A Comprehensive Neural Network Model for Colorization and Captioning of Images Along with IoT Deployment

The convergence of artificial neural networks and the internet of things (IoT) has gained popularity in the field of computer science research. In this work, an efficient neural network model for the image colorization problem is proposed along with deploying these models to the remote system using IoT deployment tools. Further, this work proposed two convolution neural network models namely the Alpha model and Beta model towards solving the image colorization of the grayscale format. An efficient combination of models is proposed and analyzed such that the loss rate is minimized as?~?0.005. Next, an efficient model for solving image captioning is proposed based on the bi-directional long short term memory model. Finally, the work discusses the merits and demerits of deploying the neural network model using the AWS Greengrass and Docker IoT environment on remote systems.

     

An IoT Virtualization Framework for Fast and Lossless Communication

An internet of things (IoT) scenario is characterized by heterogeneity found in the types of participating nodes, networks and type of data exchange. This creates a complicated scenario in relation to decision making, management and reliability. The proposed paper focuses on the design and evaluation of a management framework for an IoT scenario. This paper proposes IoT applications and a global framework to improve the applications performance under various practical. The proposed global framework is designed with network of communication systems, in which the communication has to be fast enough and lossless. The virtualization of the network allows for low computational complexity and improved processing efficiency. The various contributions have been made in the applications that were found by IoT. The proposed global framework controls data transmission delay, reduces the data loss and also improves the network performance.     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

Fog-Sec: Secure end-to-end communication in fog-enabled IoT network using permissioned blockchain system

The technological integration of the Internet of Things (IoT)-Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT-Fog-Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog-enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog-enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog-enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog-based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device-to-device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state-of-the-art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.     

EOMR: An Energy-Efficient Optimal Multi-path Routing Protocol to Improve QoS in Wireless Sensor Network for IoT Applications

The wireless sensor network based IoT applications mainly suffers from end to end delay, loss of packets during transmission, reduced lifetime of sensor nodes due to loss of energy. To address these challenges, we need to design an efficient routing protocol that not only improves the network performance but also enhances the Quality of Service. In this paper, we design an energy-efficient routing protocol for wireless sensor network based IoT application having unfairness in the network with high traffic load. The proposed protocol considers three-factor to select the optimal path, i.e., lifetime, reliability, and the traffic intensity at the next-hop node. Rigorous simulation has been performed using NS-2. Also, the performance of the proposed protocol is compared with other contemporary protocols. The results show that the proposed protocol performs better concerning energy saving, packet delivery ratio, end-to-end delay, and network lifetime compared to other protocols.

     

An improved fault detection crow search algorithm for wireless sensor network

In wireless sensor networks (WSNs), the collected data during monitoring environment can have some faulty data, and these faults can lead to the failure of a system. These faults may occur due to many factors such as environmental interference, low battery, and sensors aging etc. We need an efficient fault detection technique for preventing the failures of a WSN or an IoT system. To address this major issue, we have proposed a new nature-inspired approach for fault detection for WSNs called improved fault detection crow search algorithm (IFDCSA). IFDCSA is an improved version of the original crow search algorithm (CSA). The proposed algorithm first injects the faults into the datasets, and then the faults are classified using improved CSA and machine learning classifiers. The proposed work has been evaluated on the three real-world datasets, ie, Intel lab data, multihop labeled data, and SensorScope data, and predicts the faults with an average accuracy of 99.94%. The results of the proposed algorithm have been compared with the three different machine learning classifiers (random forest, k-nearest neighbors, and decision trees) and Zidi model. The proposed algorithm outperforms the other classifiers/models, thus generating higher accuracy and lower features without degrading the performance of the system. Index Terms—big data, crow search algorithm, IoT, machine learning, nature-inspired algorithm, wireless sensor network.     

A novel heuristic simulation‐optimization method for critical infrastructure in smart transportation systems

A simulation‐based optimization is a decision‐making tool that helps in identifying an optimal solution or a design for a system. An optimal solution and design are more meaningful if they enhance a smart system with sensing, computing, and monitoring capabilities with improved efficiency. In situations where testing the physical prototype is difficult, a computer‐based simulation and its optimization processes are helpful in providing low‐cost, speedy and lesser time‐ and resource‐consuming solutions. In this work, a comparative analysis of the proposed heuristic simulation‐optimization method for improving quality‐of‐service (QoS) is performed with generalized integrated optimization (a simulation approach based on genetic algorithms with evolutionary simulated annealing strategies having simplex search). In the proposed approach, feature‐based local (group) and global (network) formation processes are integrated with Internet of Things (IoT) based solutions for finding the optimum performance. Further, the simulated annealing method is applied for finding local and global optimum values supporting minimum traffic conditions. A small‐scale network of 50 to 100 nodes shows that genetic simulation optimization with multicriteria and multidimensional features performs better as compared to other simulation‐optimization approaches. Further, a minimum of 3.4% and a maximum of 16.2% improvement is observed in faster route identification for small‐scale IoT networks with simulation‐optimization constraints integrated model as compared to the traditional method. The proposed approach improves the critical infrastructure monitoring performance as compared to the generalized simulation‐optimization process in complex transportation scenarios with heavy traffic conditions. The communicational and computational‐cost complexities are least for the proposed approach.     

