A data-efficient intrusion detection framework tailored for Wireless Sensor Networks (WSNs) is proposed by leveraging active learning and metaheuristic optimization techniques. This framework addresses three major limitations of traditional models: data imbalance, inefficient hyperparameter tuning, and the need for large labeled datasets. To handle class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, particularly enhancing learning in complex regions of the feature space. For hyperparameter optimization, the Sandpiper Optimization (SO) algorithm is employed to fine-tune the regularization parameter of Logistic Regression (LR), leading to improved generalization. The issue of limited labeled data is tackled using Active Learning Uncertainty (ALU) and Entropy-based Active Learning (ALE), which query the most informative samples from the unlabeled pool, maximizing learning with minimal annotation effort. Simulation results show that LRALU, LRALE, and LRSO outperform traditional models with improvements of 18.18%, 19.48%, and 9.09% in accuracy; 9.30%, 1.16%, and 9.30% in precision; 18.18%, 19.48%, and 9.09% in recall; 12.20%, 8.54%, and 7.32% in F1-score; and 14.63%, 12.20%, and 9.76% in ROC-AUC, respectively. Additionally, log loss is reduced by 6.45%, 6.45%, and 35.48% for LRALU, LRALE, and LRSO, respectively. These results demonstrate that integrating intelligent sampling, active learning, and nature-inspired optimization significantly enhances intrusion detection performance in WSNs, providing an annotation-efficient solution for practical deployment.

Robust and intelligent intrusion detection is vital for securing Internet of Things (IoT) ecosystems against evolving cyber threats. However, existing systems face challenges such as class imbalance, suboptimal model performance due to manual hyperparameter tuning, and the high cost of labeled data. These limitations are addressed using the TON IoT dataset. To resolve data imbalance, the proximity weighted random affine shadow sampling generates boundary-focused synthetic samples that preserve class distribution. Further, to tackle suboptimal performance, bayesian optimization is applied to LeNet, resulting in LeBayesNet, which discovers the optimal configuration for high-accuracy detection. Next, to mitigate the scarcity of labeled data, MargiLeNet leverages marginal-based active learning, annotating the most uncertain samples to enhance model learning efficiently. Experimental results show that LeBayesNet and MargiLeNet improve performance over existing models by 7.69% and 3.30% in accuracy, 7.69% and 3.30% in F1-score, 7.69% and 3.30% in precision, 8.89% and 4.44% in the recall, 7.69% and 6.59% in receiver operating characteristic-area under the curve, 4.88% and 7.32% in matthews correlation coefficient, and 10.34% and 11.49% in precision-recall area under the curve, respectively. Both models significantly reduce hamming loss to 75% and 37.5%, indicating better generalization in complex and imbalanced scenarios. These advancements demonstrate the potential of optimization and active learning techniques in building accurate and adaptive intrusion detection systems for modern IoT networks.

This paper proposes a DL based framework using Multilayer Perceptron (MLP) tailored for multiclass DoS attack detection in Internet of Things (IoTs). After comparative data preprocessing, class imbalance is effectively mitigated using the proximity weighted random affine shadow oversampling method, enhancing minority class representation. Moreover, feature selection based on variance threshold is employed to streamline the input space and accelerate training. To reduce dependence on large labeled datasets, the approach incorporates Diversity-Based Sampling (DBS), an active learning strategy that focuses labeling efforts on diverse, informative samples. Furthermore, the proposed model’s performance is refined through metaheuristic-driven hyperparameter tuning using the Grasshopper Optimization Algorithm (GOA). This integrated methodology ensures more efficient learning, better generalization, and improved detection across varied attack scenarios in IoT settings. A comparative analysis with traditional machine deep learning and baseline models reveals that the proposed MLP+DBS and MLP+DBS+GOA model configurations consistently deliver superior performance across all evaluation metrics. Specifically, the proposed models achieve improvements of 5.7% and 9% in accuracy, 3.5% and 8.3% in precision, 5.7% and 9% in recall, 3.4% and 8.6% in F1-score, 2.3% and 3.3% in receiver operating characteristic area under the curve, and 3.3% and 6.6% in precision recall-area under the curve, respectively. These results demonstrate that the proposed models significantly outperform the existing approaches. This paper underscores the effectiveness of combining active learning and optimization for robust intrusion detection in resource-constrained IoT settings. The proposed models show strong potential for real-time deployment in smart environments requiring proactive and reliable security solutions.

Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, like Green’s and Maxwell’s equation used in electrostatics and electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed Green’s Theorem Fault-Detection (GTFD) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for self-stabilizing the network dynamically.

Swarm learning (SL) exploits the blockchain to realize a federated and decentralized learning, which is very suitable for internet of things (IoT). Different from FL using central server to update global parameter, SL using edge node (header) to do that. However, poisoning attack is also an unresolved problem to SL. Because if header is malicious, it can pollute global parameter more easily than edge nodes. Moreover, there are following important limitations in existing defense schemes for FL, which cannot be used in SL directly. First, existing defense schemes focus on building a whitelist, which obstructs the decentralization because it can just provide decentralization in honest nodes instead of all of nodes. Second, existing schemes just consider poisoning attacks from edge nodes, they cannot defend attacks from header. Third, most existing schemes will let server execute the defense algorithm, but in SL, malicious header can return wrong defense results to deceive managers. To address above challenges, in this paper, we propose a protection system that leverages the concept of zero-trust architecture for SL, which achieves continuous risk calculation, analysis of learning behavior and abnormal parameter detection based on Manhattan distance and accuracy difference of parameters. We also evaluate the performance in the presence of random and customized malicious edge nodes. Experimental results demonstrate that our scheme can achieve higher accuracy than the other existing schemes.

With the explosive growth of real-time applications in satellite systems, Time Sensitive Networking (TSN) is explored to be introduced to provide bounded low latency network services. Nevertheless, some efforts in delay ensuring techniques are ongoing, guaranteeing deterministic low latency communication in satellite networks is still a significant problem. In the article, we first present a Digital Twin-based TSN framework in which digital twin technology is introduced for the purpose of reducing the management cost and optimizing the performance of satellite networks. The virtual model of the scheduling method working in satellite systems is explored and created for the simulation and prediction of the forwarding delay results. Deep convolution generative adversarial network (DCGAN) is adopted to train the scheduling model. The simulation experiments verified that the digital twin could mirror the scheduling behavior and predict the delay in dynamic environments.

We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods” while the FRL performs best among all.

Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets.

Crowdsourcing has emerged as a promising computing paradigm that utilizes human intelligence to achieve complex tasks, but it encounters several security and trust issues. Blockchain is a potential technology that can resolve most of these issues, however, it is difficult to find an appropriate consensus protocol applicable to crowdsourcing systems. Therefore, this work presents a Trust and Transactions Chain (TTC) consensus protocol built upon blockchain technology. It selects a trusted leader and validators considering a trust model which depends on deposit ratio, block generation and validation rate, and waiting rate. The TTC protocol addresses the main challenge of ensuring correctness related to critical systems of crowdsourcing which has extreme significance as their failure can result in disastrous consequences. This work is primarily focused on fraud-resilience avoiding double-spending attack. It also deals with sybil and eclipse attacks. Model checking is exploited because it is effective and automatic to conduct formal verification. The TTC protocol is formally modeled utilizing Communicating Sequential Programs, and the fraud-resilience property is specified using Linear Temporal Logic. The verification of the model is done using Process Analysis Toolkit that takes the formal model and specified properties as input to inspect the properties’ satisfaction or violation. The results of the formal verification are analyzed with respect to the verification time and the number of visited states.

