Internet of Nano-Things, Things and Everything: Future Growth Trends

Miraz, Mahdi H.; Ali, Maaruf; Excell, Peter S.; Picking, Richard

doi:10.3390/fi10080068

Cited by 135 publications

(87 citation statements)

References 41 publications

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“…However, it may not always be possible for IoT system to possess all of these features. IoT is broadly classified into Industrial Internet of Things (IIoT) and Consumer Internet of Things (CIoT) in [18]. The IoT assisted applications for smart cities include smart grids, smart lighting systems, smart transportation, smart health-care, smart homes, augmented reality, and smart agriculture [1], [21]- [23].…”

mentioning

confidence: 99%

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan

Yaqoob

Tran

et al. 2020

IEEE Internet Things J.

301

131

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Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

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mentioning

confidence: 99%

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan

Yaqoob

Tran

et al. 2020

IEEE Internet Things J.

301

131

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“…In addition, the proliferation of big data and AI technologies brings new bricks for IoE's construction. In recent years, more relevant literature has replenished IoE's essence, i.e., gathering big data hiding from the Internet, in virtue of various AI algorithms, and enabling all devices/machines with the automated abilities [1], [2], [6], [25]. Thus, IoE has the potential to extract and analyse real-time data from millions of connected devices and then to make intelligent proactive decisions, thereby enabling "automated intelligence".…”

Section: A the Birth Of Ioementioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

179

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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“…Zhenyu et al [32] and in [33,34] addressed large-scale internet of things and its application in health care through fog computing. Having considered the rapid volumetric increase in the IoT technology, unless the underlying flaws of the supportive technologies are fixed, the cyber security is going to stay as a major cause of concern [35,36].…”

Section: Related Workmentioning

confidence: 99%

Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting

Chaudhry¹,

Qidwai

Miraz

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incorporate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use cryptographic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.The ISO/IEEE 11073 and HL7 have successfully played a key role in enabling the medical devices to exchange data amongst themselves. Due to privacy and security issues, this data needs to be protected. Since medical devices are not typically computationally very proficient and the standards advocate lightweight communications, a cryptographic data security scheme is not an ideal solution to go ahead with [4]. In order to address this problem, we propose to use the statistical fingerprinting as data obfuscation method.The Impulsive Statistical Fingerprinting (ISF) was used by Crotti et al. to classify the network traffic for anomaly detection [5]. When network traffic shows an atypical pattern and skewness from the "normal" traffic pattern, this is raised as an anomaly to the system [6]. We use the ISF to prevent leaf sensors from broadcasting the actual readings. Since the ISO/IEEE 11073 allows the flexibility to transmit metadata in the HL7 packet, we calculate the statistical model proposed in this paper to calculate the obfuscated values from sensors before translating the communication in the HL7 format.The statistical model proposed in this paper assumes that the devices are already connected and configured in a network. An initial handshaking between PHDs and IP network provide the baseline values for mean calculation that will help in recalculating back to original values of the sensor data. From the results reported in [7] we learn that the PHDs send periodic metric report containing time stamp, and status value which reports device errors and alarms. The time stamps received from the devices helps keep both the TCP session [8] and time series data consistent. Using the statistical method proposed in [9], which assumes that the time series data is stationary, we consult to the history of the PHD values. We use the algorithm proposed in [9] and apply autoregressive integrated moving average models to find the best fit of the time series model to past values of the time series. An adversary, who is using a Man-in-The-Middle (MiTM) attack, needs to know considerable amount of time series data to predict the...

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Internet of Nano-Things, Things and Everything: Future Growth Trends

Cited by 135 publications

References 41 publications

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting

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