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The fast expansion of renewable energy sources, rising electricity demand, and the requirement for improved grid dependability have made it necessary to create cutting-edge technologies for electric power systems. The electric power business faces several issues, and deep learning, a branch of machine learning, has emerged as a possible solution. This study offers a thorough analysis of deep learning applications in electric power systems, including load forecasting, fault detection, and diagnosis, assessment of the security and stability of the power system, integration and management of renewable energy sources, and asset management and maintenance of the electric grid. Although deep learning techniques have enormous potential, several issues and constraints must be resolved. These include data quality and availability issues, computational complexity, resource requirements, model interpretability, and integration with current power system tools and infrastructure. Prospects and opportunities in the field are also covered in the study, emphasizing the creation of innovative deep learning algorithms and architectures, scalable and effective computational platforms, multidisciplinary research and collaboration, standardization, and benchmarking. Deep learning has the potential to completely transform the electric power industry by tackling these issues and seizing new opportunities. This would result in improved grid sustainability, resilience, dependability, and more effective use of renewable energy sources and asset management procedures. Researchers, business professionals, and politicians interested in learning more about the state, difficulties, and potential of deep learning applications in electric power systems will find this helpful research paper.

In recent years, the Internet of Things (IoT) has had a big impact on both industry and academia. Its profound impact is particularly felt in the industrial sector, where the Industrial Internet of Things (IIoT), also known as Industry 4.0, is revolutionizing manufacturing and production through the fusion of cutting-edge technologies and network-embedded sensing devices. The IIoT revolutionizes several industries, including crucial ones such as oil and gas, water purification and distribution, energy, and chemicals, by integrating information technology (IT) with industrial control and automation systems. Water, a vital resource for life, is a symbol of the advancement of technology, yet knowledge of potential cyberattacks and their catastrophic effects on water treatment facilities is still insufficient. Even seemingly insignificant errors can have serious consequences, such as aberrant pH values or fluctuations in the concentration of hydrochloric acid (HCI) in water, which can result in fatalities or serious diseases. The water purification and distribution industry has been the target of numerous hostile cyber security attacks, some of which have been identified, revealed, and documented in this paper. Our goal is to understand the range of security threats that are present in this industry. Through the lens of IIoT, the survey provides a technical investigation that covers attack models, actual cases of cyber intrusions in the water sector, a range of security difficulties encountered, and preventative security solutions. We also explore upcoming perspectives, illuminating the predicted advancements and orientations in this dynamic subject. For industrial practitioners and aspiring scholars alike, our work is a useful, enlightening, and current resource. We want to promote a thorough grasp of the cybersecurity landscape in the water industry by combining key insights and igniting group efforts toward a safe and dependable digital future.

Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

One of the most important and recurring tasks in managing a store is to provide accurate, up-to-date price information to customers on the shelves. Manual updating of price tags has been a time-consuming, error-prone task with high labor costs. An Electronic shelf labels (ESL) system is becoming an attractive alternative for this task because of the dynamic-price-updating and customer's product-evaluation-display features. A common ESL system configuration in a retail store includes thousands of battery-powered ESL tags that are mostly connected wirelessly in a dense indoor environment. Raising the success ratio of wireless communication is essential for the system's viability due to its limited battery life. Most of the ESL traffic is the image data of goods that appear on the tags, and reducing the amount of the data is one of the most effective ways to enhance communication performance and reduce retransmission. This paper proposes an ESL image compression mechanism based on chain coding that utilizes ESL images' characteristics. The performance results show that the proposed mechanism could compress the ESL images smaller and decompress faster.

In software development, the main problem is recognizing the security-oriented issues within the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on customer and software datasets. The misclassification of bug reports has a direct impact on the effectiveness of the bug prediction model. The misclassification issue surely compromises the accuracy of the system. Manually reviewing bug reports is necessary to solve this problem, but doing so takes a lot of time and is tiresome for developers and testers. This paper proposes a novel hybrid approach based on natural language processing (NLP) and machine learning. To address these issues, the intended outcomes are multi-class supervised classification and bug prioritization using supervised classifiers. After being collected, the dataset was prepared for vectorization, subjected to exploratory data analysis, and preprocessed. The feature extraction and selection methods used for a bag of words are TF-IDF and word2vec. Machine learning models are created after the dataset has undergone a full transformation. This study proposes, develops, and assesses four classifiers: multinomial Naive Bayes, decision tree, logistic regression, and random forest. The hyper-parameters of the models are tuned, and it is concluded that random forest outperformed with a 91.73% test and 100% training accuracy. The SMOTE technique was used to balance the highly imbalanced dataset, which was initially created for the justified classification. The comparison between balanced and imbalanced dataset models clearly showed the importance of the balanced dataset in classification as it outperformed in all experiments.