Product lifecycle management (PLM) is an effective method for enhancing the market competitiveness of modern manufacturing industries. The digital twin is characterized by a profound integration of physics and information systems, which provides a technical means for integrating multisource information and breaking the time and space barrier of communication at each link of the lifecycle. Currently, however, the application of this technology focuses primarily on the product itself and “service-oriented” application results. There is a lack of focus on twin data and its internal evolutionary mechanisms separately. In the management of global data resources, the benefits of digital twin technology cannot be fully realized. This article applies ontology technology in an innovative manner to the field of the digital twin to increase the reusability of twin data. Initially, a four-layered ontology-based twin data management architecture is presented. Then, a three-dimensional and three-granularity unified evolution model of full lifecycle twin data is proposed, as well as its ontology model. Then, the service mode of data components at each stage of the lifecycle is defined, a knowledge-sharing plane is established in the digital twin, and a data governance method based on ontology reasoning using data components on the shared plane is proposed. The ICandyBox simulation platform is then used to demonstrate the concept of the proposed method, and future research directions are proposed.

Intelligent Transport Systems (ITS) have received growing attention recently driven by technical advances in Industrial Internet of Vehicles (IIoV). In IIoV, vehicles report traffic data to management infrastructures to achieve better ITS services. To ensure security and privacy, many anonymous authentication-enabled data reporting protocols are proposed. However, these protocols usually require a large number of preloaded pseudonyms or involve a costly and irrevocable group signature. Thus, they are not ready for realistic deployment due to large storage overhead, expensive computation costs, or absence of malicious users' revocation. To address these issues, we present a novel data reporting protocol for edge-assisted ITS in this paper, where the traffic data is sent to distributed edge nodes for local processing. Specifically, we propose a new anonymous authentication scheme fine-tuned to fulfill the needs of vehicular data reporting, which allows authenticated vehicles to report unlimited unlinkable messages to edge nodes without huge pseudonyms download and storage costs. Moreover, we designed an efficient certificate update scheme based on a bivariate polynomial function. In this way, malicious vehicles can be revoked with time complexity$\mathcal {O}$(1). The security analysis demonstrates that our protocol satisfies source authentication, anonymity, unlinkability, traceability, revocability, nonframeability, and nonrepudiation. Further, extensive simulation results show that the performance of our protocol is greatly improved since the signature size is reduced by at least 8%, the computation costs in message signing and verification are reduced by at least 56% and 67%, respectively, and the packet loss rate is reduced by at least 14%.

The Internet of Things (IoT) is delivering solutions with improved efficiency and security, and providing better productivity in manufacturing, retail, and other sectors. Maritime Transportation Systems (MTSs) is currently adopting the IoT to move toward a digitalized, data-driven world with increased efficiency and lower costs, and creating new revenue opportunities. Integration of the IoT also enables real-time tracking of shipments, improved efficiency in cargo handling, pre-emptive maintenance, route optimization, reduced fuel consumption, and improved safety in maritime transportation systems. With IoT technology expanding and evolving rapidly, more applications are predicted to assist and improve all aspects of MTSs, making them hassle-free and safe.

Despite the benefits of the Internet of Things (IoT), the growing influx of IoT-specific malware coordinating large-scale cyberattacks via infected IoT devices has created a substantial threat to the Internet ecosystem. Assessing IoT systems’ security and developing mitigation measures to prevent the spread of IoT malware is therefore critical. Furthermore, for training and testing the fidelity of cyber security-based Machine Learning (ML) and Deep Learning (DL) approaches, the collection and exploration of information from multiple sources from the IoT are crucial. In this regard, we propose a multitask DL model for detecting IoT malware. Our proposed Long Short-Term Memory (LSTM) based model efficiently performs two tasks: 1) determination of whether the provided traffic is benign or malicious, and 2) determination of the malware type for identifying malicious network traffic. We used large-scale traffic data of 145.pcapfiles of benign and malicious traffic collected from 18 different IoT devices. We performed a time-series analysis on the packets of traffic flows, which were then used to train the proposed model. The features extracted from the dataset were categorized into three modalities: flow-related, traffic flag-related, and packet payload-related features. A feature selection approach was employed at the feature and modality levels, and the best modalities and features were utilized for performance enhancement. For tasks 1 and 2 and multitask classification, the flow-related and flag-related modalities showed the best testing accuracies of 92.63%, 88.45%, and 95.83%, respectively.

Digital twin (DT) provides a real-time digital representation of electric device state for energy dispatching and control (EDC) model training in power system. However, the large age of information (AoI) deteriorates the consistency of DT and the precision of EDC model. In this paper, we investigate the global loss function minimization problem underthe long-term AoI constraint through coordinated resource management. The optimization problem is decoupled based on telescoping sum and Lyapunov optimization, and solved by the proposed AoI-aware DT-assisted intelligent resource management algorithm named AoI-DT. AoI-DT achievesa balanced tradeoff between AoI guarantee and EDC model precision through device scheduling and channel allocation. Simulation results verify the superior performance of AoI-DT in terms of global loss function and AoI compared withtwo state-of-the-art algorithms.

The traffic anomaly analysis in IoT-based intelligent transportation system (ITS) is crucial to improving public transportation safety and efficiency. The issue is also challenging due to the unbalanced distribution of anomaly data in IoT-based ITS, which may cause overfitting or underfitting in the training phase. However, some research on traffic anomaly analysis injected limited data to address the shortage of anomaly samples or even neglects this issue, which overlooks the potential representation of nodes in graph neural networks. In this paper, we propose an improved contrastive GNN-based learning framework for traffic anomaly analysis that alleviates the problem of imbalanced datasets in the training phase. In this framework, we provide a graph augmentation approach with coupled features to learn different views of graph data. Besides, we design an effective training method based on the contrastive loss for our framework, which can learn the better performance of latent representations utilized in the downstream tasks. Finally, we conduct extensive experiments to evaluate the performance of our proposed frame-works based on real-world datasets. We demonstrate that our framework achieves as high as 6.45% precision improvement compared to the state-of-the-art.

As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical.

The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node.

In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating patients with severe diseases. When predictions of patient mortality and readmission are combined, these models gain a new level of significance. Therefore, the most expensive components of patient care are LOS and readmission rates. Several studies have assessed readmission to the hospital as a single-task issue. The performance, robustness, and stability of the model increase when many correlated tasks are optimized. This study develops multimodal multitasking Long Short-Term Memory (LSTM) Deep Learning (DL) model that can predict both LOS and readmission for patients using multi-sensory data from 47 patients. Continuous sensory data is divided into eight sections, each of which is recorded for an hour. The time steps are constructed using a dual 10-second window-based technique, resulting in six steps per hour. The 30 statistical features are computed by transforming the sensory input into the resulting vector. The proposed multitasking model predicts 30-day readmission as a binary classification problem and LOS as a regression task by constructing discrete time-step data based on the length of physical activity during a hospital stay. The proposed model is compared to a random forest for a single-task problem (classification or regression) because typical machine learning algorithms are unable to handle the multitasking challenge. In addition, sensory data combined with other cost-effective modalities such as demographics, laboratory tests, and comorbidities to construct reliable models for personalized, cost-effective, and medically acceptable prediction. With a high accuracy of 94.84%, the proposed multitask multimodal DL model classifies the patient's readmission status and determines the patient's LOS in hospital with a minimal Mean Square Error (MSE) of 0.025 and Root Mean Square Error (RMSE) of 0.077, which is promising, effective, and trustworthy.

