methods and testbeds mainly focuses on specific application scenarios, lacking systematic review and analysis of many applications from different points of view. This paper systematically summarizes the latest testing methods covering different IoT fields and discusses the development status of the existing Internet of things testbed. Findings of this review demonstrate that IoT testing is moving toward larger scale and intelligent testing, and that in near future, IoT test architecture is set to become more standardized and universally applicable with multi-technology convergence—i.e., a combination of big data, cloud computing, and artificial intelligence—being the prime focus of IoT testing.

     

Big Data Knowledge Discovery as a Service: Recent Trends and Challenges

In remote area industrial systems, energy consumption monitoring is a crucial challenge. As the conventional monitoring methods lack an intelligent approach, the finest energy consumption monitoring is not possible. Hence, Internet of Things (IoT) based monitoring methods have been developed by recent industrial systems. Therefore, in this research, a novel cloud with IoT based energy monitoring technique is developed. The energy parameters of the Computer Numerical Control based milling machine has been gathered using IoT based Current Transducers , Voltage Transducers , and power sensors. The IoT device includes Zigbee or Bluetooth for managing communication between the machine and the monitoring system. Then the obtained data is stored in the cloud storage platform for large scale machine energy data in the windows platform. Later on, the obtained data from cloud storage is processed by the novel Normalized Recursive Least Kalman Filter for event detection processing. Moreover, the feature extraction has been done using the proposed Simplified Principal Component Analysis method. Furthermore, the energy utilization of the machine is monitored over various situations using the proposed novel Dynamic Self-evolving Reasoning based Fuzzy Neural algorithm. The Median Absolute Deviation is estimated for the conditional inference of the system. The software implementation of this work is done in MATLAB. The power consumption of the machine is validated under various cases. Besides, the proposed simulation outcomes are compared with various existing energy monitoring systems for verifying the significance of the developed method.

     

Blockchain-Enabled Deep Reinforcement Learning Approach for Performance Optimization on the Internet of Things

Internet of Things (IoT) networks are rapidly expanding, which requires adequate and reliable infrastructure and a large amount of data. The IoT device security and technical confidentiality may benefit from using Blockchain, a decentralised and trustworthy ledger. Increasing transaction throughput and coping with big data transfer situations is critical when dealing with significant volumes of IoT data on the Blockchain. Consequently, this research investigates the Deep Reinforcement Learning (DRL) crucial functioning of the blockchain-enabled IoT structure, wherever transactions are instantaneously expanded and public divisibility is confirmed. It is important to note that DRL and Blockchain are two separate advancements devoted to the reliability and usefulness of system operation. These are both transactional systems. Technology integration into information exchange and research solutions is becoming increasingly critical. As a result of Blockchain, information may be exchanged securely and decentralised. When used in tandem with DRL, it can significantly improve communication efficiency. By combining DRL and Blockchain throughout the IoT, the author first presents a decentralised and efficient communication structure that allows for scalable and trustworthy information allocation and better performance than earlier options. The DRL approach assesses whether to offload and which service to dump to improve performance up to 87.5%. Furthermore, this method focuses on constructing an effective offloading mechanism for Blockchain-based communication systems to boost performance.

     

基于雾计算的物联网架构研究

物联网是一种能将物体连接至互联网使其更加智能的技术.但是物联网设备产生的大数据难以处理,网络架构的可扩展性差,以及用户的安全隐私容易泄露等问题都限制了物联网的发展.为了解决这些问题,通过分析雾计算所具有的优势提出基于雾计算的物联网架构.基于该架构,同时考虑到用户的安全隐私问题,又提出分层的网络架构.最后对文章进行总结和展望.     

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.