UAV-based LoRaWAN flying gateway for the internet of flying things

The Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost-effective way to extend the current terrestrial network coverage. For example, temporary extensions “on-request” of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real-case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV-based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV-required hardware resources.     

Intrusion detection and prevention system for an IoT environment

Internet of Things (IoT) security is the act of securing IoT devices and networks. IoT devices, including industrial machines, smart energy grids, and building automation, are extremely vulnerable. With the goal of shielding network systems from illegal access in cloud servers and IoT systems, Intrusion Detection Systems (IDSs) and Network-based Intrusion Prevention Systems (NBIPSs) are proposed in this study. An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering. The proposed NBIPS inspects network activity streams to identify and counteract misuse instances. The NBIPS is usually located specifically behind a firewall, and it provides a reciprocal layer of investigation that adversely chooses unsafe substances. Network-based IPS sensors can be installed either in an inline or a passive model. An inline sensor is installed to monitor the traffic passing through it. The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.     

Performance improvement of wireless secret key generation with colored noise for IoT

Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS‐based security is less explored for IoT. In this work, we analyze the performance of RSS‐based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS‐based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung‐Box test. Furthermore, we develop a key generation model and proposed a moving window averaging‐based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.     

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.     

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.     

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

     

Development of information collection scheme in internet of things environment for intelligent radiation monitoring systems

The new revolution in computing and wireless communications technologies led to Internet of Things (IoT). Information collection scheme performs an important role for energy efficient utilization and latency awareness in IoT environments. The enhancement of an effectual information collection scheme is crucial to improve the overall performance of the internet of things applications. In this paper, the proposed information collection scheme aimed to enhance the confidence regarding any captured measurements under IoT environment. The scheme can verify the selection of optimal information collection routes through using the Dijkstra algorithm. It depends on selecting the preferable IoT devices (collectors) with optimal paths in efficient energy utilization. The IoT devices (sources) have to elect whichever a preferable collector can deliver the captured information with an endeavour to sense the latest contextual information. They elect the optimal collectors through implicit and explicit solutions. Also, it considers different failure conditions to specify the optimal collection time for furtherance scalability of IoT environment. The simulation results show the ability of our scheme to improve the performance in terms of network lifetime, energy consumption, and reliability.

     

Load Balancing in Software-Defined Networks Using Spider Monkey Optimization Algorithm for the Internet of Things

In the Internet of Things (IoT), the number of devices connected to the internet, and they can collect and exchange information at any time. IoT is helpful for the progress of a smart city and different applications. Software-Defined Network (SDN) offers programmability and flexibility in the IoT network. Nevertheless, the adoption of the number of gadgets will increase the transmission delay and this will lead the network to heavy loaded. To overcome this issue, an efficient load balancing technique has to be presented in the SDN network. By considering this solution as an aim, spider monkey optimization algorithm based load balancing (LB-SMOA) is presented in this paper. Using this technique, the controller with minimum load is selected and this selected controller balances the load of the heavily loaded controller. Simulation results show that the performance of the proposed LB-SMOA outperforms the existing load balancing techniques in terms of average response time, packet loss rate, and throughput.

     

Certificateless online/offline signcryption for the Internet of Things

The Internet of Things (IoT) is an emerging network paradigm that aims to obtain the interactions among pervasive things through heterogeneous networks. Security is an important task in the IoT. Luo et al. (Secur Commun Netw 7(10): 1560–1569, 2014) proposed a certificateless online/offline signcryption (COOSC) scheme for the IoT (hereafter called LTX). Unfortunately, Shi et al. showed that LTX is not secure. An adversary can easily obtain the private key of a user by a ciphertext. Recently, Li et al. proposed a new COOSC scheme (hereafter called LZZ). However, both LTX and LZZ need a point multiplication operation in the online phase, which is not suitable for resource-constrained devices. To overcome this weakness, we propose a new COOSC scheme and prove its security in the random oracle model. In addition, we analyze the performance of our scheme and show its application in the IoT.     

物联网中音频事件检测方法的研究

Recently, the Internet of Things (IoT) has attracted more and more attention. Multimedia sensor network plays an important role in the IoT, and audio event detection in the multimedia sensor networks is one of the most important applications for the Internet of Things. In practice, it is hard to get enough real-world samples to generate the classifiers for some special audio events (e.g., car-crashing in the smart traffic system). In this paper, we introduce a TrAdaBoost-based method to solve the above problem. By using the proposed approach, we can train a strong classifier by using only a tiny amount of real-world data and a large number of more easily colle cted samples (e.g., collected from TV programs), even when the real-world data is not sufficient to train a model alone. We deploy this approach in a smart traffic system to evaluate its performance, and the experiment evaluations demonstrate that our method can achieve satisfying results.     

IoT-based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.     

Schedule-Based Cooperative Multi-agent Reinforcement Learning for Multi-channel Communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.