Objective: Prediction of delivery latency complicated with preterm premature rupture of membrane (PPROM) is crucial for reducing maternal and neonatal complications. Therefore, we investigated the correlations between latency period and cut-off values of ultrasonographic parameters, ultimately predicting delivery latency. Materials and methods: The retrospective study was performed on 121 PPROM patients enrolled between March 2010 and July 2015. Parameters including amniotic fluid index (AFI), single deepest pocket (SDP) and transvaginal cervical length (TVCL) were measured in 99 singleton pregnancies with PPROM. Latency was defined as the period from sonographic measurements to delivery day. The parameters were analyzed independently by Wilcoxon rank sum test and Fisher's exact test. Cut-off values were determined using a receiver operating characteristic (ROC) curve. Results: In delivery latency within 3 days, AFI and SDP were decreased with significantly shorter TVCL. AFI and SDP had the highest sensitivity (82.2%) and SDP combined with TVCL showed the highest specificity (75.9%) in area under curve (AUC) value. The predicted median latency period was less than 2 days within the cutoff value of parameter (AFI ≤ 7.72, SDP ≤ 3.2 and TVCL ≤ 1.69). Conclusion: AFI and SDP combined with TVCL could be useful predictive parameters of the latency interval from PPROM to delivery.

Modern Supervisory Control and Data Acquisition (SCADA) systems are essential for monitoring and managing electric power generation, transmission and distribution. In the age of the Internet of Things, SCADA has evolved into big, complex and distributed systems that are prone to be conventional in addition to new threats. Many security methods can be applied to such systems, having in mind that both high efficiency, real time intrusion identification and low overhead are required. Keywords: SCADA systems, Security

Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.

An IPv6 routing protocol for low power and lossy networks provides an IPv6 communication for a wide range of applications in multi-hop mesh networks. The routing protocol for low power and lossy networks defines the creation and management of downward routes with two modes of operations: storing and non-storing modes. The storing and non-storing modes have weaknesses for memory constraints and packet traffic overheads, respectively. The storing mode may cause routing failures due to constraints on memory in routers and the non-storing mode may cause packet fragmentation that can become a factor for packet delays or loss. Then the problems may degrade the downward route performance in routing protocol for low power and lossy networks. Therefore, in this article, we propose a hybrid mode that combines the advantages of the existing two modes to improve the performance of downward packet transmission in routing protocol for low power and lossy networks networks. The proposed hybrid mode uses a new routing header format. The routing information for packet delivery is distributed with the extended routing header. We implement the proposed hybrid mode in Contiki OS environment to compare with existing techniques. From the experiment, it was observed that the proposed hybrid mode can improve the performance of downward packet transmission. Therefore, with the proposed hybrid mode, it is possible to configure a network enable to be composed of many leaf nodes with constrained memory. We also discuss future works.

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone to detect a senior citizen falling in real time and to use the communication capabilities of the smartphone to notify the administrator of such an event. The proposed system conducts real-time location tracking using Google’s Map and 3D information to cope with a critical situation and to make an urgent intervention in an emergency. Since Google’s 3D mapping service can provide information on the surrounding buildings and their shapes with more precision than a 2D service or text-based service, it is possible to ensure a prompt response for senior citizens when such location information is available. To verify the validity and efficiency of this article, we measured the change in acceleration sensor value by classifying the subjects by age group and gender. As a result, we found that the signal vector magnitude value of the acceleration sensor value variation showed a great difference in daily life such as walking, running, sitting, and falling.

Background: Transcriptional co-activator with PDZ-binding motif (TAZ), a downstream effector of the Hippo pathway, has been reported to regulate organ size, tissue homeostasis, and tumorigenesis by acting as a transcriptional co-activator. Lysophosphatidic acid (LPA) is a bioactive lipid implicated in tumorigenesis and metastasis of ovarian cancer through activation of G protein-coupled receptors. However, the involvement of TAZ in LPA-induced tumorigenesis of ovarian cancer has not been elucidated. Methods: In order to demonstrate the role of TAZ in LPA-stimulated tumorigenesis, the effects of LPA on TAZ expression and cell migration were determined by Western blotting and chemotaxis analyses in R182 human epithelial ovarian cancer cells. Results and Conclusion: Treatment of R182 cells with the LPA receptor inhibitor Ki16425 blocked LPA-induced cell migration. In addition, transfection of R182 cells with small interfering RNA specific for LPA receptor 1 resulted in abrogation of LPA-stimulated cell migration. LPA induced phosphorylation of ERK and p38 MAP kinase in R182 cells and pretreatment of cells with the MEK-ERK pathway inhibitor U0126, but not the p38 MAPK inhibitor SB202190, resulted in abrogation of LPA-induced cell migration. Pretreatment of R182 cells with U0126 attenuated LPA-induced mRNA levels of TAZ and its transcriptional target genes, such as CTGF and CYR61, without affecting phosphorylation level of YAP. These results suggest that MEK-ERK pathway plays a key role in LPA-induced cell migration and mRNA expression of TAZ in R182 cells, without affecting stability of TAZ protein. In addition, small interfering RNA-mediated silencing of TAZ expression attenuated LPA-stimulated migration of R182 cells. These results suggest that TAZ plays a key role in LPA-stimulated migration of epithelial ovarian cancer cells.