Cloud computing is now being utilized as a prospective alternative for catering storage service. Security issues of cloud storage are a potential deterrent in its widespread adoption. Privacy breach, malicious modification, and data loss are emerging cyber threats against cloud storage. Recently, a fog server based three-layer architecture has been presented for secure storage employing multiple clouds. The underlying techniques used are Hash-Solomon code and customized hash algorithm in order to attain the goal. However, it resulted in loss of smaller portion of data to cloud servers and failed to provide better modification detection and data recoverability. This paper proposes a novel fog-centric secure cloud storage scheme to protect data against unauthorized access, modification, and destruction. To prevent illegitimate access, the proposed scheme employs a new technique$Xor - Combination$to conceal data. Moreover,$Block - Management$outsources the outcomes of$Xor - Combination$to prevent malicious retrieval and to ensure better recoverability in case of data loss. Simultaneously, we propose a technique based on hash algorithm in order to facilitate modification detection with higher probability. We demonstrate robustness of the proposed scheme through security analysis. Experimental results validate performance supremacy of the proposed scheme compared to contemporary solutions in terms of data processing time.

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme.

In this paper, we employ stochastic geometry to analyze ground-to-unmanned aerial vehicle (UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with uplink transmissions, where the distribution of UEs follows the Poisson Cluster process (PCP) and each UAV is dedicated to a specific cluster. In particular, we characterize the Laplace transform of the interference caused by multiple UEs in terms of the distribution of UEs as well as the transmission probability of each UE. We then derive analytical expressions of the successful transmission probability. We next conduct a comprehensive numerical analysis with consideration of different system parameters. The results show that four factors (i.e., the geographical surroundings, the transmission powers, the Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, and the UAV height) have main influences on ground-to-UAV communications.

COVID-19 (Coronavirus) is a very contagious infection that has drawn the world public’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system's design is a challenging problem. In this paper, motivated by the outstanding performance of deep learning (DL) in many solutions, we used DL based approach for computer-aided design (CAD) of the COVID-19 detection system. For this purpose, we used a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness by using two benchmark publicly available datasets (Covid-Chestxray-Dataset and Chex-Pert Dataset). The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a 10-fold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed system are high, and it performs better than the existing state-of-the-art systems. The proposed system based on DL will be helpful in medical diagnosis research and health care systems.

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

Real-estate advertisements through electronic and print media are bringing considerable fortune to the global real-estate sector. However, innovative advertisement methods must be adopted if real estate aims to transform into smart real estate. The current study, which is based on a systematic literature review of 58 articles published in the last decade, identifies key media for real-estate advertisements as print media (e.g., magazines, brochures, newspapers, and digests), electronic media (e.g., websites, social media, and other digital tools), and mixed methods (e.g., billboards, signs and banners, and personalized messaging). This study takes the case of Kingsford suburb in the eastern Sydney area, and investigates the performance of the Australian real-estate industry in general and lists the key dynamics of properties in Kingsford and its prominent real-estate agencies. An unmanned aerial vehicle (UAV)-based smart real-estate advertisement material delivery system is proposed to deliver advertisement materials and gifts to the potential customers of these agencies. The system paths are optimized through four Java-run algorithms: greedy, interroute, intraroute, and Tabu. Results based on six cases, three each for rent and sales with varying numbers of customers and UAVs and an 8-h operating time, indicate that the Tabu algorithm provides the best-optimized paths in all cases, followed by the interroute, intraroute, and greedy algorithms. However, the inter- and intraroute algorithms show superior performance in terms of computation speed. The proposed framework is a practical approach in disrupting the real-estate advertising sector, thereby helping this sector transform into a smart real estate consistent with industry 4.0 goals.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.

Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

Parkinson's disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson's patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson's patients' count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient's conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson's over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson's progression.

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.

With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.

Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV.

Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%.

In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity.

Modern power grids depend on the Advanced Metering Infrastructure (AMI) for consumption monitoring, energy management and billing. However, AMIs are vulnerable to electricity theft cyber attacks due to addition of communication layer. Electricity theft is one of the major Non-Technical Losses (NTLs) in the electricity distribution systems that has become a global concern, recently. Although the machine learning techniques are widely used for Electricity Theft Detection (ETD) in literature, some significant challenges need to be address. (i) The consumption data is usually unlabeled, there should be proper method to label the data. (ii) The fair consumers significantly outnumber the fraudulent consumers, which negatively impacts the performance of classification algorithm. (iii) The performance of classifier must be validated using proper performance evaluation measures. In this paper, an enhanced ETD model is proposed that is an optimized classifier Differential Evaluation Random Under Sampling Boosting (DE-RUSBoost) is used for classification. Proposed classifier DE-RUSBoost is optimized using a metaheuristic optimization algorithm named Differential Evaluation (DE). The proposed method is evaluated on a real-world dataset, i.e., State Grid Corporation of China (SGCC) datasets. DE-RUSBoost achieves the highest accuracy of 96% and low false detection rate of 0.004. The proposed method outperforms its counterparts in terms of accuracy and false detection rate.

Smart grid (SG) is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy (RE), smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as security-based reliability issues and energy inadequacy. In addition, existing energy planning models like levelized cost of energy (LCOE) that evaluate the cost of RE do not measure the impact of reliability on energy cost. LCOE is a method used to compare the economic costs of RE and non-RE. However, the main problem of evaluating RE based on LCOE is that it does not consider the fill rate (FR) and service level (SL) effect. This paper proposes a direct communication-based LCOE (BLCOE) model as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. The model also considers daily variations in the cost of solar modules and battery storage across sub-Sahara Africa (SSA). Furthermore, Quasi-Newton's method is employed to optimize the capacity of solar module and battery storage. Simulation results show the reduction of energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing LCOE used in the literature.

The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration.

In this study, we propose a privacy preservation and efficient distributed searching and matching of Electric Vehicles (EVs) charging demander with suppliers based on reputation. Partially homomorphic encryption-based on reputation computation using local communication is used in the implementation, while hiding EVs users' location. A private blockchain is incorporated in the system to verify and permit secure trading of energy among the EVs' demander and suppliers. The results of the simulation show that the proposed privacy preserved algorithm converges more faster as compared to Bichromatic Mutual Nearest Neighbor (BMNN) algorithm.

Electricity theft is the primary cause of electrical power loss that significantly affects the revenue loss and the quality of electrical power. Nevertheless, the existing methods for the detection of this criminal behavior of theft are diversified and complicated since the imbalanced nature of the dataset, and high dimensionality of time-series data make it challenging to extract meaningful information. This paper addresses these problems by developing a novel electricity theft detection model, integrating three algorithms in a pipeline. The proposed method first applies the synthetic minority oversampling technique (SMOTE) for balancing the dataset, secondly integration of kernel function and principal component analysis (KPCA) for the feature extraction from high dimensional time-series data, and support vector machine (SVM) for the classification. Besides, the performance of the proposed pipeline is measured using a comprehensive list of performance metrics. Extensive experiments are performed by using real electricity consumption data, and results show that the proposed method outperforms other methods in terms of theft detection.

In this paper, we deal with some major energy issues related to the charging of vehicles in vehicular network. The exponential increase of Electric Vehicles (EVs) has led to the more complex problems. In general, there are two major issues related to th EVs. First, its difficult to find a nearest charging station with required energy. Second, how much energy is needed to reach charging station from current location. In traditional systems, the energy trading between charging station and EVs is not secured due to centralized girds. To deal with this problem, a consortium blockchain based secure energy trading system is proposed. Blockchain is used for secure energy trading with moderate cost. The main purpose of the proposed system is resource reduction and find out the present state of charging stations. Simulations and results show that the proposed schemes outperform the conventional schemes in terms of minimizing the charging cost of battery and expenses of EVs.

