Consumer behavior in retail stores gives rise to product graphs based on copurchasing or co-viewing behavior. These product graphs can be analyzed using the known methods of graph analysis. In this paper, we analyze the product graph at Target Corporation based on the Erd˝os-Renyi random graph model. In particular, we compute clustering coefficients of actual and random graphs, and we find that the clustering coefficients of actual graphs are much higher than random graphs. We conduct the analysis on the entire set of products and also on a per category basis and find interesting results. We also compute the degree distribution and we find that the degree distribution is a power law as expected from real world networks, contrasting with the ER random graph.

A clique in a graph is a set of vertices that are all connected to each other. A maximum clique is a clique of maximum size. A graph may have more than one maximum cliques. The problem of finding a maximum clique is a strongly hard NP-hard problem. It is not possible to find an approximation algorithm which finds a maximum clique that is a constant factor of the optimum solution. In this work, we present a genetic algorithm for the maximum clique problem that is able to find optimum or close to optimum solutions to most DIMACS graphs. The genetic algorithm uses new crossover mechanisms which are able to find reasonably good cliques which can then be used in other applications downstream. We also provide C++ code for our algorithm. Results show that our algorithm is able to find maximum cliques for most DIMACS instances, and if not, close to optimum solutions for the other instances.

A clique in a graph is a set of vertices that are all directly connected to each other i.e. a complete sub-graph. A clique of the largest size is called a maximum clique. Finding the maximum clique in a graph is an NP-hard problem and it cannot be solved by an approximation algorithm that returns a solution within a constant factor of the optimum. In this work, we present a simple and very fast randomized algorithm for the maximum clique problem. We also provide Java code of the algorithm in our git repository. Results show that the algorithm is able to find reasonably good solutions to some randomly chosen DIMACS benchmark graphs. Rather than aiming for optimality, we aim to find good solutions very fast.

In the fifth-generation communication system, secured and reliable data packets will rely on the network with high availability and low latency. The 5G network enables the dynamic control of nodes with low latency. Ultra-reliability is one of the challenging tasks in software-defined networks. The ultra-reliability feature is an interface working with high availability and low latency that brings in fifth-generation networks. The Internet of Things will work in the societies so that it required ultra-reliability features to empower the peoples as well as machines for collaborating with their neSoftware Defined Networksighbors. The connections among ultra-reliability networks are extremely hard situations to keep low packets corruption. The Software Defined Networks (SDN) will provide a platform for measuring, controlling, monitoring and scaling the smart objects in reality or virtually. Virtualization enables IT, specialists, to combine or separate the considerable networking or create new programmable networking among virtual machines. In this article, the authors are reviewed the architecture of SDN based on virtualization under the umbrella of 5G networks.

With the inevitable dominance of video traffic on the Internet, providing perceptually good video quality is becoming a challenging task. This is partly due to the bursty nature of video traffic, changing network conditions and limitations of network transport protocols. This growth of video traffic has made Quality of Experience (QoE) of the end user the focus of the research community. In contrast, Internet service providers are concerned about maximizing revenue by accepting as many sessions as possible, as long as customers remain satisfied. However, there is still no entirely satisfactory admission algorithm for flows with variable rate. The trade-off between the number of sessions and perceived QoE can be optimized by exploiting the bursty nature of video traffic. This paper proposes a novel algorithm to determine the upper limit of the aggregate video rate that can exceed the available bandwidth without degrading the QoE of accepted video sessions. A parameter $\beta$ that defines the exceedable limit is defined. The proposed algorithm results in accepting more sessions without compromising the QoE of on-going video sessions. Thus it contributes to the optimization of the QoE-Session trade-off in support of the expected growth of video traffic on the Internet.

The Internet of Things (IoT) devices have capabilities to interact and communicate in 5G heterogeneous networks. The IoT devices also have capabilities to form a network with neighborhood devices without a centralized approach. This network is called the mobile ad hoc network (MANET). Through an infrastructure-less system of the Internet of Things environment, the MANET enables IoT nodes to interact with one another. Those IoT nodes could interactively connect, communicate as well as share knowledge between several nodes. The role of cloud throughout this structure would be to store as well as interpret information through IoT nodes. The communication security has also been introduced to be one of the techniques in which trying to solve the data transmission security issue that could result in the performance increase in cloud consumption and ubiquity. Our purpose in this research would be to establish a communication system among IoT nodes in such an embedded Cloud and MANET structure. The main goal of this research is to create an efficient and secure approach for communication in Cloud-MANET-IoT integrated framework. This approach is implemented and tested.

