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Privacy and security of information are the prime concerns of today’s internet users. They want to get better and reliable services without sacrificing privacy from the current paradigm like the Internet of Things (IoT). In order to ensure IoT as a truthful service platform for the vast users, we need to protect the information that evolves throughout the internet and in the storage. In this paper, we propose a steganographic method for Bangla text communication over the unsecured internet based on RGB color image with the help of secret key for better protection of information. The proposed system consists of four main components namely preprocessing, embedding, extraction, and post-processing. We use dynamic positioning in case of substitution of (Bangla) text into the image. The secret key is transmitting throughput the image so that there is no extra hassle for communicating the secret key. We justify our proposed approach by using simulation with respect to imperceptibility, capacity, and robustness. We compare the result of the proposed method with other existing methods and get a better result over several existing efficient methods.

The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network.

There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good results. Convolutional neural networks (CNNs) are employed in real-time applications to achieve two goals: high accuracy and speed. However, identifying drowsiness at an early stage significantly improves the chances of being saved from accidents. Drowsiness detection can be automated by using the potential of artificial intelligence (AI), which allows us to assess more cases in less time and with a lower cost. With the help of modern deep learning (DL) and digital image processing (DIP) techniques, in this paper, we suggest a CNN model for eye state categorization, and we tested it on three CNN models (VGG16, VGG19, and 4D). A novel CNN model named the 4D model was designed to detect drowsiness based on eye state. The MRL Eye dataset was used to train the model. When trained with training samples from the same dataset, the 4D model performed very well (around 97.53% accuracy for predicting the eye state in the test dataset). The 4D model outperformed the performance of two other pretrained models (VGG16, VGG19). This paper explains how to create a complete drowsiness detection system that predicts the state of a driver’s eyes to further determine the driver’s drowsy state and alerts the driver before any severe threats to road safety.

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%., This work was submitted to the IEEE Transactions on Wireless Communications

In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.

Skin cancer is one of the worst diseases noticed in humankind. It beholds some types, which even experts find challenging to categorize. In recent times, neural network-based automated systems have been entitled to perform this difficult task for their amazing ability of pattern recognition. However, the challenge remains due to the requirement for high-quality images and thus the necessity of highly configured resources. In this research manuscript, the authors have addressed these issues. They pushed the boundary of neural networks by utilizing a low-resolution (80 × 80, 64 × 64, and 32 × 32 pixels), highly imbalanced, grayscale HAM10000 skin cancer dataset into several pre-trained network architectures (VGG16, DenseNet169, DenseNet161, and ResNet50) that have been successfully used for a similar purpose with a high-resolution, augmented RGB HAM10000 skin cancer image dataset. The image resolution of the original HAM10000 dataset is 800 × 600 pixels. The highest achieved performance for 80 × 80, 64 × 64, and 32 × 32 pixel images were 80.46%, 78.56%, and 74.15%, respectively. All of these results were accomplished from the ImageNet pre-trained VGG16 model. The second-best model in terms of transfer learning was DenseNet169. The performances demonstrate that even within these severe circumstances, neural network-based transfer learning holds promising possibilities.

Autonomous vehicles no longer belong to the realm of science fiction. They have become a prominent area of research in the last two decades because of the integration of Artificial Intelligence in the automobile industry. Apart from the development of various complex learning algorithms, the advancement of cameras, sensors, and geolocation technology as well as the escalation in the capacity of machines have played a crucial role in bringing this technology into reality. We have had significant breakthroughs in the development of autonomous cars within the last ten years. However, despite the success of multiple prototypes in navigating within the borders of a delimited area, researchers are yet to overcome several drawbacks before embodying them in the transport system; and one of those hurdles lies in the lane detection system of the cars. Therefore, in this article, we present an intelligent lane detection algorithm incorporating fully-connected Neural Networks with a secondary layer protection scheme to detect the borders of a lane. We achieved over 98% classification accuracy using the proposed lane detection model. We also implemented the model in a small prototype to take a look at its performance. Experimental results infer that the algorithm is capable of lane detection and ready for practical use.

The consequences of depression are breathtaking these days. The suicidal tendency, as well as other fatigues, depression has almost soaked the world. A detection system can combat such consequences early. Motor activity sensor values carry out an individual's daily routine activities that can somewhat signify momentary changes in behavior. A consolidation of these motor sensor data with other demographic, clinical data can be very convenient in terms of depression detection. The combination of motor sensor reads as well as demographic data has been obligated in this study with machine learning approaches, namely Random Forest(RF), AdaBoost, and Artificial Neural Networks (ANN), achieving accuracy and Fl-score of 98% in both cases. The Cohen's kappa coefficient and Matthew's correlation coefficient are 0.96 in both factors.