An Intelligent Transportation System (ITS) aims to achieve efficiency of traffic by minimizing its problems, such as traffic congestion, road accidents, etc. It is not only limited to control traffic congestion but also enhances the safety and comfort of the commuters. For road traffic safety and efficient infrastructure usage, vehicles need to communicate with each other to disseminate information related to traffic. However, vehicles cannot directly communicate with each other and other infrastructure because of privacy and security concerns. In the proposed work, blockchain is implemented on Road Side Units (RSUs) that are used to provide reliable communication between vehicles. Furthermore, cloud and edge servers are used to tackle the storage issue. We proposed a Transactions Filtering Pattern Matching Scheme (TFPMS) to filter the transactions before sending them to the blockchain network. In this way, it saves storage space and reduces computational overhead of blockchain. Moreover, we are exploiting consortium blockchain to implement our proposed scheme. Simulations are performed based on the number of transactions and cost to achieve high-quality data sharing between vehicles, which result in a reduction in storage overhead as compared to the existing schemes.

A large amount of data is involved in an effective and timely exchange of traffic information between vehicles in Vehicular Ad-hoc Networks (VANETs), which ensures efficiency and reliability. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. Moreover, traditional storage mechanisms require the involvement of third parties, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, we proposed a blockchain-based data storage scheme for VANETs by exploiting the benefits of the Interplanetary File System (IPFS), which is deployed on Road Side Units (RSUs). Furthermore, RSUs are able to receive the aggregation packet comprising of the event information acquired from the vehicles. After receiving and verifying the aggregation packet, the RSU stores the event's information in IPFS and the reputation values of vehicles in blockchain. Moreover, we proposed an incentive mechanism in this work, in which monetary incentives are given to the repliers who agree with the vehicle regarding the event information. The incentives are given by the initiator after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in blockchain. The simulation results prove the efficiency of the proposed scheme in terms of transaction cost and storage savings in VANETs.

Advertisement sharing in vehicular network through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is a fascinating in-vehicle service for advertisers and the users due to multiple reasons. It enable advertisers to promote their product or services in the region of their interest. Also the users get to receive more relevant ads. Usually, users tend to contribute in dissemination of ads if their privacy is preserved and if some incentive is provided. Recent researches have focused on enabling both of the parameters for the users by developing fair incentive mechanism which preserves privacy by using Zero-Knowledge Proof of Knowledge (ZKPoK) (Ming et al., 2019). However, the anonymity provided by ZKPoK can introduce internal attacker scenarios in the network due to which authenticated users can disseminate fake ads in the network without payment. As the existing scheme uses certificate-less cryptography, due to which malicious users cannot be removed from the network. In order to resolve these challenges, we employed conditional anonymity and introduced Monitoring Authority (MA) in the system. In our proposed scheme, the pseudonyms are assigned to the vehicles while their real identities are stored in Certification Authority (CA) in encrypted form. The pseudonyms are updated after a pre-defined time threshold to prevent behavioural privacy leakage. We performed security and performance analysis to show the efficiency of our proposed system.

In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness.

The significance of electricity cannot be overlooked in terms of advancements in economic and technological fields. In this study, Ensemble Empirical Mode Decomposition (EEMD) method in combination with the Ensemble Bi-Long Short Term Memory (EBiLSTM) and Support Vector Machine (SVM) is used. Non linear and non stationary IMFs are forecast using EBiLSTM forecasting algorithm as it performs efficiently in complex and non linear scenario. Whereas, linear IMFs are forecast using SVM as EBiLSTM take high computational time unlike SVM. The proposed technique EEMD-EBiLSTM-SVM gives good results.

The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in the Internet of Vehicles (IoV). However, the trustless environment in IoV enabled data trading faces conflicting interests and disputes of trading parties. To build trust, we exploit consortium blockchain for secure data trading with information transparency. In addition, a hash list of traded data is maintained by roadside units accompanied by bloom filters for fast lookup, to avoid data duplication. The reliability and integrity of trading data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. For long term availability of traded data, an external distributed storage, i.e., InterPlanetary File System (IPFS) can provide reliable, high capacity storage resources. The experimental results verified that our proposed solution is efficient for data trading in IoV and reliable for long term availability of data storage.

In this paper, a Hybrid Deep Neural Network (HDNN) is proposed in this work, which is the combination of Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Particle Swarm Optimization (PSO), termed as CNN-GRU-PSO HDNN. In this paper, real time electricity consumption data of consumers is used, which is taken from an easily available online source, named as State Grid Corporation of China (SGCC). The original dataset consists of actual values along with the erroneous and missing values. The pre-processing steps are performed initially to refine the data. After that, feature selection and extraction are performed using CNN, which reduce both the dimensionality and the redundancy present in the dataset. Furthermore, the classification of provided data into honest and fake consumers is done using GRU-PSO technique. The proposed HDNN model's performance is then compared with various benchmark techniques like Logistic Regression (LR), Support Vector Machine (SVM), Long Short Term Memory (LSTM) and GRU. The efficiency of the proposed model is validated using various performance parameters like Area Under the Curve (AUC), precision, accuracy, recall and F1-Score. The simulation results show that the proposed model outperforms the existing techniques in terms of ETD and class imbalanced issues. Moreover, the proposed model is also more robust and accurate than the existing methods.

In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques.

Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT.

The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness.

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.

Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications.

A rapid development has been seen in the Vehicular ad hoc networks (VANETs) because of their applicability and significance in the fields of traffic management, road monitoring and safety, infotainment, and on-demand services. Route planning in vehicular networks based on efficient collection of real-time data can effectively mitigate traffic congestion problems in urban areas. Furthermore, real-time data is shared by using an effective sharing mechanism to avoid redundancy of the collected information. However, dynamic route replanning and effective sharing mechanisms based on real-time data are still challenging problems. Therefore, based on the aforementioned constraints, this paper describes a route discovery technique that uses real time data collected from various vehicles using the Internet of Things. The proposed scheme is based on the novel data dissemination technique for information sharing among the roadside units. RTRD is comprised of VANETs, vehicular traffic servers, and a 5G-based cellular system of public transportation. By considering the traffic congestion in urban areas, the optimal path is calculated to re-plan routes based on the k shortest path algorithm, and a load balancing technique is adopted to avoid further congestion.

Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches. However, these approaches are not suitable for IoTs devices due to their limited storage and computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. Blockchain also supports data sharing mechanism. In this paper, we proposed a secure service providing mechanism for IoTs using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The edge node reputation is considered as a service rating given by end users. Incentive is given to edge servers after validation of service codes. Incentive is in the form of cryptocurrency. Incentive and edge node reputation values are stored in cloud node and are updated with respect to time. Smart contract is proposed to check the validity state of the edge servers. Smart contract is also used for the comparison and verification of the service codes provided by edge servers. In our proposed system, we perform service authentication at both cloud and edge server layer. Moreover, Proof of Authority (PoA) is used as a consensus mechanism. PoA enhanced overall performance of our proposed system. By experimental analysis, it is shown that our proposed model is suitable for resource constrained devices.

The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced.