     

流计算大数据技术在运营商实时信令处理中的应用

基于Hadoop搭建的大数据平台采用离线批处理的方式,无法满足对数据实效性敏感的业务要求。针对运营商动态数据信息开放大数据平台的实时信令处理要求,对流式计算大数据组件进行了分析,介绍了与流计算大数据相关的实时采集、汇聚和处理组件,形成了端到端实时信令处理大数据技术解决方案,并提出了融合批处理和实时计算的大数据平台解决方案,提高了网络信令数据的时效性,为业务创新提供更大空间,带来更多利益。     

Real Time Scheduling in TSCH for IoT Using Neurogenetic Algorithm

Internet of Things (IoT) is a widely adoptable technology in industrial, smart home, smart grid, smart city and smart healthcare applications. The real world objects are remotely connected through internet and it provides services with the help of friendly devices. Currently IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard is gaining a part of consideration among the IoT research community because of its effectiveness to improvise the reliability of communication which is orchestrated by the scheduling. As TSCH is an emerging Medium Access Control (MAC) protocol, it is used in the proposed work to enhance the network scheduling by throughput maximization and delay minimization. The paper focuses on proper utilization of the channel through node scheduling. NeuroGenetic Algorithm (NGA) has been proposed for TSCH scheduling and its performance is evaluated with respect to time delay and throughput. The system is implemented in real time IoT devices and results are perceived and analyzed. The proposed algorithm is compared with existing TSCH scheduling algorithms.

     

Wormhole Attack Detection System for IoT Network: A Hybrid Approach

Many errors in data communication cause security attacks in Internet of Things (IoT). Routing errors at network layer are prominent errors in IoT which degrade the quality of data communication. Many attacks like sinkhole attack, blackhole attack, selective forwarding attack and wormhole attack enter the network through the network layer of the IoT. This paper has an emphasis on the detection of a wormhole attack because it is one of the most uncompromising attacks at the network layer of IoT protocol stack. The wormhole attack is the most disruptive attack out of all the other attacks mentioned above. The wormhole attack inserts information on incorrect routes in the network; it also alters the network information by causing a failure of location-dependent protocols thus defeating the purpose of routing algorithms. This paper covers the design and implementation of an innovative intrusion detection system for the IoT that detects a wormhole attack and the attacker nodes. The presence of a wormhole attack is identified using location information of any node and its neighbor with the help of Received Signal Strength Indicator (RSSI) values and the hop-count. The proposed system is energy efficient hence it is beneficial for a resource-constrained environment of IoT. It also provides precise true-positive (TPR) and false-positive detection rate (FPR).

     

Survey on data security and privacy-preserving for the research of edge computing

With the rapid development and extensive application of the Internet of things (IoT),big data and 5G network architecture,the massive data generated by the edge equipment of the network and the real-time service requirements are far beyond the capacity if the traditional cloud computing.To solve such dilemma,the edge computing which deploys the cloud services in the edge network has envisioned to be the dominant cloud service paradigm in the era of IoT.Meanwhile,the unique features of edge computing,such as content perception,real-time computing,parallel processing and etc.,has also introduced new security problems especially the data security and privacy issues.Firstly,the background and challenges of data security and privacy-preserving in edge computing were described,and then the research architecture of data security and privacy-preserving was presented.Secondly,the key technologies of data security,access control,identity authentication and privacy-preserving were summarized.Thirdly,the recent research advancements on the data security and privacy issues that may be applied to edge computing were described in detail.Finally,some potential research points of edge computing data security and privacy-preserving were given,and the direction of future research work was pointed out.     

Exploring New Vista of Secured and Optimized Data Slicing for Big Data: An IOT Paradigm

Security and privacy are useful concerns in the context of big data. The Internet of Things (IoT) serves both to bolster and to ease security worries. IoT gadgets raise immense new security challenges, particularly with regards to things like basic framework. Be that as it may, they additionally offer approaches to help keep clients progressively secure by adding additional obstructions of safeguard to information and people. In order to sustain the integrity of data and to provide in order to implicit security for any big database, data slicing is constructive. Data slicing implicitly provides the preservation and the query performance to the database users. The sliced data are stored at servers in a distributed system to protect the data from the attackers. In this article, an intelligent and efficient model is developed to partition the polynomial data securely and to store at various servers in a distributed system. An auto-key generator spawns an encryption key to encrypt the polynomial data as a higher level security. Encrypted data is partitioned by an efficient Fast Fourier transform Technique. A novel clustering methodology entitled as Binary Reverse Clustering is introduced to optimize the performance as well as to reduce the servers’ requisition. Moreover, the novel clustering technique is compared with the traditional clustering algorithm.