Reliable source to sink communication is the most important factor for an efficient routing protocol especially in domains of military, healthcare and disaster recovery applications. We present weighted energy aware multipath reliable routing (WEAMR), a novel energy aware multipath routing protocol which utilizes hotline-assisted routing to meet such requirements for mission critical applications. The protocol reduces the number of average hops from source to destination and provides unmatched reliability as compared to well known reactive ad hoc protocols i.e., AODV and AOMDV. Our protocol makes efficient use of network paths based on weighted cost calculation and intelligently selects the best possible paths for data transmissions. The path cost calculation considers end to end number of hops, latency and minimum energy node value in the path. In case of path failure path recalculation is done efficiently with minimum latency and control packets overhead. Our evaluation shows that our proposal provides better end-to-end delivery with less routing overhead and higher packet delivery success ratio compared to AODV and AOMDV. The use of multipath also increases overall life time of WSN network using optimum energy available paths between sender and receiver in WDNs.

Wireless sensor grids form a special class of sensor networks that find more applicability than their randomly deployed counterparts in many commercial applications due to innate regularity in their design and operation. In contemporary wireless sensor grids, primarily single gateway sensor grids, each node senses the information and follows a relay relationship with gateway forming a multisource single receiver paradigm and pursues an added energy consumption pattern that serves as a premier bottleneck in wireless sensor grid environment. This research focuses on statistically modeling the energy consumption of single gateway sensor grids in terms of network availability by analyzing different energy consumption phenomena with communication as a grid-wide routing problem. On the basis of our findings and results in conjunction with Moor's law of semiconductors and batteries, we propose a linear programming heuristic for multiple gateway deployment to reduce network survivability costs and optimize communication for wireless sensor grids. We then present a case study using the proposed heuristic that is augmented through a node scheduler for nearest gateway connectivity, collision avoidance, and fairness, to show the performance gain that presents an ultimate cut-through energy paradigm.

Time Slotted Channel Hopping (TSCH) is widely used in the industrial wireless sensor networks due to its high reliability and energy efficiency. Various timeslot and channel scheduling schemes have been proposed for achieving high reliability and energy efficiency for TSCH networks. Recently proposed autonomous scheduling schemes provide flexible timeslot scheduling based on the routing topology, but do not take into account the network traffic and packet forwarding delays. In this paper, we propose an autonomous scheduling scheme for convergecast in TSCH networks with RPL as a routing protocol, named Escalator. Escalator generates a consecutive timeslot schedule along the packet forwarding path to minimize the packet transmission delay. The schedule is generated autonomously by utilizing only the local routing topology information without any additional signaling with other nodes. The generated schedule is guaranteed to be conflict-free, in that all nodes in the network could transmit packets to the sink in every slotframe cycle. We implement Escalator and evaluate its performance with existing autonomous scheduling schemes through a testbed and simulation. Experimental results show that the proposed Escalator has lower end-to-end delay and higher packet delivery ratio compared to the existing schemes regardless of the network topology.

An extremely reliable source to sink communication is required for most of the contemporary WSN applications especially pertaining to military, healthcare and disaster-recovery. However, due to their intrinsic energy, bandwidth and computational constraints, Wireless Sensor Networks (WSNs) encounter several challenges in reliable source to sink communication. In this paper, we present a novel reliable topology that uses reliable hotlines between sensor gateways to boost the reliability of end-to-end transmissions. This reliable and efficient routing alternative reduces the number of average hops from source to the sink. We prove, with the help of analytical evaluation, that communication using hotlines is considerably more reliable than traditional WSN routing. We use reliability theory to analyze the cost and benefit of adding gateway nodes to a backbone-assisted WSN. However, in hotline assisted routing some scenarios where source and the sink are just a couple of hops away might bring more latency, therefore, we present a Signature Based Routing (SBR) scheme. SBR enables the gateways to make intelligent routing decisions, based upon the derived signature, hence providing lesser end-to-end delay between source to the sink communication. Finally, we evaluate our proposed hotline based topology with the help of a simulation tool and show that the proposed topology provides manifold increase in end-to-end reliability.

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework.