Ultra Reliable Low Latency Communication (URLLC) is a 5G New Radio (NR) application that requires strict reliability and latency. URLLC traffic is usually scheduled on top of the ongoing enhanced Mobile Broadband (eMBB) transmissions ( i.e., puncturing the current eMBB transmission) and cannot be queued due to its hard latency requirements. In this letter, we propose a risk-sensitive based formulation to allocate resources to the incoming URLLC traffic, while minimizing the risk of the eMBB transmission ( i.e., protecting the eMBB users with low data rate) and ensuring URLLC reliability. Specifically, the Conditional Value at Risk (CVaR) is introduced as a risk measure for eMBB transmission. Moreover, the reliability constraint of URLLC is formulated as a chance constraint and relaxed based on Markov’s inequality. We decompose the formulated problem into two subproblems in order to transform it into a convex form and then alternatively solve them until convergence. Simulation results show that the proposed approach allocates resources to the incoming URLLC traffic efficiently, while satisfying the reliability of both eMBB and URLLC.

LTE over unlicensed band (LTE-U) has emerged as an effective technique to overcome the challenge of spectrum scarcity. Using LTE-U along with advanced techniques such as carrier aggregation, one can boost the performance of existing cellular networks. However, if not properly managed, the use of LTE-U can potentially degrade the performance of co-existing Wi-Fi access points, which operate over the unlicensed frequency bands. Moreover, most of the existing works consider single operator in their proposals. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is studied. The goal is to enable the cellular network to use LTE-U with CA to meet the quality-of-service (QoS) needs of its users while protecting Wi-Fi access points (WAPs) for a network with multiple operators. In particular, the problem of LTE-U sum-rate maximization is addressed under user QoS and WAP-LTE-U coexistence constraints. To solve this problem, a cooperative Nash bargaining game (NBG) and a one-sided matching game are proposed. Here, the NBG solves the coexistence issue between LTE-U and Wi-Fi system, while the matching game solves the resources allocation problem in the LTE-U system. These two games repeat until convergence. Simulation results show the quality of the proposed approach over other comparing methods in terms of the per-user achieved rate, percentage of unsatisfied users, and fairness. The result also shows that the proposed approach can better protect the performance of Wi-Fi users, compared to the conventional listen-before-talk scheme.

Pneumonia is a fatal disease responsible for almost one in five child deaths worldwide. Many developing countries have high mortality rates due to pneumonia because of the unavailability of proper and timely diagnostic measures. Using machine learning-based diagnosis methods can help to detect the disease early and in less time and cost. In this study, we proposed a novel method to determine the presence of pneumonia and identify its type (bacterial or viral) through analyzing chest radiographs. We performed a three-class classification based on features containing diverse information of the samples. After using an augmentation technique to balance the dataset’s sample sizes, we extracted the chest X-ray images’ statistical features, as well as global features by employing a deep learning architecture. We then combined both sets of features and performed the final classification using the RandomForest classifier. A feature selection method was also incorporated to identify the features with the highest relevance. We tested the proposed method on a widely used (but relabeled) chest radiograph dataset to evaluate its performance. The proposed model can classify the dataset’s samples with an 86.30% classification accuracy and 86.03% F-score, which assert the model’s efficacy and reliability. However, results show that the classifier struggles while distinguishing between viral and bacterial pneumonia samples. Implementing this method will provide a fast and automatic way to detect pneumonia in a patient and identify its type.

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally, about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

IoT (Internet of Things) is dominating all over the world for developing technology. It is another information industry following the computer, internet, and mobile connection. In modern society, we must ensure security for leading a comfortable life. Nowadays, security has been affected by different types of matters. Gas leakage and fire incidents are considered among them. At present, there are many undesirable accidents from gas leakage and fire incidents. One way to prevent accidents involving gas leakage and fire incident detection is to affix a gas leakage and fire incident detection device at adequate places. Indeed, when the gas leakage or fire incident occurs, then the temperature can be increased naturally. Our proposed work, a simple system using low-cost devices, has been designed to send a phone call to the user via the GSM module in case of any gas or smoke leakage. It also sends data to the alarm, alerting the users and sending a graphical alert to the server via NodeMCU. Besides, a temperature sensor also detects the temperature of that hazardous situation at the same time and sends data to the web server. We are using different algorithms to know the sensor's early predictions' overall accuracy in real-life-critical situations through the machine learning approach. This proposed work will contribute if gas leaks or fires occur at home or in the industry, then people can take the necessary precaution in advance.