Buffer-aided cooperative relaying is often investigated either using decode and forward (DF) or amplify and forward (AF) relaying rules. However, it is seldom investigated using the hybrid decode-amplify-forward (HDAF) relaying rule. In this work, the performance of signal-to-noise ratio (SNR) based HDAF relaying rule is followed for buffer-aided cooperative relaying. Relay with the best possible corresponding channel is determined for reception or transmission. When source to relay hop is the most powerful, data is forwarded to chosen relay and its SNR is compared against the predefined SNR threshold at the relay. If it is greater than the threshold, the decoded data is saved in the corresponding buffer. Otherwise, the amplified data is saved in the respective buffer. When relay to destination link is the most powerful, data is forwarded to the destination. The famous Markov chain analytical model is used to illustrate the progression of the buffer state and to get the outage probability expression. Mathematical and simulation outcomes support our findings and prove that the outage probability performance of the proposed technique beats the existing SNR based buffer-aided relaying protocols based on DF and AF relaying rules by 2.43 dBs and 8.6 dBs, respectively.

Despite significant performance gains, buffer-aided cooperative communication incurs an increased latency. It is handled by prioritizing relay-to-destination links. The contribution of this paper is two-fold, firstly, we studied buffer threshold based decode-and-forward (DF) relaying for Rayleigh fading channels in Nakagami-m fading channels. Secondly, we present the outage analysis of buffer occupancy based amplify and forward (AF) relaying by introducing a modified threshold in terms of signal to noise ratio at relay and destination, which enables the conventional Markov chain (MC) based analysis of DF relaying to work for AF relaying with slight modifications. Using this approach, we evaluate the system using MC-based analysis for outage probability, latency and throughput. The results depict that the buffer threshold based relaying can significantly decrease the latency and increase the average throughput by negotiating the outage probability. Furthermore, extensive Monte-Carlo simulations are carried out to prove the theoretical outcomes.

In recent times, different routing protocols have been proposed in the Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) to explore the underwater environment for different purposes, i.e., scientific and military purposes. However, high Energy Consumption (EC), End to End (E2E) delay, low Packet Delivery Ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in Underwater Wireless Sensor Network (UWSN). The high E2E delay, EC and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. So, this paper presents two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy Efficient Routing (Im-CBE2R) protocol. At this end, both routing protocols minimize the probability of void hole occurrence and in return minimizes the EC and E2E delay. In both routing protocols, courier nodes are positioned at different strategic locations to keep the greedy forwarding continuous. The proposed routing protocols are also analyzed by varying the Packet Size (PS) and Data Rate (DR). Additionally, various simulations have been performed to authenticate the proposed routing protocols. Simulation results show that the proposed routing protocols outperform the baseline routing protocols in counterparts.

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development.

Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.

Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.

Non Orthogonal Multiple Access (NOMA) has become one of the prospective candidates for upcoming 5thgeneration Cellular network standard. Multicarrier NOMA (MC-NOMA) is a kind of hybrid NOMA, where, Orthogonal Frequency Division Multiple Access (OFDMA) may be employed to achieve higher capacity. However, MC-NOMA scheme based on OFDMA face the problem of higher Peak-to-Average Power Ratio (PAPR). The high PAPR reduces both energy and spectral efficiency of the MC-NOMA scheme. Therefore, in this paper, a new Finite Impulse Response (FIR) filtering based Zadoff-Chu Matrix Transform (ZCMT) precoded uplink MC-NOMA scheme is presented to reduce higher PAPR. MATLAB® simulations demonstrate that, the proposed filter based ZCMT precoded uplink MC-NOMA scheme outperform the ZCMT precoded uplink MC-NOMA schemes without filtering, uplink Single Carrier NOMA (SC-NOMA) schemes and conventional uplink MC-NOMA schemes available in the literature.

In energy constraint networks, the utilization of limited node battery is very crucial to enhance the network lifespan. The imbalanced node battery dissipation greatly effects the performance of the network. In this paper, we propose QLearning based energy-efficient and balanced data gathering routing protocol (QL-EEBDG). The effectiveness of a forwarder node is computed based on; residual energy of the source node and group energies of the neighbour nodes. The consideration of energy parameters provides complete control on the forwarder node selection and ensures efficient energy consumptions in the network. Still, due to topology changes, void node occurs which is avoided through adjacent node technique (QL-EEBDG-ADN). This scheme finds an alternate route via neighbor nodes to provide continuous communication among the network nodes. Simulations are performed to validate the effectiveness of proposed schemes against existing scheme based on energy tax, network lifetime.

This paper explored cooperative relaying in the presence of energy constrained relays with data storage facility. The relays depend only on the source signal to harvest energy and forward signal to the destination. The relay is selected according to the instantaneous strength of the wireless link. The strongest link among all links on both sides i.e., source-relay and relaydestination links is selected for relay to receive or transmit data, respectively. Two protocols are used for energy harvesting and information transfer namely: ”power splitting based relaying” and ”time switching based relaying”. We evaluate the outage probability performance of the presented scheme using Monte Carlo simulations. The results show that TSR performs better than PSR protocol.

This piece of work presents a novel design for buffer-aided relaying to increase the diversity gain. In this design, we used random buffer access method and associate each buffer location with its own antenna resource. Thus, each buffer location acts as an independent entity known as the virtual relay. The relay selection is based on the instantaneous strength of wireless link and the status of buffers. Markov chain is used to illustrate the growth of buffer status and to derive the closed-form expressions for the outage probability and diversity gain. The proposed design achieves the diversity gain of KL as compared to current buffer-aided max-link and max-max schemes having the diversity gain of 2K and K, respectively. Moreover, the proposed design achieves less delay at low SNR and 1+KL at high SNR. Analytical results are validated via simulation results.

This paper presents a decisive threshold termed as the buffer-threshold to control the selection probability of source-relay (SR) or relay-destination (RD) links for the buffer aided cooperative wireless networks. The weights of the links are reassigned using buffer-threshold and a link with the maximum weight is activated. The proposed scheme is termed as Buffer-Threshold based relay selection scheme (BTRS). The relations of the outage probability (OP) and the average delay are calculated by the Markov Modelling of the buffers. Theoretical results are analyzed for different cases of the buffer-threshold and validated by the Monte-carlo simulations. For the performance evaluation BTRS is compared with the max link relay selection (MLRS) scheme and the max weight relay selection (MWRS) scheme and outperforms its counterparts in terms of the average delay.

Recently, Internet of Vehicles (IoVs) is getting growing interest because of their suitability for a wide range of emerging applications. Most of these applications require vehicles to continuously update their information to a centralized location in order to gain various services. However, frequent transmission of messages by an abundance number of vehicles may not only overwhelm a centralized server but also causes a huge congestion which might disrupt various services including emergency situations. The aim of this research is to minimize congestion and messaging delay. This paper presents a fog-assisted congestion avoidance scheme for IoV named Energy Efficient Message Dissemination (E2MD). To capitalize the merits of fog computing and minimize latency, E2MD opts a distributed approach by employing a fog server to complement services in IoVs. In E2MD, vehicles continuously update their status to a fog server either directly or through intermediate nodes. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance supremacy of E2MD compared to contemporary schemes in terms of end-to-end delay and messaging cost.

In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission.

Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle‐to‐Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.

Mobile Cloud Computing (MCC) extends cloud services to the resource-constrained mobile devices. Compute-intensive mobile applications can be augmented using cloud either in client/server model or through cyber foraging. However, long or permanent network disconnections due to user mobility increase the execution time and in certain cases refrain the mobile devices from getting response back for the remotely performed execution. In this paper, we propose use of process state synchronization (PSS) as a mechanism to mitigate the impact of network disconnections on the service continuity of cloud-based interactive mobile applications. To validate the PSS-based execution, we develop a mathematical model that incorporates the disconnection and synchronization intervals, and mobile device capabilities along with that of cloud. The comparison with existing mechanisms shows that PSS reduces the execution time by upto 47% for intermittent network connectivity compared to COMET and by upto 35% for optimized VM-based offloading.

