Search

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]

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]

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]

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]

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]

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]