In the next generation of computing, Mobile ad-hoc network (MANET) will play a very important role in the Internet of Things (IoT). The MANET is a kind of wireless networks that are self-organizing and auto connected in a decentralized system. Every device in MANET can be moved freely from one location to another in any direction. They can create a network with their neighbors’ smart devices and forward data to another device. The IoT-Cloud-MANET framework of smart devices is composed of IoT, cloud computing, and MANET. This framework can access and deliver cloud services to the MANET users through their smart devices in the IoT framework where all computations, data handling, and resource management are performed. The smart devices can move from one location to another within the range of the MANET network. Various MANETs can connect to the same cloud, they can use cloud service in a real time. For connecting the smart device of MANET to cloud needs integration with mobile apps. My main contribution in this research links a new methodology for providing secure communication on the internet of smart devices using MANET Concept in 5G. The research methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.

Blockchain (BC) in the Internet of Things (IoT) is a novel technology that acts with decentralized, distributed, public and real-time ledger to store transactions among IoT nodes. A blockchain is a series of blocks, each block is linked to its previous blocks. Every block has the cryptographic hash code, previous block hash, and its data. The transactions in BC are the basic units that are used to transfer data between IoT nodes. The IoT nodes are different kind of physical but smart devices with embedded sensors, actuators, programs and able to communicate with other IoT nodes. The role of BC in IoT is to provide a procedure to process secured records of data through IoT nodes. BC is a secured technology that can be used publicly and openly. IoT requires this kind of technology to allow secure communication among IoT nodes in heterogeneous environment. The transactions in BC could be traced and explored through anyone who are authenticated to communicate within the IoT. The BC in IoT may help to improve the communication security. In this paper, I explored this approach, its opportunities and challenges.

The internet of smart devices is a network of intelligent gadgets with sensors, programs, Wi-Fi and communication network connections. These devices store the data in cloud and process data outside the device using the proposed Cloud-Internet communication framework. These devices can communicate with other devices using the proposed framework. However, there are many challenges for communication security among the internet of smart devices. The Cloud can store the device data with security, reliability, privacy and service availability. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud computing. Our purpose of this study is to create a framework for providing the communication security among smart devices network for the internet of things using cloud computing. Our main contribution links a new study for providing communication security for the internet of smart devices using the cloud-Internet framework. This study can be helpful for communication security problem in the framework of the Internet of Things. The proposed study generates a new framework for solving the issue of communication security among internet of smart devices.

Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communicate with another SD through any medium and protocol. The communication among intelligent physical things is a challenging task to exchange information that guaranteed to reach to the destination completely in a real time with the same order as sending without corruption. The reliable communication between physical things can be built in the transmission control protocol (TCP) layers. In TCP layer, the reliable communication is required the error detection, correction and confirmation to exchange information among smart devices. In this paper, the author represents a framework to deal with reliability issues to enable the adoption of IoT devices. The results found the improvement in reliability.

In this paper, a Hewlett-Packard (HP) memristor model with a new window function and its versatile characteristics are presented. SPICE behaviors of the linear and nonlinear memristor model are studied through PSpice simulation. High flexibility is demonstrated for emulating the behaviors of the practical HP memristors. Furthermore, the characteristics of the composite SPICE behaviors are both investigated when two memristors are connected in series and in parallel. The polarity of each memristor is also taken into consideration. The relationships among flux, charge, voltage, current, and memristance of the double memristor circuits are simulated and analyzed.

The fog computing is the emerging technology to compute, store, control and connecting smart devices with each other using cloud computing. The Internet of Things (IoT) is an architecture of uniquely identified interrelated physical things, these physical things are able to communicate with each other and can transmit and receive information. This research presents a framework of the combination of the Internet of Things (IoT) and Fog computing. The blockchain is also the emerging technology that provides a hyper, distributed, public, authentic ledger to record the transactions. Blockchains technology is a secured technology that can be a boon for the next generation computing. The combination of fog, blockchains, and IoT creates a new opportunity in this area. In this research, the author presents a middleware framework based on the blockchain, fog, and IoT. The framework is implemented and tested. The results are found positive.

