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.

     

Energy aware cloud-edge service placement approaches in the Internet of Things communications

In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud-edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud-edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud-edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud-edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side-by-side taxonomy is proposed to categorize the relevant studies on cloud-edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented.     

RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system.” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.     

A dynamic fog service provisioning approach for IoT applications

Internet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions.     

方兴未艾的物联网

传感器网与互联网的结合产生物联网,是未来互联网的重要组成部分。物联网支持包括"感知中国"在内的各种应用,向服务网演化。有着广阔的发展前景。物联网本身是一个开放的结构,它使用开放协议的体系结构,支持各种基于互联网的应用,目前我国传感器网/物联网尚处于发展初期,运营商已经开始提供M2M业务支持物联网,运营商在物联网产业链中定位有待发展中形成。发展物联网一方面要高屋建瓴积极促进,另一方面要保持平和的心态,稳健地发展,应用驱动是成功的关键。     

A secure multifactor remote user authentication scheme for Internet of Multimedia Things environment

To attain ubiquitous connectivity of everything, Internet of Things (IoT) systems must include “multimedia things.” Internet of Multimedia Things (IoMT) is a heterogeneous network of smart multimedia things connected together and with other physical devices to the Internet so as to achieve globally available multimedia services and applications. Due to the ever increasing amount of multimedia data in IoT environments, securing these systems becomes crucial. This is because these systems are easily susceptible to attacks when information or any service is accessed by the users. In this paper, we propose a secure three‐factor remote user authentication scheme for IoMT systems using ECC. The formal security proof performed using ROR model and BAN logic confirms that an attacker will not be able to extract sensitive user information. Through informal security analysis, we justify the resistance of the scheme against several security attacks. The performance comparison shows that the scheme is efficient in terms of computational cost, security features, and attack resistance. Furthermore, simulation of the scheme using AVISPA and Proverif proves that the scheme is secure against all active and passive attacks.     

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.     

Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end. In this connection, a key issue is to determine when and where services should be deployed. Common service deployment strategies used to be static based on the rules defined at the design time. However, dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation, where the static service deployment solutions are not efficient. In this paper, we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data. To effectively predict upcoming workloads, we combine the online machine learning methods with an online optimization algorithm for service deployment. A simulation-based evaluation demonstrates that, compared with those state-of-the art approaches, the approach proposed in this paper has a lower latency of stream processing.     

Structural and behavioral reference model for IoT‐based elderly health‐care systems in smart home

Increasing level of personal and social health and life expectancy has resulted in the growth of aged people population. Elderly care is an essential and costly issue in any society that should be addressed in researches. Elderly care has faced some problems such as elderly solitary, trained caregivers insufficiency, and increasing cost of late diagnosis of diseases and accident. Regarding to these problems, smart home technology can be used as an efficient solution. It provides an expertise, long‐term, and low‐cost care that empowers elderly to have an independent life. In this paper, we propose an architecture for a health‐care system in a smart home. In this architecture, rapid and timely diagnosis of environmental incidents and health risks causes reduction in costs of health care and relief. Given to the vital aspect of health‐care systems, the proposed architecture components and solutions are presented to meet quality attributes such as availability, performance, security, and interoperability. The proposed architecture evaluation is based on the Architecture Tradeoff Analysis Method (ATAM) scenario‐based approach. ATAM is a method for analysis and evaluation of software systems architecture. And quality attributes scenarios are examined to meet quality requirement. Compared with the previous works, more quality attributes are addressed in this paper.     

Smart integrated IoT healthcare system for cancer care

The emergence of the internet of things (IoT) has drastically influenced and shaped the world of technology in the contexts of connectivity, interconnectivity, and interoperability with smart connected sensors, objects, devices, data, and applications. In fact, IoT has brought notable impacts on the global economy and human experience that span from industry to industry in a variety of application domains, including healthcare. With IoT, it is expected to facilitate a seamless interaction and communication of objects (devices) with humans in the environment. Therefore, it is imperative to embrace the potentials and benefits of IoT technology in healthcare delivery to ensure saving lives and to improve the quality of life using smart connected devices. In this paper, we focus on the IoT based healthcare system for cancer care services and business analytics/cloud services and also propose the adoption and implementation of IoT/WSN technology to augment the existing treatment options to deliver healthcare solution. Here, the business analytics/cloud services constitute the enablers for actionable insights, decision making, data transmission and reporting for enhancing cancer treatments. Furthermore, we propose a variety of frameworks and architectures to illustrate and support the functional IoT-based solution that is being considered or utilized in our proposed smart healthcare solution for cancer care services. Finally, it will be important to understand and discuss some security issues and operational challenges that have characterized the IoT-enabled healthcare system.

     

Evolution from ancient medication to human-centered Healthcare 4.0: A review on health care recommender systems

The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future.     

