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Network operations involve several decision-making tasks. Some of these tasks are related to operators, such as extending the footprint or upgrading the network capacity. Other decision tasks are related to network functions, such as traffic classifications, scheduling, capacity, coverage trade-offs, and policy enforcement. These decisions are often decentralized, and each network node makes its own decisions based on the preconfigured rules or policies. To ensure effectiveness, it is essential that planning and functional decisions are in harmony. However, human intervention-based decisions are subject to high costs, delays, and mistakes. On the other hand, machine learning has been used in different fields of life to automate decision processes intelligently. Similarly, future intelligent networks are also expected to see an intense use of machine learning and artificial intelligence techniques for functional and operational automation. This article investigates the current state-of-the-art methods for packet scheduling and related decision processes. Furthermore, it proposes a machine learningbased approach for packet scheduling for agile and cost-effective networks to address various issues and challenges. The analysis of the experimental results shows that the proposed deep learning-based approach can successfully address the challenges without compromising the network performance. For example, it has been seen that with mean absolute error from 6.38 to 8.41 using the proposed deep learning model, the packet scheduling can maintain 99.95% throughput, 99.97% delay, and 99.94% jitter, which are much better as compared to the statically configured traffic profiles. [ABSTRACT FROM AUTHOR]

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes. [ABSTRACT FROM AUTHOR]

The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of ML applications in any domain can only be ensured if intelligent capabilities cover all of its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. First, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Second, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/cross-domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned previously be addressed with improved ML architecture and agile operations and propose an ML pipeline based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain, and multi-layer environment and provide decision making on a global or holistic rather than an individual function level. [ABSTRACT FROM AUTHOR]

Conventional underwater technologies were not able to provide authentication and proper visualization of unexplored ocean areas to accommodate a wide range of applications. The aforesaid technologies face several challenges including decentralization, beacon node localization (for identification of nodes), authentication of Internet of Underwater Things (IoUTs) objects and unreliable beacon node communication between purpose oriented IoT-enabled networks. Recently, new technologies such as blockchain (BC) and the IoUTs have been used to reduce the issues but there are still some research gaps; for example, unreliable beacon messages for node acquisition have significant impacts on node identification and localization and many constrained node resources, etc. Further, the uncertainty of acoustic communication and the environment itself become problems when designing a trust-based framework for the IoUTs. In this research, a trust-based hybrid BC-enabled beacon node localization (THBNL) framework is proposed to employ a secure strategy for beacon node localization (BNL) to mine the underwater localized nodes via the hybrid blockchain enabled beacon node localization (HB2NL) algorithm. This framework helps to merge two disciplines; it is hybrid because it follows the nature and bio inspired meta heuristics algorithms for scheduling the beacon nodes. The performance of the proposed approach is also evaluated for different factors such as node losses, packet delivery ratios, residual and energy consumption and waiting time analysis, etc. These findings show that the work done so far has been successful in achieving the required goals while remaining within the system parameters. [ABSTRACT FROM AUTHOR]

With the rapid increase of compute intensive tasks and workload, the need of the hour is to manage the total workload due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in federated cloudlet environment, load balancing is the main challenge. Load balancing means to divide the total workload between all the member nodes to get the maximum benefits from the federated environment. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible to the mobile devices in the vicinity. Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various member cloudlets. Different tasks consume different amount of energy for their execution that results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat, the more performance degradation, so it becomes very vital to keep the energy consumption as minimum as possible. To address this problem, this paper proposes a novel scheduling strategy that will distribute the total load between all the member nodes in a federated cloudlet environment for load balancing. The proposed methodology not only considers the energy level of the system on which the task has to be executed but also considers the energy required by the tasks in queue. The proposed methodology is tested in a Cloudlet Federation environment and the results show an improved load balancing in terms of energy consumption. [ABSTRACT FROM AUTHOR]

The smart vehicles are one of critical enablers for automated services in smart cities to provide intelligent transportation means without human intervention. In order to fulfil requirements, Vehicle-to-Anything(V2X) communications aims to manage massive connectivity and high traffic load on base stations and extend the range over multiple hops in 5G networks. However, V2X networking faces several challenges from dynamic topology caused by high velocity of nodes and routing overhead that degrades the network performance and increases energy consumption. The existing routing scheme for V2X networking lacks energy efficiency and scalability for high velocity nodes with dense distribution. In order to handle the challenges, this article proposes a scalable and energy-efficient routing scheme called Dynamic proactive reactive routing for 5G (DPR5) for high mobility speed and dense environment. As compared to existing schemes it uses a single routing table and efficiently minimizes the energy consumption in dense environment, improves node 's lifetime upto 42%, and optimizes network performance by reducing the packet loss ratio upto 46% in a high velocity dense environment. [ABSTRACT FROM AUTHOR]

Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency. [ABSTRACT FROM AUTHOR]

