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Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones.

Efficient character recognition in ancient handwritten Devanagari documents is crucial for societal advancements. Challenges such as overlapping characters, missing headlines, and over-inked stains further complicate the recognition process. In response, we propose a Capsule Network (CapsNet) with LSTM to address hierarchical temporal dependencies in Devanagari scripts, following initial implementation of a simple CNN. We also explored a combined CNN+LSTM architecture for character recognition, leveraging CNN’s feature extraction capabilities with LSTM’s sequential modeling to handle temporal dependencies in Devanagari scripts. Our experimentation involved a dataset of 10,825 characters from ancient Devanagari manuscripts, encompassing basic characters, modifiers, and conjuncts, classified into 399 classes. Testing various training–testing ratios (9:1, 8:2, and 7:3), we visually and statistically evaluated the experimental data, demonstrating the superiority of CapsNet and LSTM in handling these challenges. We calculated recognition accuracy, precision, and recall values, with CapsNet achieving a maximum accuracy of 95.98% after 30 epochs. This research underscores the effectiveness of CapsNet and LSTM in advancing character recognition for ancient Devanagari manuscripts.

Abstract Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users’ private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

Diagnosing brain tumors is difficult, especially at an early stage of the disease. Conventional approaches often cause delays in providing required treatment to the patients and shorten their lifespan. This paper presents a novel integrated approach with advanced subsets of artificial intelligence, including deep learning and supervised learning algorithms. These new technologies have demonstrated outstanding potential due to their ability to capture the appropriate features based on the input data. They can assist medical experts in identifying abnormal growth of cells inside the brain. We use publicly available brain magnetic resonance imaging (MRI) datasets to diagnose brain tumors and develop an automated system. The proposed approach uses data augmentation to enhance the image sizes and maintain standardization. We then deploy a visual geometry group with 16 layers following transfer learning to help minimize the medical experts’ workload in making accurate decisions. We extract the most significant features and improve the diagnostic speed and accuracy using a supervised learning algorithm and linear support vector machines (SVM). The proposed model outperforms the existing approaches with an accuracy of 98.87%, precision of 99.09%, recall of 98.73%, specificity of 99.02%, and F1-score of 98.91%.

Abstract The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with $$N=100$$ N = 100 with $$b=2$$ b = 2 over IRS with $$b=1$$ b = 1 .

Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet.

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

Abstract Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies.

The tremendous growth in the internet of things (IoT) technology has provided great support for e-healthcare applications. The remote health monitoring of patients at home is gaining popularity with the increase in design of number of IoT enabled wearable devices. The secure and efficient health data retrieval during continuous uploading of huge data on network is a challenge. In addition, another challenge exists due to limited resources available with IoT device to proceed efficient data retrieval. On the other hand, the edge cloud in edge computing technology provide abundant resources to its devices for various computations, storage and processing. The integration of IoT with edge would solve the resource constraint issue. However, efficient retrieval of health data from large pool of data would still be an issue due to IP based solutions offered by IoT. The Named Data Networking (NDN) is a promising solution for efficient delivery of data based on its name based content searching. This paper proposed NDN communication based secure remote healthcare monitoring framework by integrating IoT with edge computing where security to the each individual content is provided through hashing and encryption mechanism. The performance of the proposed framework is evaluated against its companion in Icarus simulator. The results proved the efficiency of our framework for content retrieval delay and cost associated in accessing the required data.

5G has been launched in a few countries of the world, so now all focus shifted towards the development of future 6G networks. 5G has connected all aspects of society. Ubiquitous connectivity has opened the doors for more data sharing. Although 5G is providing low latency, higher data rates, and high-speed yet there are some security-related vulnerabilities. Those security issues need to be mitigated for securing 6G networks from existing challenges. Classical cryptography will not remain enough for securing the 6G network. As all classical cryptography can be disabled with the help of quantum mechanics. Therefore, in the place of traditional security solutions, in this article, we have reviewed all the existing quantum solutions of 5G existing security issues to mitigate them and secure 6G in a Future Quantum World.

Abstract In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of $$\alpha = 1.5$$ α = 1.5 and $$\gamma = 2.5$$ γ = 2.5 with the varying number of nodes.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models’ performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models’ performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%.