     

Mobile Edge Computing Platform with Container-Based Virtualization Technology for IoT Applications

The next generation of fifth generation (5G) network, implementing mobile edge computing (MEC), network function virtualization (NFV) and software defined networking technologies, establishes a flexible and resilient network in line with various internet of things (IoT) devices. While NFV adds flexibility scale in or out networks by allowing network functions to be dynamically deployed and inter-connected, MEC provide intelligence at the edge of a mobile network; reduces latency, and increases capacity. With the diverse development of networking applications, the proposed MEC with container-based virtualization technology (CVT) as IoT gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively enhance the quality of service. In this work, the proposed IoT gateway will be analyzed to elucidate the combined effect of simultaneously deploying virtual network functions and MEC applications on the same network infrastructure. Low latency, high bandwidth and high agility, supporting the connection of large-scale devices, and the efficient combination of resources from network edge and cluster clouds, account for real-time network conditions, reducing the IoT applications and services to indicate that a number is the average of 30% of the latency, that could get more suitable service quality to develop such as both augmented reality and virtual reality application intelligence in coming 5G network.

     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

I-CARES: advancing health diagnosis and medication through IoT

Internet of Things (IoT) is changing the way many sectors operate and special attention is paid to promoting healthy living by employing IoT based technologies. In this paper, a novel approach is developed with IoT prototype of Wireless Sensor Network and Cloud based system to provide continuous monitoring of a patient’s health status, ensuring timely scheduled and unscheduled medicinal dosage based on real-time patient vitals measurement, life-saving emergency prediction and communication. The designed integrated prototype consists of a wearable expandable health monitoring system, Smart Medicine Dispensing System, Cloud-based Big Data analytical diagnostic and Artificial Intelligence (AI) based reporting tool. A working prototype was developed and tested on few persons to ensure that it is working according to expected standards. Based on the initial experiments, the system fulfilled intended objectives including continuous health monitoring, scheduled timely medication, unscheduled emergency medication, life-saving emergency reporting, life-saving emergency prediction and early stage diagnosis. In addition, based on the analysis reports, physicians can diagnose/decide, view medication side effects, medication errors and prescribe medication accordingly. The proposed system exhibited the ability to achieve objectives it was designed using IoT to alleviate the pressure on hospitals due to crowdedness in hospital care and to reduce the healthcare service delays.

     

Cyclic Delay Diversity with Phase Shift in Low Rate SC-FDMA Uplink

This paper proposes a phase shift scheme in cyclic delay diversity (CDD) for single-carrier frequency division multiple access in Internet of Things (IoT) applications. The proposed scheme is assumed to be applied to the uplink of long term evolution (LTE) systems. Since the transmission rates of IoT applications are small, each uplink connection may occupy less than 12 subcarriers that corresponds to one resource block of the LTE. Since the length of the data sequence in time domain is short so that CDD may provide limited frequency diversity. The proposed scheme shifts the phases of the data symbols in time domain and spreads each subcarrier component over multiple subcarriers. Thus, more diversity gain can be realized with CDD. Numerical results obtained through computer simulation shows that the proposed scheme improves the performance by about 5–7 dB at the bit error rate of 10^?4 for the data sequence length of 4. It is also shown that no increase of peak-to-average power ratio is observed with the proposed scheme.

     

Discovering Non-Cooperating Nodes by Means of Learning Automata in the Internet of Things

Internet of Things (IoT) refers to a set of things that are wirelessly connected. The lack of cooperation of nodes, which is due to the reduction of energy level, leads to non-cooperating nodes. Discovering non-cooperating nodes is regarded as one of the main challenges of IoT. In this paper, we addressed this issue by using learning automata where misbehavior of non-cooperating nodes is identified and removed from the network. Simulation results of the proposed method were compared with those of previous works and methods; it was found that the proposed method optimized the other methods in terms of power consumption, throughput, the precision of discovering non-cooperating nodes, and false-positive rate.