At present, the whole world is witnessing a horrifying outbreak caused by the Coronavirus Disease 2019 (COVID-19). The virus responsible for this disease is called SARS-CoV-2. It affects its victims’ respiratory system and causes severe lung inflammation, making it harder for them to breathe. The virus is airborne, and so has a high infection rate. Originated in China last December, the virus has spread across seven continents, affecting the population of over 210 countries, making it one of the fiercest pandemics ever recorded. Despite multiple independent and collaborative attempts to develop a vaccine or a cure, an effective solution is yet to come out. While the disease has put the world in a standstill, detecting the positive subjects and isolating them from the others as soon as possible is the only way to minimize its spread. However, many countries are currently experiencing a massive shortage of diagnostic equipment and medical personals. This insufficiency inspired us to work on a computer-based automatic method for the diagnosis of COVID-19. In this paper, we proposed a sequential Convolutional Neural Network (CNN)-based model to detect COVID-19 through analyzing Computed Tomography (CT) scan images. The model is capable of identifying the disease with almost 92.5% accuracy. We believe the implementation of this model will help the physicians and pathologists all over the world to single out the victims quickly and thus reduce the prevalence of COVID-19.

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient&rsquo, s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

COVID-19is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective to control this pandemic. In this paper, we provide an analytical model on the effectiveness of the sustainable lockdown policy that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we analyze a noncooperative game environment that provides an incentive for maintaining social distancing. Furthermore, the sustainability of the lockdown policy is also interpreted with the help of a game-theoretic incentive model for maintaining social distancing. Finally, an extensive numerical analysis is provided to study the impact of maintaining a social-distancing measure to prevent the Covid-19 outbreak. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

uRLLC and eMBB are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the MEAR of eMBB UEs while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use PSUM based algorithm, whereas the optimal TM is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs., Comment: 30 pages, 11 figures, IEEE Transactions on Communications

This paper introduces a method for flow monitoring through various supply lines by implementing a mathematical function, majority of which is dependent on standard deviation. Instead of including various sensors and numerous complex electrical connections which is still the common way to measure and monitor leakage or flow, this project just uses flow measuring sensor to collect data and process the data in such a way that actually focuses on user conditions and takes regular variation of flow into account and modifies its monitoring protocols regularly. The other systems in this sector implies various sensors to accurately detect leakage which is good but those systems lack the ability to collect, process and store regular data related to the supply line. For this reason, users often have to handle two different systems simultaneously, one to monitor leakage and the another for regular data collection regarding supply. To reduce that effort and cost, this system includes both flow measurement, data storing and monitoring as well as keeps an eye whether the flow is regular or there is any leakage or illegal line taken out. In the field of commercial distribution of oil and water, leakage and unauthorized or illegal supply is one of the main issues to be considered. Yet now, not many systems has been developed to prevent or monitor such losses. We have developed a system to monitor and locate line leakage, illegal supply lines and faulty supply lines in a much swift and simpler way which is cost effective too.

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two major service classes in the emerging 5G mobile network. uRLLC applications demand a stringent latency and reliability whereas eMBB services necessitate utmost data rates. The coexistence of uRLLC and eMBB services on the same radio resource leads to a challenging scheduling problem because of the trade-off among latency, reliability and spectral efficiency. In this paper, a puncturing scheme based coexistence approach between uRLLC and eMBB traffic is proposed for the upcoming 5G mobile networks. Specifically, an optimization problem is formulated with the objective of maximizing the minimum expected achieved rate of eMBB users in the long run basis while meeting the uRLLC requirements. To solve this co-scheduling optimization problem, we decomposed it into two sub-problems with the same objective of the original problem: 1) resource allocation problem for eMBB users, and 2) resource allocation problem for uRLLC users. A heuristic algorithm is used for solving the first sub-problem, whereas the one-sided matching game is used for solving the second. Simulation results show the advantages of the proposed approach over other baseline methods in terms of the minimum achieved rate and fairness among the eMBB users.

