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The Internet of Things (IoT) has been a rapidly developing field since its inception in 1982. In order to assess the past and future trends in IoT research, a search was conducted on Google Scholar which yielded 25 papers. These papers were then analyzed and discussed, leading to the conclusion that IoT is not only here to stay, but also continuously evolving. This can be seen in the advancements in technology, applications, areas of use, benefits, problems, and challenges. It is expected that this pattern of development will continue in the future, with the emergence of new technologies, applications, software, and areas of use. However, as with any other field, there will be barriers and challenges that need to be overcome at each stage of progress. Furthermore, there is immense potential to integrate IoT with Artificial Intelligence (AI), leading to even more innovative and efficient applications. Overall, the future of IoT research looks promising, with continued growth and advancements in various aspects of the field. [ABSTRACT FROM AUTHOR]

With increase in the demand of better data collection from various IoT devices, researchers are showing more interests in providing the enhanced data collection methods to the users. Many data collection methods have proposed, but still more efficiency and better results are required. In this paper, a novel and secure data collection method from IoT devices has proposed. In this paper, SEED (Secure and energy efficient data-collection) method has discussed. It includes creation of aggregator nodes and path discovery algorithms. Integrity of data is analysed by using MD5 hashing technique. This hashing technique is one of the most usable techniques as compared to other. In the regard of implementation of this novel secure and energy data collection method, MD5 is used to just hash the processing data in the entire process. In earlier scenarios, there were various issues regarding fault tolerance, congestion, energy wastage, path discovery and load balancing in a network. To resolve these issues, updating of the aggregator node is done after each failure or transmission. Transmission of huge amount of data can create different challenges in network. This research is completely different from other existing researches of data collection with routing as it deals with sink node by using a unique path discovery algorithm. This proposed mechanism provides the better results for all of the nodes and network in terms of energy efficiency and throughput. [ABSTRACT FROM AUTHOR]

Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time. [ABSTRACT FROM AUTHOR]

• Proposed IoT based Dynamic Food Supply Chain Network for Smart Cities. • Proposed IoT based Food Supply with Dynamic Vehicle Routing (IFSCDVR) using Bee Colony algorithm. • Proposed DPSTT for efficient tracing and tracking of food product. • Real-time analysis of food supply chain management in smart cities. The rapid growth of population in metropolitan areas has put incremental pressure on urban cities. The centric strategy towards smart cities are expected to cover solution for metropolitan life and ecological environment. One of the significant application areas of IoT in smart cities is the food industry. IoT systems help to monitor, analyze, and manage the real-time food industry in smart cities. In this research, we proposed an IoT based Dynamic Food Supply Chain for Smart Cities which not only ensures the food quality but also provides intelligent vehicle routing as well as tracing sources of contamination in FCM. Furthermore, a smart sensor data collection strategy based on IoT is utilized which would improve the efficiency and accuracy of the supply chain network with the minimized size of dataset and vehicle routing algorithm is introduced and tracing the contamination sources of infected food in the markets. Our proposed model is evaluated with the comprehensive evaluation and used various performance metrics such as tracing accuracy, delay, execution time, and traveling time. The results show that the proposed system outperforms when compared with existing approach. [ABSTRACT FROM AUTHOR]

• Proposed novel IoT based FoG assisted cloud network architecture. • Proposed task priority and load balancing algorithms. • Wearable sensor devices are used for collecting the health related data. • Real-time analysis in smart sustainable environment. In the recent years, important key factor for urban planning is to analyze the sustainability and its functionality towards smart cities. Presently, many researchers employ the conservative machine learning based analysis but those are not appropriate for IoT based health data analysis because of their physical feature extraction and low accuracy. In this paper, we propose remote health monitoring and data analysis by integrating IoT and deep learning concepts. We proposed novel IoT based FoG assisted cloud network architecture that accumulates real-time health care data from patients via several medical IoT sensor networks, these data are analyzed using a deep learning algorithm deployed at Fog based Healthcare Platform. Furthermore, the proposed methodology is applied to the sustainable smart cities to evaluate the process for real-time. The proposed framework not only analyses the healthcare data but also provides immediate relief measures to the patient facing critical conditions and needs immediate consultancy of doctor. Performance is measure in terms of accuracy, precision and sensitivity of the proposed DHNN with task scheduling algorithm and it is obtained 97.6%, 97.9%, and 94.9%. While accuracy, precision and sensitivity for deep CNN is 96.5%, 97.5% and 94% and for Deep auto-encoder is 92%, 91%, and 82.5%. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. A Beta probabilistic distribution is used to combine different trust evidences and direct trust has been calculated. The theory of ARMA/GARCH has been used to combine the recommendation trust evidences and predict the resultant trust value of each node in multi-step ahead. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models. [ABSTRACT FROM AUTHOR]