Geosocial Network data can be served as an asset for the authorities to make real-time decisions and future planning by analyzing geosocial media posts. However, there are millions of Geosocial Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make real-time decisions. Therefore, in this paper, we proposed an efficient system for exploring Geosocial Networks while harvesting data as well as user's location information. A system architecture is proposed that processes an abundant amount of various social networks' data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem in order to run real-time analyses. Twitter and Flickr are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus, and snow. The system is evaluated with respect to efficiency while considering system throughput. We proved that the system has higher throughput and is capable of analyzing massive Geosocial Network data at real-time.

Modeling of Underwater Wireless Sensor Networks (UWSNs) with a goal of maximum network lifetime and throughput with minimum energy consumption is a quite difficult task because of limited battery power and harsh underwater environment. Balanced Energy Efficient Rectangular routing protocol (BEER) covers the maximum network area with the mobility of sinks and collects the data from sensor nodes in their transmission range using direct transmission. Sink movement maximizes the throughput and balanced the energy consumption. Simulation results verify that our scheme performs outstanding in terms of network lifetime, stability period and throughput with minimum energy consumption.

Underwater wireless sensor networks (UWSNs) arouse as a better alternative of underwater wired instruments for data gathering. Acoustic signals offer low bandwidth and UWSNs faces low reliability, high delay and high energy consumption issues. Moreover, energy holes creation decreases network performance in terms of energy and throughput. The design of routing protocols which considers these challenges can improve data gathering. In this paper, we propose forward layered multipath power control-one (FLMPC-One) and FLMPC-Two routing protocols to reduce energy utilization, achieve reliability and elude energy holes. Both FLMPC-One and FLMPC-Two are multicast routing protocols. In order to achieve reliability, both schemes direct multiple copies towards surface through different paths which posses low noises by establishing binary tree. Mostly, current forwarder takes decision of next forwarder selection and gets deceived by energy holes. Therefore, FLMPC-One and FLMPC-Two makes decision by including two and three hops neighbors, respectively to detect and elude energy hole. In this way, they conserve energy and reduce delay introduced by retransmission.

In this paper, we propose an algorithm to alleviate coverage hole problem using geographic routing strategy for wireless sensor networks (WSNs). In order to accomplish desired results, an optimal number of forwarder nodes is computed along with the selection of path that has minimum energy consumption. Moreover, at each hop residual energy of a sensor is calculated and knowledge up-to one hop neighbors of forwarder node that ensures the avoidance of energy hole problem. Simulations are conducted to validate that our claim of outperforming compared existing schemes in terms of packet delivery ratio (PDR) and energy dissipation of the network nodes.

In Wireless Sensor Networks (WSNs), there are two major factors which minimize the performance of the network. The one is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other one is the presence of energy hole near the sink due to death of the nodes. An optimum transmission strategy is the need in this case in order to maximize the network lifetime via hole alleviation. To this end, we provide a solution that is able to optimize the network performance through balance transmission strategy by equally dividing the network into number of sectors to balance the energy among the nodes. Two types of nodes are considered, the one with minimum energy level are normal nodes, the other one with maximum energy level are super nodes. Void hole is removed while selecting super node as a data forwarder in each region and energy hole is alleviated while selecting super node as a forwarder for data transmission in case when nodes near the sink exhaust their energy and die out.

Due to the limited battery power of sensor nodes, design of underwater wireless sensor networks (UWSNs) is very difficult. Information is not gained efficiently through sensor nodes in aquatic environment and energy consumption is also the major problem in UWSNs. We proposed logical sub-region with sink mobility for throughput maximization and energy consumption minimization in rectangular UWSNs (LSSR). Sensor nodes are randomly deployed in the network field. Two mobile sinks are moving strategically in the network field and gather data from their respective nodes and cover the maximum area of the network field. LSSR performs better in terms of network lifetime, throughput and residual energy as shown in the simulation results.

This paper presents an incremental cooperative communication scheme for a buffer-aided three node relay network consisting of a source, an Amplify and Forward (AF) relay and a destination. We consider the presence of direct link and propose a scheme which is based on the concept of packet diversity rather than link diversity. The scheme is designed in such a way that packets that experienced bad channel conditions in source-relay link experience good channel conditions in the relay-destination link and vice versa. Source multi-casts all packets to both relay and destination. If direct transmission is not successful, source needs assistance from the relay. Based on the quality of relay-destination link, a packet is picked from buffer at the relay to be transmitted to the destination. Further, direct and relayed signals are combined at the destination using Maximal Ratio Combining (MRC) technique. The closed-form expressions for outage and error probabilities are derived, further, the delay and the diversity orders for the proposed scheme are also investigated.

Relay selection in cooperative communication is an efficient approach to mitigate the spectral efficiency loss faced in cooperative diversity systems. In this paper, we propose a relay selection scheme for buffer-aided cooperative systems. It simultaneously considers the instantaneous link quality and the buffer status of relay nodes in the relay selection decision. The normalized and weighted sum of these parameters results into the overall score of each link, and a link with maximum score is selected. First, the concept of equivalent outage of a link corresponding to a buffer-aided relay is explained, and then Markov chain (MC) modeling is used for the evaluation of states of buffer. We provide the specific examples with fixed values of number of relays and buffer size. The system achieves the full diversity gain of 2K for the smaller buffer sizes.

Due to unique and unreliable characteristics of Underwater Wireless Sensor Networks (UWSN), providing scalable and energy efficient services are very challenging. Acoustic communication is used for the data transmission, which has limited bandwidth that causes the performance deficiencies. In this paper, we propose Singular division of Multipath Power Control (SMPC) routing protocol for UWSNs. We divided the network area into vertical sections of equal size and tree based routing strategy is established for the data transmission. Multiple copies of the same data packet are generated by the source nodes and send through its leaf nodes toward surface gateways. Then surface gateways deliver all these packets to the sink. Multiple copies are combined and original packet is generated by sink. Simulation results are conducted on the bases of different parameters, and results shows that SMPC significantly improve the network performance in sense of energy consumption and end to end delay.

Next generation mobile networks aim to provide faster speed and more capacity along with energy efficiency to support video streaming and massive data sharing in social and communication networks. In these networks, user equipment has to register with IP Multimedia Subsystem (IMS) which promises quality of service to the mobile users that frequently move across different access networks. After each handover caused due to mobility, IMS provides IPSec Security Association establishment and authentication phases. The main issue is that unnecessary reregistration after every handover results in latency and communication overhead. To tackle these issues, this paper presents a lightweight Fast IMS Mobility (FIM) registration scheme that avoids unnecessary conventional registration phases such as security associations, authentication, and authorization. FIM maintains a flag to avoid deregistration and sends a subsequent message to provide necessary parameters to IMS servers after mobility. It also handles the change of IP address for user equipment and transferring the security associations from old to new servers. We have validated the performance of FIM by developing a testbed consisting of IMS servers and user equipment. The experimental results demonstrate the performance supremacy of FIM. It reduces media disruption time, number of messages, and packet loss up to 67%, 100%, and 61%, respectively, as compared to preliminaries.

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objectives, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance.

The rate of data generation is enormously growing due to the number of internet users and its speed. This increases the possibility of intrusions causing serious financial damage. Detecting the intruders in such high-speed data networks is a challenging task. Therefore, in this paper, we present a high-speed Intrusion Detection System (IDS), capable of working in Big Data environment. The system design contains four layers, consisting of capturing layer, filtration and load balancing layer, processing layer, and the decision-making layer. Nine best parameters are selected for intruder flows classification using FSR and BER, as well as by analyzing the DARPA datasets. Among various machine learning approaches, the proposed system performs well on REPTree and J48 using the proposed features. The system evaluation and comparison results show that the system has better efficiency and accuracy as compare to existing systems with the overall 99.9 % true positive and less than 0.001 % false positive using REPTree.