A topological index is a quantity that is somehow calculated from a graph (molecular structure), which reflects relevant structural features of the underlying molecule. It is, in fact, a numerical value associated with the chemical constitution for the correlation of chemical structures with various physical properties, chemical reactivity, or biological activity. A large number of properties like physicochemical properties, thermodynamic properties, chemical activity, and biological activity can be determined with the help of various topological indices such as atom-bond connectivity indices, Randić index, and geometric arithmetic indices. In this paper, we investigate topological properties of two graphs (commuting and noncommuting) associated with an algebraic structure by determining their Randić index, geometric arithmetic indices, atomic bond connectivity indices, harmonic index, Wiener index, reciprocal complementary Wiener index, Schultz molecular topological index, and Harary index. [ABSTRACT FROM AUTHOR]

In spatiotemporal multi-channel surface electromyogram (EMG) images where the x-axis is time, the y-axis is EMG channels and the gray level is EMG amplitude, the motor unit action potential (MUAP) appears as a linear Gaussian structure. The appearance of this MUAP pattern in the spatiotemporal images is mostly distorted either by the destructive superposition of other MUAPs occurring in the conducting volume or by various noises such as a power line, bad electrode and skin contacts and movement artifacts. For accurate automatic detection of MUAP, EMG image enhancement is needed to suppress the background noises and enhance the line-like MUAP propagation patterns. This study presents several candidate filters to enhance the MUAPs propagation pattern in spatiotemporal EMG images. The filters, which can detect and enhance line-like structure in digital images, are used. Specifically, the Hermite shape filter is used for EMG image enhancement and compared with Gabor filter and steerable filters. The performance of the filters regarding accuracy, specificity, and sensitivity is evaluated with real sEMG signal measured from different muscles and computer-generated EMG signals. In the enhanced images the visibility of the MUAP region is improved. These results can help in better estimation of muscle characteristics from sEMG signals. [ABSTRACT FROM AUTHOR]

• This paper focuses on Distributed Denial of Service attacks by adding the design of an Intrusion Detection Systems for IoV. • Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be investigated enabling refined defense architecture. • A fuzzy logic and Q-learning based solution is tested through simulations to observe the usefulness of the approach. Privacy and trustworthiness are the key apprehensions for the users of Internet of Vehicle (IoV) services. Having multiple components involved in the communication (i.e., sensors, vehicles, humans, and infrastructures), the IoV platforms are exposed to a range of attacks. This manuscript will focus on Distributed Denial of Service (DDOS) attacks by adding the design of an Intrusion Detection Systems (IDS) tailored to IoV systems. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be investigated that can help in making refined defense architecture for countering DDOS attacks in IoV networks. Furthermore, a fuzzy logic and Q-learning based proposed solution is tested through simulations which argues about the usefulness of the proposed approach in comparison with conventional techniques. [ABSTRACT FROM AUTHOR]

Privacy of data is a basic requirement for all types of applications but is especially relevant to online social networks. These systems allow users to share personal data with their friends and thus rightfully desire to have enriched social interactions. Current access control models fulfill basic requirements of individual users but struggle to meet needs of social interactions such as multiparty privacy, politeness and asymmetric, non-reciprocal and intransitive nature of relationships. A personalized access control model, based on the social relations model and the socio-technical design paradigm, is proposed in this paper to satisfy these requirements. The model is formulated through extreme formal modeling and implemented through Facebook APIs. The model's expressiveness is examined for overlapping policies of multiple users, its consistency is analyzed for conflicting and redundant policies and user acceptance testing is performed for validity. The results suggest that the proposed technique outperformed the existing one to secure data in multiparty interactions. [ABSTRACT FROM AUTHOR]

Purpose: This retrospective study presents a comparative analysis of the overall survival and toxicities, as side effects, of docetaxel plus cyclophosphamide (TC) and doxorubicin plus cyclophosphamide (AC). The study measured their efficacies during adjuvant chemotherapy, treating Pakistani breast cancer patients by validating the results obtained, with the published analysis of the same treatment given to US patients. Patients and methods: Between June 2015 and September 2017, for four chemotherapy cycles, 189 patients out of 358 received TC (75 mg/m2 of docetaxel, 600 mg/m2 of cyclophosphamide) and 169 were treated with AC (60 mg/m2 of doxorubicin, 600 mg/m2 of cyclophosphamide). On the basis of using pathological markers to assess patients, toxicities, as side effects, (due to docetaxel, doxorubicin, and cyclophosphamide) were listed in the database of this study. Common factors with respect to common terminology criteria for adverse events version 5.0 and side effects listed in MedlinePlus, NIH US database, and from the database of this study were then separated to be included in comparison for this study. Statistically, chi-squared test was used at α=0.05. Results: There was no statistically significant difference between the proportions of patients with vomiting, extreme tiredness, diarrhea, mild anemia, stability, and overall survival because P-value >0.05. However, AC remained less toxic (P-value <0.05) by 22.6%, 25.7%, 25.3%, 12.4%, 20.8%, and 16.4% compared to TC for changes in taste, muscle pain, burning hands, change in hemoglobin level, moderate anemia, and needing blood transfusion respectively, whereas TC remained less toxic by 52.9%, 32.5%, and 26.3% for dizziness, weight loss, and sores in throat and mouth, respectively. Conclusion: At 27 months, TC was more toxic than AC, whereas both combinations had the same overall survival rate. [ABSTRACT FROM AUTHOR]

The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale. [ABSTRACT FROM AUTHOR]