This paper reports the findings of a study of an optoelectronic oscillator (OEO) with large delay under proportional and proportional integral control by a phase-locked loop (PLL). The study is the first to fully account for the OEO delay in responding to a tuning stimulus including the location of all the countable infinity of poles of the system function.

A novel rectifying metasurface presented that features high efficiency at low power applicable to wireless power transfer and energy harvesting. The proposed rectifying metasurface is a two-sided structure consisting of modified electric inductive-capacitive unit cells on one side and a rectifying circuitry on the other, designed to resonate and rectify at 2.45 GHz. Each unit cell is directly connected to a voltage doubler rectifier, allowing the incident microwaves to be directly rectified upon reception. The impedance characteristics and rectification performance of the proposed rectifying metasurface are tested via numerical simulation and measurement, respectively. The measured results demonstrate the overall conversion efficiency electromagnetic power absorption + rectification) of 75% at 0.4 dBm incident power.

Playing games always been interest in child and adolescent. Technology specially internet given new dimension to gaming and over the decade gaming over internet attracted major percentage of player. Excessive gaming when not controlled also lead to the behavioral dysfunction along with physiological issues. World Health Organization (WHO) has added Internet Gaming Disorder (IGD) as potential mental disorder recently in Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as research field to include in main manual. Research in this field gained interest, but about diagnostic criteria debate is still on in research group. For diagnosis of IGD, not only subjective symptoms but its underlying neurobiology needs to be included. Major behavioral addiction shares its symptoms with different addiction, which can be seen into IGD and Alcohol Use Disorder (AUD) also. In this paper, we aim to identify potential biomarkers to explore relationships between AUD and IGD symptoms using EEG signals recordings. Noise and other artifacts are removed during pre-processing and bands splitted in intermediate steps of proposed method. Spectral features are potential indicator in EEG data, for all five EEG bands power spectral density is computed and visible difference found in mean power on most of the channels for all bands. Across theta and beta bands, mean absolute power is greatly reduced in the frontal and prefrontal brain region. The results indicate symptoms are matching with impulsive behavior. Based on statistical analysis across we found similarities in EEG features in terms of emotional imbalance, high arousal, slow response inhibition and our study indicate Alcohol Use Disorder and IGD are correlated.

This study analyzed highly-correlated, feature-rich datasets from hyperspectral remote sensing data using multiple machine and statistical-learning methods. The effect of filter-based feature-selection methods on predictive performance was compared. Also, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%) was modeled as a function of reflectance, and variable importance was assessed using permutation-based feature importance. Overall support vector machine (SVM) outperformed others such as random forest (RF), extreme gradient boosting (XGBoost), lasso (L1) and ridge (L2) regression by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than the unfiltered feature sets, while ensemble filters did not have a substantial impact on performance. Permutation-based feature importance estimated features around the red edge to be most important for the models. However, the presence of features in the near-infrared region (800 nm - 1000 nm) was essential to achieve the best performances. More training data and replication in similar benchmarking studies is needed for more generalizable conclusions. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies.

COVID-19 has emerged as a highly contagious disease which has caused a devastating impact across the world with a very large number of infections and deaths. Timely and accurate testing is paramount to an effective response to this pandemic as it helps identify infections and therefore mitigate (isolate/cure) them. In this paper, we investigate this challenge and contribute by presenting a blockchain-based solution that incorporates self-sovereign identity, re-encryption proxies, and decentralized storage, such as the interplanetary file systems (IPFS). Our solution implements digital medical passports (DMP) and immunity certificates for COVID-19 test-takers. We present smart contracts based on the Ethereum blockchain written and tested successfully to maintain a digital medical identity for test-takers that help in a prompt trusted response directly by the relevant medical authorities. We reduce the response time of the medical facilities, alleviate the spread of false information by using immutable trusted blockchain, and curb the spread of the disease through DMP. We present a detailed description of the system design, development, and evaluation (cost and security analysis) for the proposed solution. Since our code leverages the use of the on-chain events, the cost of our design is almost negligible. We have made our smart contract codes publicly available on Github.

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. In this paper, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user c omfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.