Mobility in 6LoWPAN (IPv6 over Low Power Personal Area Networks) is being utilized in realizing many applications where sensor nodes, while moving, sense and transmit the gathered data to a monitoring server. By employing IEEE802.15.4 as a baseline for the link layer technology, 6LoWPAN implies low data rate and low power consumption with periodic sleep and wakeups for sensor nodes, without requiring them to incorporate complex hardware. Also enabling sensor nodes with IPv6 ensures that the sensor data can be accessed anytime and anywhere from the world. Several existing mobility-related schemes like HMIPv6, MIPv6, HAWAII, and Cellular IP require active participation of mobile nodes in the mobility signaling, thus leading to the mobility-related changes in the protocol stack of mobile nodes. In this paper, we present LoWMob, which is a network-based mobility scheme for mobile 6LoWPAN nodes in which the mobility of 6LoWPAN nodes is handled at the network-side. LoWMob ensures multi-hop communication between gateways and mobile nodes with the help of the static nodes within a 6LoWPAN. In order to reduce the signaling overhead of static nodes for supporting mobile nodes, LoWMob proposes a mobility support packet format at the adaptation layer of 6LoWPAN. Also we present a distributed version of LoWMob, named as DLoWMob (or Distributed LoWMob), which employs Mobility Support Points (MSPs) to distribute the traffic concentration at the gateways and to optimize the multi-hop routing path between source and destination nodes in a 6LoWPAN. Moreover, we have also discussed the security considerations for our proposed mobility schemes. The performance of our proposed schemes is evaluated in terms of mobility signaling costs, end-to-end delay, and packet success ratio.

Diagnosing the deployed network efficiency and anomaly detection, which is an important research issue in traditional networking systems, has not been carefully addressed in industrial wireless sensor networks. Although recent wireless systems for industrial automation such as ISA100.11a employ device management protocols, these protocols generate and report a large amount of status information from individual sensor nodes. Also, these protocols do not capture influences on network performance from external sources such as malicious nodes or interference from other networks. We propose a latent network diagnosis system (LaNDS) for industrial sensor networks. LaNDS employs a packet sniffing method for efficiently evaluating network performance and instantly identifying degradation causes of networking performance. LaNDS adopts an efficient network evaluation approach for detecting abnormalities from both internal and external causes. In our proposed monitoring scenario, special sniffer devices having M2M capability (WiMAX interface) are used to monitor the industrial sensor network by employing ethical sniffing. Our approach does not incur additional traffic overhead for collecting desired information. For evaluation, we have tested LaNDS locally on an ISA100.11a based sensor network in a lab environment and have validated the efficiency of the system based on the possible erroneous cases of industrial sensor network.

With the rapid growth of information technologies, mobile devices have been utilized in a variety of services such as e-commerce. When a remote server provides such e-commerce services to a user, it must verify the legitimacy of the user over an insecure communication channel. Therefore, remote user authentication has been widely deployed to verify the legitimacy of remote user login requests using mobile devices like smart cards. In this paper we propose a smart card-based authentication scheme that provides both user anonymity and mutual authentication between a remote server and a user. The proposed authentication scheme is a simple and efficient system applicable to the limited resource and low computing performance of the smart card. The proposed scheme provides not only resilience to potential attacks in the smart card-based authentication scheme, but also secure authentication functions. A smart card performs a simple one-way hash function, the operations of exclusive-or and concatenation in the authentication phase of the proposed scheme. The proposed scheme also provides user anonymity using a dynamic identity and key agreement, and secure password change.

Transforming growth factor-β1 (TGF-β1) induces the differentiation of human adipose tissue-derived mesenchymal stem cells (hASCs) into smooth muscle cells. Lipid rafts are cholesterol-rich microdomains in cell membranes that reportedly play a key role in receptor-mediated signal transduction and cellular responses. In order to clarify whether lipid rafts are involved in TGF-β1-induced differentiation of hASCs into smooth muscle cells, we analyzed the lipid raft proteome of hASCs.Pretreatment of hASCs with the lipid raft disruptor methyl-β-cyclodextrin abrogated TGF-β1-induced expression of α-smooth muscle actin, a smooth muscle cell marker, suggesting a pivotal role of lipid rafts in TGF-β1-induced differentiation of hASCs to smooth muscle cells. Sucrose density gradient centrifugation along with a shotgun proteomic strategy using liquid chromatography-tandem mass spectrometry identified 1002 individual proteins as the lipid raft proteome, and 242 of these were induced by TGF-β1 treatment. ADAM12, a disintegrin and metalloproteases family member, was identified as the most highly up-regulated protein in response to TGF-β1 treatment. TGF-β1 treatment of hASCs stimulated the production of both ADAM12 protein and mRNA. Silencing of endogenous ADAM12 expression using lentiviral small hairpin RNA or small interfering RNA abrogated the TGF-β1-induced differentiation of hASCs into smooth muscle cells.These results suggest a pivotal role for lipid raft-associated ADAM12 in the TGF-β1-induced differentiation of hASCs into smooth muscle cells.