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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.

Purpose A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain. Design/methodology/approach To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used. Findings Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data. Originality/value The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

IoT is expected to have far-reaching consequences on society due to a wide spectrum of applications like smart healthcare, smart transportation, smart agriculture, smart home, etc. However, ethical considerations of AI-enabled smart devices have not been duly considered from a design perspective. In this paper, the authors propose a novel fuzzy logic-based method to incorporate ethics within smart things of IoT. Ethical considerations relevant to a machine context are represented in terms of fuzzy ethics variables (FEVs) and ethics rules. For each ethics rule, a value called scaled ethics value (SEV) is used to indicate its ethical desirability. In order to model flexibility in ethical response, the authors employ the concept of ethics modes that selectively allow scenarios depending on the value of SEV. The method offers a viable mechanism for smart devices to imbue ethical sensitivity that can pave the way for a technology society amenable to human ethics. However, the method does not account for varying ethics, as such incorporating learning mechanisms represent a promising research direction.

Recent years have seen enormous growth in the number of devices connected to the Internet. With such rapid development in the Internet of Things (IoT) domain, data generated by these devices is eve...

Recent years have witnessed significant growth in the area of Internet of things (IoT) and is further expected to increase manifolds in the foreseeable future. Such a massive usage of IoT comes with spectrum scarcity issues. In the last few years, Cognitive Radio Network (CRN) has been widely researched because of its potential to be an effective solution to the problem of spectrum scarcity. The unlicensed users in CRN need to rendezvous on a commonly available channel for the purpose of necessary information exchange. Channel hopping (CH) is one of the representative technique to achieve rendezvous in CRNs in which users hop among available channels according to a pre-defined sequence. Providing guaranteed rendezvous which is fair and having full rendezvous diversity in minimum possible time is a challenging issue in CRN. In this paper, we propose an asymmetric asynchronous CH algorithm that provides guaranteed, fair and full rendezvous diversity among the users. The idea is to construct sender and receiver matrices to generate CH sequences based on the number of available channels of sender and receiver. Analytical and simulation results verify that the proposed algorithm outperform some existing state of the art rendezvous algorithms in terms of maximum time to rendezvous (MTTR), provide full rendezvous diversity and rendezvous fairness among the users.

Background: Cloud computing is a widely adopted computing paradigm and its importance has increased multi-folds in the recent past due to the inception of Internet of Things (IoT). Objectives: Efficient load balancing techniques are required to optimize the use of the cloud resources although load balancing in cloud is known to be a NP-hard problem. Methods: This work focuses on multiple load balancing algorithms whose performance has been analysed and compared under varying load conditions. Results: Comparative analysis of 5 algorithms is given, among which max-min algorithm is found to be the best performing algorithm with approximately 28% better job finish time and 23% higher throughput than the worst performing algorithm (FCFS). Conclusion: Simulations have been performed in CloudSim under varying input loads and the performance has been analysed under multiple scenarios. All the simulations have pointed towards the superiority of Max-min algorithm over other algorithms. This work will prompt the researchers to further investigate into load balancing algorithms so that better results are achieved.

Today, 73 years after the independence and twenty years after the turn of the century, “Health for All” which should have been accomplished by now, remains a far-fetched and an elusive dream. Instead, the people of India are bequeathed a triple burden of disease: sustaining the weight of transmittable infections, expanding burden of nontransferable illnesses, and a healthcare system not efficient enough to handle them both. At present, India is home to one-third of the poor population around the world. After a high population growth rate, unregulated and inefficient healthcare is the major cause for this abjection and poverty. The global position of India vis-à-vis the health indicators like Infant Mortality Rate (IMR), Crude Birth Rate (CBR), Crude Death Rate (CDR) and life expectancy is shocking, shameful and on a downward trend. The objective of this paper was to identify the major issues in the Indian healthcare system and offer Internet of Things (IoT) based solutions. The underdevelopment of health and health services in India is brought about by the same determinants that cause underdevelopment in the first place. This paper discusses these causes and major issues responsible for the dilapidated state of Indian healthcare and offers IoT based solutions for dealing with each of these issues. Moreover, a collaborative edge/cloud IoT based framework has been proposed for remedying the Indian healthcare. : The presented solutions could be used for making healthcare and thereby, health, a reality for all.

Purpose The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. Design/methodology/approach The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time. Findings Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper. Practical implications The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper. Social implications Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis. Originality/value To the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

As the IoT is moving out of its early stages, it is emerging as an area of future internet. The evolving communication paradigm among cloud servers, Fog nodes and IoT devices are establishing a multilevel communication infrastructure. Fog provides a platform for IoT along with other services like networking, storage and computing. With the tremendous expansion of IoT, security threats also arise. These security hazards cannot be addressed by mere dependence on cloud model. In this paper we present an overview of security landscape of Fog computing, challenges, and, existing solutions. We outline major authentication issues in IoT, map their existing solutions and further tabulate Fog and IoT security loopholes. Furthermore this paper presents Blockchain, a decentralized distributed technology as one of the solutions for authentication issues in IoT. We tried to discuss the strength of Blockchain technology, work done in this field, its adoption in COVID-19 fight and tabulate various challenges in Blockchain technology. At last we present the Cell Tree architecture as another solution to address some of the security issues in IoT, outlined its advantages over Blockchain technology and tabulated some future course to stir some attempts in this area.