Internet of Things offloading: Ongoing issues,opportunities, and future challenges

Internet of Things (IoT) has very remarkable advantages over customary communication technologies. However, IoT suffers from different issues, such as limited battery life, low storage capacity, and little computing capacity. For this reason, in many IoT applications and devices, we require an alternative unit to execute the tasks from the user's device and return results. In general, the problem of limited resources by transferring the computation workload to other devices/systems with better resources is addressed by offloading computation. It can be focused on improving the application, extending battery life, or expanding storage capacity. The offloading operation can be performed based on various quality of service (QoS) parameters that contain computational demands for load balancing, response time, application, energy consumption, latency, and other things. Moreover, the systematic literature review (SLR) method is used to identify, assess, and integrate findings from all relevant studies that address one or more research questions on IoT offloading and conduct a comprehensive study of empirical research on offloading techniques. However, we present a new taxonomy for them based on offloading decision mechanisms and overall architectures. Furthermore, we offer a parametric comparison for the offloading methods. As well, we present the future direction and research opportunities in IoT offloading computation. This survey will assist academics and practitioners to directly understand the progress in IoT offloading.     

A lightweight convergent personal mobile service delivery approach based on phone book

Mobile phone users are demanding an efficient convergent communication approach for efficient communication and information sharing with their real life social circles. Unfortunately, the existing telecom services and the popular Internet services are not organically integrated to provide convenient convergent services. To overcome this issue, one of the promising convergent communication services considered by telecom carriers is RCS (Rich Communication Suite). However, existing issues, such as insufficient interoperability among different operators, shortage of terminals and heavy dependence on large‐scale IP multimedia subsystem (IMS) network deployment, mean that it will take time to implement RCS across worldwide networks and for it to become a service equally universal as voice and short message. Instead of a heavyweight RCS solution, in this paper, we propose a lightweight, quick deployment mobile phone book application system based on cloud computing without a fully deployed IP multimedia subsystem infrastructure, which seamlessly integrates traditional telecommunication services, instant messaging services, social networking services and automatic contact information management. Based on this, ‘Telco‐OTT’ service provision approach, telecom carriers can allow their subscribers to enjoy conveniently the rich convergent personal service experience quickly without a large initial investment. Thus, it is a low‐risk opportunity for telecom carriers to meet subscriber demand for advanced user‐centric convergent service features. Copyright © 2013 John Wiley & Sons, Ltd.     

A systematic literature review of machine learning applications in IoT

The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.     

A metaheuristic‐based method for replica selection in the Internet of Things

With the advent of the Internet of Things (IoT), the count of gadgets connected to the Internet has been increased. IoT, as a modern paradigm, has been used to describe the future in which physical things like RFID tags, sensors, actuators, and cellphones can intermingle for achieving shared purposes. Also, we can employ cloud computing for storing the things' information in the IoT. However, this information has been replicated through the network for increasing availability. In this paper, due to the NP‐hard nature of the replica selection problem, an improved version of ant colony optimization (IACO) has been applied. The impact of pheromone on the chosen path is converted by ants to invert the underlying logic of ACO. Due to the existence of different IoT centers, the IACO has been employed for selecting the replicated data in the IoT where the load balancing among IoT centers has been considered. In this method, an ant chooses the ideal point for its movement; then others may not pass the track that the preceding ants have been passed. The obtained outcomes have shown that the method has outperformed the ACO, HQFR, and RTRM approaches regarding the waiting time and load balancing.     

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.     

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.     

A secure and efficient remote patient‐monitoring authentication protocol for cloud‐IoT

The ongoing Cloud‐IoT (Internet of Things)–based technological advancements have revolutionized the ways in which remote patients could be monitored and provided with health care facilities. The real‐time monitoring of patient's health leads to dispensing the right medical treatment at the right time. The health professionals need to access patients' sensitive data for such monitoring, and if treated with negligence, it could also be used for malevolent objectives by the adversary. Hence, the Cloud‐IoT–based technology gains could only be conferred to the patients and health professionals, if the latter authenticate one another properly. Many authentication protocols are proposed for remote patient health care monitoring, but with limitations. Lately, Sharma and Kalra (DOI: 10.1007/s40998‐018‐0146‐5) present a remote patient‐monitoring authentication scheme based on body sensors. However, we discover that the scheme still bears many drawbacks including stolen smart card attack, session key compromise, and user impersonation attacks. In view of those limitations, we have designed an efficient authentication protocol for remote patient health monitoring that counters all the above‐mentioned drawbacks. Moreover, we prove the security features of our protocol using BAN logic‐based formal security analysis and validate the results in ProVerif automated security tool.     

Security in Internet of Things: issues,challenges, taxonomy,and architecture

Internet technology is very pervasive today. The number of devices connected to the Internet, those with a digital identity, is increasing day by day. With the developments in the technology, Internet of Things (IoT) become important part of human life. However, it is not well defined and secure. Now, various security issues are considered as major problem for a full-fledged IoT environment. There exists a lot of security challenges with the proposed architectures and the technologies which make the backbone of the Internet of Things. Some efficient and promising security mechanisms have been developed to secure the IoT environment, however, there is a lot to do. The challenges are ever increasing and the solutions have to be ever improving. Therefore, aim of this paper is to discuss the history, background, statistics of IoT and security based analysis of IoT architecture. In addition, we will provide taxonomy of security challenges in IoT environment and taxonomy of various defense mechanisms. We conclude our paper discussing various research challenges that still exist in the literature, which provides better understanding of the problem, current solution space, and future research directions to defend IoT against different attacks.