Energy is one of the most precious resource in Wireless Sensor Networks (WSNs) which is mainly consumed in communication, sensing and processing. Performance evaluation of WSN routing protocols primarily rely on simulation-based studies. Most of these studies only assume oversimplified First Order Radio Model (FORM) and ignore node's energy consumed in sensing and processing. This paper presents an Advanced first order Energy Consumption Model (A-ECM) for terrestrial WSNs. Unlike FORM, A-ECM factors in essential energy guzzlers of wireless transmission and reception (i.e., coding rate, bit rate and decoding) besides sensing and processing. The performance of A-ECM is validated through simulations which demonstrate the effectiveness of A-ECM for realistic scenarios.

In this work, we propose a novel quality link metric; Bandwidth adjusted Inverse ETX (BIETX) for Static Wireless Multi-hop Networks (SWMhNs). The proposed metric considers two path selection parameters into account i.e., packet delivery ratio and link capacity. For computing packet delivery ratios in BIETX, the mechanism of Expected Transmission Count (ETX) is adopted. On the other hand, we take two methods of computing link capacity in BIETX. These methods are based upon the size of pair probes; equal size and different size. We also enhance Optimized Link State Routing (OLSR) protocol while using BIETX. A comparative analysis of proposed metric with equal size and different size pair probe; BIETX-1 and BIETX-2, with two existing quality metrics (ETX and Expected Transmission Time (ETT)) in SWMhNs is also a part of this work. From simulation results, we conclude that BIETX-2 outperforms rest of the metrics because of low routing load in ad-hoc probes, and low routing latencies due to enhancements of routing update frequencies and window size in OLSR.

Internet service providers (ISPs) and telecommunication authorities are interested in detecting VoIP calls either to block illegal commercial VoIP or prioritize the paid users VoIP calls. Signature-based, port-based, and pattern-based VoIP detection techniques are not more accurate and not efficient due to complex security and tunneling mechanisms used by VoIP. Therefore, in this paper, we propose a rule-based generic, robust, and efficient statistical analysis-based solution to identify encrypted, non-encrypted, or tunneled VoIP media (voice) flows using threshold approach. In addition, a system is proposed to efficiently process high-speed real-time network traffic. The accuracy and efficiency evaluation results and the comparative study show that the proposed system outperforms the existing systems with the ability to work in real-time and high-speed Big Data environment.

Abstract The unabated flurry of research activities to augment various mobile devices in terms of compute‐intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud (MAC) or mobile cloud. It is a new type of mobile cloud computing (MCC). MAC is deemed to be a candidate blueprint for future compute‐intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state‐of‐the‐art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity‐ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions. Copyright © 2016 John Wiley & Sons, Ltd.

Underwater Wireless Sensor Networks (UWSNs) are getting growing interest because of wide-range of applications. Most applications of these networks demand reliable data delivery over longer period in an efficient and timely manner. However, resource-constrained nature of these networks makes routing in a harsh and unpredictable underwater environment challenging. Most existing schemes either employ static or mobile sink for data collection. However, in former sensors near the sink deplete out their energy more quickly which limits network lifetime. Mobile sink based schemes are not suitable for delay-sensitive large-scale applications. Unlike prior work, this paper presents a novel Advanced AUV-aided Energy Efficient Routing Protocol (AAEERP) for reliable data delivery. To prolong network lifetime, AAEERP employs an autonomous underwater vehicle to collect data from gateways. To minimize energy consumption, we use a shortest path tree algorithm while associating sensor nodes with the gateways and devise a criterion to limit the association count of nodes. Moreover, the role of gateways is rotated to balance the energy consumption. To prevent data loss, AAEERP allows dynamic data collection time to AUV depending up the count of member sensors for each gateway. The performance of the AAEERP is validated through simulations. Simulation results demonstrate the effectiveness of AAEERP in terms of various performance metrics.

In this paper, we present need-based clustering (NbC) with dynamic sink mobility (DYN-NbC) scheme for wireless sensor networks (WSNs). Our proposed scheme increases the stability period, network lifetime, and throughput of the WSN. The scheme incorporates dynamic sink mobility in a way that mobile sink (MS) moves from dense (in terms of number of nodes) regions towards sparse regions. Intelligently moving the sink to high density regions ensure maximum collection of data. As, more number of nodes (sensors) are able to send data directly to MS, therefore, significant amount of energy is saved in each particular round. However, there is a certain limitation to this approach. Nodes which are far from sink have to wait much for their turn. So, there are chances of buffer (node storage) overflow that is not desirable. To overcome this issue our scheme includes. Clustering (communication via CHs) becomes the part for those regions which are away from MS. Simulation results show that DYN-NbC outperforms the other two protocols D-LEACH and LEACH in terms of stability period, network lifetime, and network throughput.

Orientation sensing is not a new concept. It is being used since ages however, with emergence of new technologies such as Wireless Body Area Sensor Networks (WBASNs), it gives new challenges. Commencement of smart phones that have built in orientation sensors are replacing expensive and complex Inertial Measurement Units (IMUs) designed for a specific purpose. Orientation sensing in WBASN have numerous applications. In e-health applications, rehabilitation investigation of backbone injuries can be measured by continues readings of posture. For that, gyroscopes and accelerometers are key sensors that play vital role. For machines such as robots and air crafts, such data fusion is in practice. However, considering human body movements yet there is a need to find an accurate fusion algorithm that meets all demands with low complexity. In this work, we discussed and compared two algorithms considering Wireless Body Area Sensor Fusion (WBASF) i.e. Kalman and Complementary data fusion techniques. According to our findings, Kalman Filter may have given very good results regarding machines however, Complementary filter proved itself better in performance, complexity and required computational power in WBASNs.

The state of the art of old-age grids into smart grids provides residents the opportunity to schedule their appliances to consume the energy optimally that leads to potentially balance the demand side as well as the supply side more effectively and minimizes the power Peak-to-Average Ratio (PAR), which ultimately provides benefit to the residents in the form of reduction cost and expense. The Energy Management System (EMS) in the home receives the market and system signals and controls the loads, Heating, Ventilation and Air Conditioning systems (HVAC), storages and local generation units according to the user preferences. This survey encompasses novel home energy management techniques including different shift able and non-shift able load scheduling methods and peak shaving strategies. Several Pricing strategies have been suggested for smart grid such as, Real-Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP), etc. Moreover, this paper discusses the HEM architecture and reveals that the different energy management techniques intelligently schedule the appliances in order to satisfy the maximum resident's comfort level and consume the energy optimally.

The Machine-to-Machine (M2M) communication has the potential to connect millions of devices in the near future. Since they agree on this potential, several standard organizations need to focus on improved general architecture for M2M communications. Currently, there is a lack of consensus to improve the general feasibility of M2M communication. Heterogeneous Mobile Ad hoc Networks (HetMANETs) can normally be considered appropriate for M2M challenges. When a mobile node (MN) moves inside a HetMANET, various challenges including a selection of the target network and energy efficient scanning take place, which need to be addressed for efficient handover. To cope with these issues, we propose a handover management scheme that efficiently initiates a handover process and selects an optimal network. Our proposed scheme is composed of two phases, i.e., i) the MN performs handover triggering based on the optimization of the Receive Signal Strength (RSS) from an access point/base station (AP/BS), and, ii) the network selection process is carried out by considering different parameters such as delay, jitter, velocity, network load, and energy consumption by the network interface. Moreover, if there are more networks available, then the MN selects the one that can provide the highest quality-of- service (QoS) using the Elimination and Choice Expressing Reality (ELECTRE) decision model. The performance of the proposed scheme is compared in the context of the number of handovers, average stay-time of an MN in the network, and energy consumption against periodic and adaptive scanning. Similarly, a two- state Markov model is defined that efficiently distribute the number nodes on the available access points and base stations. The proposed scheme efficiently optimizes the handoff related parameters and outperforms existing schemes.

