Journal of Interconnection Networks

This paper performed some exploratory data visualization on this data set. The nature and representation of input data was found out and the preliminary feature selection was conducted in this step. And this paper performed data preprocessing and feature engineering on this data set, which had critical importance of the accuracy of prediction results. The paper built multiple regression models on the missing values prediction in the testing set. The paper implemented various data mining algorithms to build predictive models, including Gaussian Naive Bayes classifier, K-Nearest Neighbors (K-NN) algorithm, Multi-layer Perceptron (MLP), Logistic regression, random forest and XGBoost. After the experiments, XGBoost classifier could give the best result among all the models.

MIMO is a type of antenna technology which utilizes multiple antennas to separate signals traveling in various paths as a result of reflections, etc., to be separated and their ability utilized to enhance the throughput of data and the signal to noise ratio which in return enhances the performance of the system. For providing enhanced signal performance and enhanced data rates, MIMO is utilized within LTE. When the quantities of antennas are increased, there is increase in the probability that deep fading is experienced by at least some antennas, which affects the overall efficiency of the MIMO system. To handle these issues, a reliable technique has been presented that involves selection of antenna subset. The proposed technique incorporates combination of SBO and PSO for antenna selection. Antenna selection’s main concept is to utilize a bounded quantity of analog chains that are adaptively switched to subset of the available antennas capable of preserving the selection diversity gains and also can minimize the quantity of radio frequency chains needed. The maximum channel capacity of the channel has been considered as the objective function for selecting optimal antennas. The comparison of the proposed approach’s performance and the existing approaches’ performance is done. From the simulation result, it has been shown that the presented approach’s performance has been better than the performance of the existing approaches in terms of BER, energy efficiency, spectral efficiency and optimal transmit power.

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures.

Wireless network virtualization (WNV) is an evolving technology that supports networking for future wireless networks and agile IoT applications that optimize wireless bandwidth, provide network protection, and increase network performance in general. However, the exponential growth and increased demand for IoT have posed considerable scalability problems, interoperability, and heterogeneity across diverse fields from intelligent cities to healthcare, urban computing, and tactile fast internet growth. In this paper, IoT-based WNV (IoT-WNV) technology has been used. The leading technologies that create a scalable and stable network, consistent with many IoT products, for effective wireless virtualization in next-generation agile IoT systems are the networking function virtualization (NFV), mobile edge computing, and software-defined networks (SDNs). The network function virtualization (NFV) feature improves network stability, while the SDN operates, optimizes, and configures network services dynamically, enabling dynamically-based network functionality and interconnectivity. This special problem involves virtualizing IoT system wireless sensor networks to speed up network resource management by analyzing data obtained from devices deployed in various applications. This technological combination will satisfy the demands of modern applications. IoT-WNV aims to improve wireless virtualization for the next-generation network by implementing human-smart technology for load balance, network management, and network security.

Let ${\mathrm{\text{Oct}}}_1^{+}$ and ${\mathrm{\text{Oct}}}_2^{+}$ be the planar and non-planar graphs, respectively, obtained from the Octahedron by 3-splitting a vertex. For ${\mathrm{\text{Oct}}}_1^{+}$, we prove that if a 4-connected graph is ${\mathrm{\text{Oct}}}_1^{+}$-minor-free, then it is $C_6^2$, $C_{2k+1}^2$ $(k\ge 2)$ or it is obtained from $C_5^2$ by repeatedly 4-splitting vertices. We also show that a planar graph is ${\mathrm{\text{Oct}}}_1^{+}$-minor-free if and only if it is constructed by repeatedly taking 0-, 1-, 2-sums starting from $\{K_1,K_2,K_3\}\cup \kappa \cup \{Octahedron,L_5\}$, where $\kappa$ is the set of graphs obtained by repeatedly taking the special 3-sums of $K_4$ and $L_5$ is the graph obtained from two 5-cycles $a_1{a}_2\dots a_5{a}_1$, $b_1{b}_2\dots b_5{b}_1$ by adding the five edges $a_i{b}_i$ for all $1\le i\le 5$. For ${\mathrm{\text{Oct}}}_2^{+}$, we prove that if a 4-connected graph is ${\mathrm{\text{Oct}}}_2^{+}$-minor-free, then it is planar, $C_{2k+1}^2$ $(k\ge 2)$, the line graph of $K_{3,3}$ or it is obtained from $C_5^2$ by repeatedly 4-splitting vertices.

Software-Defined Networking disassociates the control plane from data plane. The problem of deciding upon the number and locations of controllers and assigning switches to them has attracted the attention of researchers. Foreseeing the possibility of failure of a controller, a backup controller has to be maintained for each switch so that the switches assigned to the failed controller can immediately be connected to their backup controllers. Hence, the switches cannot experience disconnections in case of failure of their controller. In this paper, two mathematical models are proposed. The first model focuses on minimizing the average of latencies from all switches to their backup controllers while considering the failure of the controllers. The second model aims at minimizing both the average and worst-case of latencies from all switches to the corresponding backup controllers. Both of our models are evaluated on three networks and are compared (in terms of two metrics, viz., average and worst-case latencies) with an existing model that focuses on minimizing only worst-case latency. The first model gives better average latency compared to the reference model. The second model also gives better average latency and almost equal worst-case latency compared to the reference model.

Localization through received signal strength (RSS) has attained a lot of interest across industries and research organization due to ease of use, high efficiency and low computation complexity; thus, it is widely used in Wireless Sensor Networks (WSNs)-based applications. Existing localization model has been predominantly designed with known transmit power. Recently, few localization approaches have been modeled considering unknown transmit power employing non-convex least squared relative error (LSRE) measurement model. The LSRE optimization problem is solved through semidefinite programming (SDP) using semidefinite relaxation (SDR). However, LSRE-SDP suffers immensely under highly dynamic and noisy environment and induces high computation overhead in meeting convergence. In addressing the aforementioned problem, this paper presents Dynamic Noisy Measurement Aware Localization (DNMAL) model for WSNs using improved least square bounding model. The objective DNMAL is to measure target position by neglecting the collected through noisy (faulty) sensor device. The DNMAL aids in achieving optimal solution using improved least square bounding model through iterative process. The DNMAL is efficient in bounding unknown distribution because of presence of noisy sensor and significantly reduces localization error even with presence of extreme noise.