The Internet of Things (IoT) is a promising technology for addressing the challenges of urbanisation, however ethical ramifications of introducing such pervasive technology have not been duly considered. It is assumed that smart devices are exempt from moral, religious or legal responsibilities with regard to their surroundings. Such a perspective on device functioning may lead to situations where essential human values are put at risk. We suggest that social parameters be also included within the realm of machine functioning. In this work, we propose a neuro fuzzy system (NFS) that is able to implement ethics relevant to the context of a device and also fine tune them. Initially ethical requirements are specified in terms of fuzzy ethics rules and appropriate ethical response is learned subsequently. The method enables us to address the vital question of ethical compliance of smart things so that ethical rights of people are not infringed upon. By such incorporation, we ensure that machines respect core human values paving way for human friendly technology ecosystems like ethical smart city.

Motivated by the recent explosion of interest around the Internet of Things (IoT), this paper is focused on the dynamics of its security. It is a fact that at present, there is a dearth of consolidated and methodical visions for assuring the security of IoT. This owes to its unique characteristics like interdependence of IoT devices and services, device heterogeneity, mobility, affinity to owners, pervasiveness, and unattended nature. The constraints of energy, memory, bandwidth and computability further complicate this picture. The strategies available in the literature today do not entirely consider this intricate nature of IoT environments. Moreover, uncertainty is not adequately acknowledged by the researchers while designing the security procedures for IoT, even though most of the IoT related security problems stem from an uncertain environment. This paper focuses on addressing this uncertainty, along with the other major security issues of IoT. It gives detailed categorization of the attacks in IoT. To this end, a Fuzzy Logic and Fog based Secure Architecture for IoT (FLFSIoT) has been proposed in this paper that works in real-time. In FLFSIoT, fuzzy logic has been used to alleviate the uncertainty of belonging to one crisp cluster of an edge node and for detecting various classical attacks. The Fog supported IoT architecture has been used to make FLFSIoT intrinsically more secure in comparison to the cloud-supported IoT by omitting the latency and other issues. As such, this work feeds the benefits of fuzzy log and fog computing into the carving of a generic solution to the IoT’s security problem. The efficiency of FLFSIoT has been evaluated against the Distributed Denial of Service (DDoS) and Collusion attacks, and it has been observed that it gives more accurate results than the existing benchmarks.

Because of the massive development of the Internet of Things (IoT), the quantity of remote devices associated with the Internet is estimated to touch 26 billion by 2020. This will challenge both the energy proficiency of wireless battery fuelled gadgets and the bandwidth capacities of wireless networks. One answer for both difficulties could be to use packet header compression. The primary reason for having header compression is that there is a huge amount of redundancy in the header fields of the packets owned by the same packet stream. Hence, there is a need to reduce this redundancy to make the best use of the available bandwidth so as to make the processing of data faster. This can be done by bringing the compressor and decompressor in sync with each other. To reduce the header size considerably, sending static/fixed information only initially followed by making predictions and the use of dependencies for rest of the fields fructifies. This paper reviews various existing header compression schemes, identifies the need for Robust Header Compression (ROHC) scheme and introduces the header compression of 6LoWPAN. 6LoWPAN provides header compression mechanisms to reduce the size of upper layer headers. 6LoWPAN header compression mechanisms can be used to compress the security headers as well.

With confusion and uncertainty ruling the world, 2020 created near-perfect conditions for cybercriminals. As businesses virtually eliminated in-person experiences, the COVID-19 pandemic changed the way we live and caused a mass migration to digital platforms. However, this shift also made people more vulnerable to cyber-crime. Victims are being targeted by attackers for their credentials or financial rewards, or both. This is because the Internet itself is inherently difficult to secure, and the attackers can code in a way that exploits its flaws. Once the attackers gain root access to the devices, they have complete control and can do whatever they want. Consequently, taking advantage of highly unprecedented circumstances created by the Covid-19 event, cybercriminals launched massive phishing, malware, identity theft, and ransomware attacks. Therefore, if we wish to save people from these frauds in times when millions have already been tipped into poverty and the rest are trying hard to sustain, it is imperative to curb these attacks and attackers. This paper analyses the impact of Covid-19 on various cyber-security related aspects and sketches out the timeline of Covid-19 themed cyber-attacks launched globally to identify the modus operandi of the attackers and the impact of attacks. It also offers a thoroughly researched set of mitigation strategies which can be employed to prevent the attacks in the first place. Moreover, this manuscript proposes a fuzzy logic and data mining-based intelligence system for detecting Covid-19 themed malicious URL/phishing attacks. The performance of the system has been evaluated against various malicious/phishing URLs, and it was observed that the proposed system is a viable solution to this problem.