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

An essential component of the Internet of Things (IoT) is wireless sensor networks (WSNs). Since individual sensor nodes are strongly power-constrained, several techniques are adopted to save power. By grouping nodes into clusters—thus reducing the transmission distance between sensor nodes and the base station (BS)—a clustering protocol can ensure energy preservation and increase the lifetime of the network. However, current clustering techniques have problems with the clustering structure that negatively impact their performance. Whenever routing protocols were implemented for a longer period of time, it was observed that they had a higher rate of energy consumption, a shorter period of stability, and fewer data transfers to the BS. In this paper, an improved region-based routing protocol (REERP) is developed for wireless sensor networks in the IoT is developed. It is based on (i) the addition of new nodes to the already formed clusters, (ii) the selection of the new head node based on the amount of residual energy, (iii) the setup of the multi-hop communication in all the regions of network, and (iv) the utilization of the energy hole reduction method. All of these tactics increase the useful life of the network. Performance has been evaluated against (1) a stable election protocol, (2) a gateway energy-aware routing protocol, and (3) a heterogeneous gateway energy-aware routing protocol, and using the metrics lifetime, energy consumption, number of dead nodes, and number of packets sent to the base station vs. number of packets acquired by the base station. The results of the proposed routing protocol have been found to outperform the state-of-the-art approaches considered.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Most data nowadays are stored in the cloud; therefore, cloud computing and its extension—fog computing—are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable.

The most important and emerging characteristic of Wireless Body Area Networks (WBANs), which differentiates them from other wired and wireless area networks, is mobility. Therefore, the routing protocols for WBAN are designed in such a way that they can deal with dynamic changes in topology and provide maximum throughput, packet delivery ratio, average end-to-end delay, and minimum energy consumption. Thus, achieving optimal values for every performance parameter becomes a big challenge. This work investigates the performance of three separate path discovery protocols, such as Destination-Sequenced Distance-Vector Routing (DSDV), Ad Hoc On-demand Distance Vector (AODV), and Ad Hoc On-demand Multipath Distance Vector Routing protocol (AOMDV), for two different mobility models with a fixed-positioned sink. During experimentation, the AOMDV routing protocol achieves a high packet delivery ratio (PDR), average end-to-end delay, and throughput as compared to other routing protocols.

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.

Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling.

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique.

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%.

Energy consumption is one of the constraints in wireless sensor networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues, viz., energy consumption, network lifetime, network scalability, and packet overhead. The key issue in WSN is that these networks suffer from the packet overhead, which is the root cause of more energy consumption and degrade the QoS in sensor networks. In WSN, there are several routing protocols, which are used to enhance the performance of the network. Out of those protocols, dynamic source routing (DSR) protocol is more suitable in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the efficiency decreases as the data packets need to wait at the initial point, where the packet has been sent and this increases the waiting time and end-to-end delay of the packets, which leads to increase in energy consumption. Our problem is to identify the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets conserved. In order to resolve these issues, we propose directional transmission-based energy aware routing protocol named PDORP. The proposed protocol PDORP has the characteristics of both power efficient gathering sensor information system and DSR routing protocols. In addition, hybridization of genetic algorithm and bacterial foraging optimization is applied to proposed routing protocol to identify energy efficient optimal paths. The performance analysis, comparison through a hybridization approach of the proposed routing protocol, gives better result comprising less bit error rate, less delay, less energy consumption, and better throughput, which leads to better QoS and prolong the lifetime of the network. Moreover, the computation model is adopted to evaluate and compare the performance of the both routing protocols using soft computing techniques.

One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

Various heterogeneous devices or objects will be integrated for transparent and seamless communication under the umbrella of Internet of things (IoT). This would facilitate the open access of data for the growth of various digital services. Building a general framework of IoT is a complex task because of the heterogeneity in devices, technologies, platforms and services operating in the same system. In this paper, we mainly focus on the framework for Big Data analytics in Smart City applications, which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication to improve the quality of life of citizens. A novel approach is proposed in this paper to enhance energy conservation and reduce the delay in Big Data gathering at tiny sensor nodes used in IoT framework. To implement the Smart City scenario in terms of Big Data in IoT, an efficient (optimized in quality of service) wireless sensor network (WSN) is required where communication of nodes is energy efficient. Thus, a new protocol, QoS-IoT(quality of service enabled IoT), is proposed on the top layer of the proposed architecture (the five-layer architecture consists of technology, data source, data management, application and utility programs) which is validated over the traditional protocols.