Provisioning network survivability is especially crucial in wireless sensor and actor network (WSAN) because nodes deployed in hostile environments are prone to frequent failures. Failure of an actor significantly impact actor connected coverage which is essential for effective network operation. Existing mobility-based recovery schemes are either geared towards restoring inter-actor connectivity or area coverage. None of them consider sustaining actor coverage (i.e., having sensors reachable to actors) while restoring inter-actor connectivity. This paper presents RACE, a novel mechanism to Restore Actor Connected Coverage with reduced recovery overhead. RACE distinguishes critical/non-critical actors based on 2-hop information to better assess the scope of the failure and optimize the recovery procedure. Neighbors of a failed actor employ a cooperative failure detection scheme and only perform a limited-scale network reconfiguration to adopt any bereaved sensors left unreachable (uncovered by an actor) due to failure of a non-critical actor. In case a critical actor fails, RACE substitutes it with a non-critical neighbor that has the least impact on coverage (i.e., number of sensors). If it is necessary to engage critical actors in the recovery, RACE is recursively applied by relocating actors until a non-critical node is picked. Simulation results confirm the performance advantage of RACE compared to the best contemporary schemes.

Wireless Body Area Networks (WBANs) are getting growing interest because of their suitability for wide range of medical and non-medical applications. These applications demand WBAN to stay functional for a longer time which requires energy-efficient operation. In this paper, we propose a new routing protocol for Balanced Energy Consumption (BEC) in WBANs. In BEC, relay nodes are selected based on a cost function. The nodes send their data to their nearest relay nodes to route it to the sink. The nodes closer to the sink send their data directly to it. Furthermore, the nodes send only critical data when their energy becomes less than a specific threshold. In order to distribute the load uniformly, relay nodes are rotated in each round based on a cost function. Simulation results show that BEC achieves 49% increased network lifetime than OINL (On Increasing Network Lifetime) algorithm.

The unique characteristics of Underwater Wireless Sensor Networks (UWSNs) attracted the research community to explore different aspects of these networks. Routing is one of the most important and challenging function in UWSNs, for efficient data communication and longevity of sensor node's battery timing. Sensor nodes have energy constraint because replacing the batteries of sensor nodes is an expensive and tough task in harsh aqueous environment. Also interference is a major performance influencing factor. Providing solutions for interference-free communication are also essential. In this paper, we propose three energy-efficient and interference-aware routing protocols named as Inverse Energy Efficient Depth-Based Routing protocol (IEEDBR), Interference-Aware Energy Efficient Depth-Based Routing protocol (IA-EEDBR) and Interference-Aware Inverse Energy Efficient Depth-Based Routing protocol (IA-IEEDBR). Unlike EEDBR, IEEDBR protocol uses depth and minimum residual energy information for selecting data forwarder. While IA-EEDBR takes minimum number of neighbors for forwarder selection. IA-IEEDBR considers depth, minimum residual energy along with minimum number of neighbors for selection of forwarder. Our proposed schemes are validated through simulation and the results demonstrate better performance in terms of improved network lifetime, maximized throughput and reduced path loss.

In Cognitive Radio Ad Hoc Networks (CRAHNs), cooperative spectrum sensing schemes exploit spatial diversity of the Secondary Users (SUs), to reliably detect an unoccupied licensed spectrum. Soft energy combining schemes provide optimal detection performance by combining the actual sensed information from SUs. For reliable data fusion, these techniques mandate weight estimation for individual SUs in each sensing interval, resulting in high cooperation overhead in terms of time, processing and bandwidth. Alternately, a hard energy combining scheme offers lower cooperation overhead in which only local SU decisions are reported to the fusion center. However, it provides sub-optimal detection performance due to the information loss. In this paper, a Log-Likelihood Ratio (LLR) based cooperative spectrum sensing scheme is proposed in which each SU performs a local LLR based sensing test employing two threshold levels. The local decision and sequentially estimated SNR parameter values (for weight computation) are not reported to the fusion center if the local test result is in-between the two threshold levels. Thereby, cooperation overhead is reduced in proportion to the hard combining techniques; nevertheless simulation results show that the detection performance of the proposed scheme is close to the optimal soft combining techniques.

Maintaining coverage-aware connectivity is extremely crucial for successful operation of mobile actor/robot networks as mobile nodes have to collaborate on the data received from sensors and perform coordinated action. However, failure of a critical node (i.e., cut vertex) may introduce a coverage hole besides partitioning the network into disjoint segments, and thus disrupts the operation. Most of the published schemes are reactive, require additional placement of nodes and only concentrate on restoring connectivity. Mission-critical time-sensitive applications crave an instantaneous self-healing recovery with minimum overhead. This paper presents a localized and hybrid coverage-aware connectivity restoration (CARE) algorithm which opts to rejuvenate lost connected coverage while minimizing recovery overhead. The design philosophy of CARE is based on “caretaker” theory. CARE proactively segregates critical/non-critical nodes, designates appropriate caretaker to each critical node in order to minimize recovery delay and avoid overreacting against non-critical node failure. CARE prefers to nominate a highly connected non-critical neighbor with highest overlapped coverage to minimize the repercussions and scope of recovery. The pre-designated guardian detects the failure and instigates a recovery that may involve controlled and coordinated multi-node relocation. Simulation results confirm the effectiveness and efficiency of CARE compared to contemporary schemes found in the literature.

This position paper introduces a novel wireless sensor and actor network (WSAN) framework for autonomous monitoring and maintenance of pipe and power line (oil, gas, water, electricity) infrastructures in an efficient and cost-effective manner. The main focus is on boosting the availability of lifeline infrastructures through advancements in the WSAN technology. First, we categorize and classify the existing lifeline monitoring systems. Second, we identify the requirements for effective and efficient monitoring and maintenance of lifeline infrastructures. Third, we propose a novel WSAN architecture that combines sensing with distributed decision-making and acting capabilities through advanced robotics. Two operational models for the proposed architecture are also presented. The first is a push-up model that employs low-cost, multi-functional sensors along the lifeline to observe certain phenomena of interest, e.g., leakage, ruptures, clogs, etc., in real time and reports to actors over wireless links. The actors process the received data, coordinate with each other in order to identify the most appropriate response. The second is a pull-down model that capitalizes the resources of elite nodes (i.e. actors) in the network.

Recently, Wireless Sensor and Actor Networks have been receiving a growing attention from the research community because of their suitability for critical applications. Maintaining inter-actor connectivity becomes extremely crucial in such situations where actors have to quickly plan optimal coordinated response to detected events. Failure of critical actor partitions the inter-actor network into disjoint segments, and thus hinders the network operation. Autonomous detection and rapid recovery procedures are highly desirable in such case. This paper presents PCR, a novel distributed partitioning detection and connectivity restoration algorithm. PCR proactively identifies critical actors based on local topological information and designate appropriate backup nodes (preferably non-critical) to handle their failure. A backup actor detects the failure and initiates a recovery process that may involve coordinated multi-actor relocation. The purpose is to avoid procrastination, localize the scope of recovery process and minimize the movement overhead. Simulation results validate the performance of PCR that outperforms contemporary schemes found in literature.