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. [Display omitted] • IoT-based energy optimization model for smart city scenarios. • Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO). • Several output metrics, such as the number of alive nodes, load, residual energy, and cost function, were used to pick the best cluster head nodes in IoT network clusters. • The proposed method enacts an intelligent duty cycling by predicting sleep–wake cycles. [ABSTRACT FROM AUTHOR]

Since last few years and in the present era of digitization everyone wants to become an IT incumbent. Intuitively saying, we are depending on technology which plays a vital role in our day to day life (Like Intelligent Communication and Internet of Things (IoT). Amid other technologies, in microstrip antenna Long Range-wide area network is one of the low power technologies which also provide a wider coverage area. So, it receives a huge attention from researchers working in the field of IoT applications. LoRa technology provides low data rate communication over a wider scale area. In the past few years, there is an increment in the research activities related to the antenna system design and its optimization in long range. With the increment in the wireless devices applications, the technology and antenna associated with these devices need to upgrade continuously for the efficient communication. In the antenna technologies for the Long Range-wide area network, the patch and printed antennas are the best options as it provides good features such as planar structure, light in weight and cheap with good antenna performance characteristics. The ease of their integration with the electronic system has opened a lot of new scope in the field of wide-area networks in IoT applications. In this article we have provided a summary of some of the latest research work published from 2016 to 2020 to explore more opportunities and challenges. First, a brief description of the technology is explained, and then the article discussed by categorizing the article into sections as : IOT, IoT Application with LoRa, Sensitization System, Implementation of these to design a Smart Cities and Real-Time Challenges and available Solutions. [ABSTRACT FROM AUTHOR]

In the modern world, there is a huge increase in the usage of various technologies which may produce heterogeneous data. Using the latest security algorithms like Triple Data Encryption Standard (TDES), Rivest Shamir Adleman (RSA), Advanced Encryption Standard (AES) these (heterogeneous) data are transferred over the internet. Despite of such good security mechanisms, there exist vulnerabilities to the user data. In the recent years, researchers started the usage of bio-inspired computing models towards security. In this paper, two such bio-inspired computing models namely splicing system and Deoxyribonucleic acid (DNA) sequencing were used. We proposed a novel cryptosystem using contextual array splicing system for DNA sequenced input data. In this process, we have designed Turing machine(s) that perform(s) the conversion of a binary input to DNA sequence and the cryptosystem operation using contextual array splicing system. Next, the developed novel cryptosystem is applied in the domain of Medical Internet of Things (MIoT). Further, we had investigated the proof of correctness. Next, the proposed cryptosystems are proved to be Turing computable. Finally, the security analysis and evaluation of the proposed cryptosystem has been studied. [ABSTRACT FROM AUTHOR]

Recently, there is an exponential increase in the multimedia and other data over the Internet of Things (IoT). This data is generally send to the cloud for processing and storage. The fog layer in-between readily bridges communication among the IoT devices and the cloud. It delivers services efficiently by computing and analyzing various multimedia information generated by the IoT devices residing on the sensors. However, provision of effective security and energy are critical challenges. The purpose of this work is to enhance the secure transfer of information like multimedia. This scheme uses Message Queue Telemetry Transport (MQTT) protocol over SSL/TLS. Since MQTT is vulnerable to eavesdropping, the Elliptic curve-ElGamal cryptography algorithm is introduced which lends a homomorphic factor thereby mitigating man-in-the-middle attack. The dynamic key change and proportional offloading of data as proposed in the current research work helps to preserve node energy by selectively transferring data to the cloud and the fog according to the data topic. The results depict that the system security and lifetime can be improved in comparison to the existing protocols. [ABSTRACT FROM AUTHOR]