Long-Term Evolution in the Unlicensed Spectrum (LTE-U) is considered as an indispensable technology to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the Base Station (BS). However, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raise the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this paper, we propose a novel mechanism based on neural networks for the coexistence of an LTE-U BS in the unlicensed spectrum alongside with WiFi access points. Specifically, we model the problem in coexistence as a 2-Dimensions Hopfield Neural Network (2D-HNN) based optimization problem that aims to achieve fairness considering both the LTE-U data rate and the QoS requirements of WiFi networks. Using the energy function of 2D-HNNs, precise investigation of its minimization property can directly provide the solution of the optimization problem. Furthermore, the problem of allocating the unlicensed resources to LTE-U users is modeled as a 2D-HNN and its energy function is leveraged to allocate resources to LTE-U users based on their channel states. Numerical results show that the proposed algorithm allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi networks. Moreover, more than 90% fairness among the LTE-U users is achieved when allocating the unlicensed resources to LTE-U users based on the proposed algorithm.

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

LTE over unlicensed band (LTE-U) has emerged as an effective technique to overcome the challenge of spectrum scarcity. Using LTE-U along with advanced techniques such as carrier aggregation (CA), one can boost the performance of existing cellular networks. However, if not properly managed, the use of LTE-U can potentially degrade the performance of co­existing Wi-Fi access points which operate over the unlicensed frequency bands. Moreover, most of the existing works consider a macro base station (MBS) or a small cell base station (SBS) for their proposals. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is studied. The goal is to enable the cellular network to use LTE-U with CA to meet the quality-of-service (QoS) of the users while protecting Wi-Fi access points (WAPs), considering multiple SBSs from different operators in a dense deployment scenario. Specifically, an LTE-U sum-rate maximization problem is formulated under a user QoS and WAP-LTE-U co-existence constraints. To solve this problem, a cooperative Nash bargaining game is proposed. This game allows LTE-U and WAPs to share time resource while protecting Wi-Fi system. For allocating unlicensed resource among LTE-U users, a heuristic algorithm is proposed. Simulation results show that the proposed method is better than the comparing methods regarding per user achieved rate, percentage of unsatisfied users and fairness. The result also shows that the proposed method protects Wi-Fi user far better way than basic listen-before-talk (LBT) does.

Daily-life oriented applications and multimedia entertainments through smart devices are creating immense stress to the current cellular foundation. To overwhelm from this loopholes, both academia and industry are trying their best by incorporating different technologies with existing ones using licensed spectrum. On the other side, Device-to-Device (D2D) communications are also being used to improve the spectrum efficiency and user experience by reutilizing licensed cellular spectrum. But with insufficient licensed spectrum, it is impossible to meet the demand of users in the current scenario. So, peoples are thinking to utilize unlicensed spectrum with licensed one to alleviate this scarcity issue and provide guaranteed Quality of Service (QoS) to the users. In this paper, we want to extend D2D communication and LTE-A network into the unlicensed spectrum. But this initiative will harm the performance of other technologies which are already working in the same unlicensed band. Moreover, if multiple mobile network operators (MNOs) use the same unlicensed band then they will diminish the benefits of each other. So this paper wants to maximize the sum-rate of LTE users and D2D pairs by allocating licensed and unlicensed subchannels considering their QoS requirements while protecting minimum requirements of WiFi Access Points (WAPs). Then we solve this problem with the help of Nash bargaining game (NBG) between Small Cell Base Stations (SBSs) and WAPs by the cooperative approach. Simulation results show the effectiveness and efficiency of the proposed approach.

Social networks have gained huge research interest, especially in viral marketing due to their rapid boom in the past years. It is very crucial to identify the influential users in the social networks for viral and target marketing. Influence maximization (IM) problem estimates such influential users in the social networks. With an initial seed set, the IM finds a maximum number of nodes that can be activated in the network under some diffusion models e.g. Linear Threshold model or Independent Cascade model. But previous works in this field have not studied about the minimum cost, termed as opportunity cost (OC), to motivate those seed nodes. In this work, we define a novel Reverse Influence Maximization (RIM) problem to determine the opportunity cost of influence maximization. Employing the influence propagation in opposite order, the RIM determines the minimum number of nodes that must be activated in order to motivate a set of target nodes. We propose Random RIM (R-RIM) and Randomized Linear Threshold RIM (RLT-RIM) models to tackle the RIM problem. We also perform a simulation to evaluate the performance of the algorithms using two real world datasets. The result shows that the proposed models determine the optimized opportunity cost with faster running time margin.

Data-intensive applications are anticipated to increase day by day. The amount of computing and storage resources required by these are also increasing simultaneously, creating high demands for cloud resources. Cloud provider's limited resources are not adequate to meet the elastic demand of consumers. Cloud coalition is the key approach to deal with such elastic demand of resources in the environment. But traditional coalition formation mechanism allows a cloud provider to participate into a single coalition which leads to under-utilize of coalition resources with increasing security risk like botnet attacks. In this paper, a mathematical framework from cooperative game namely overlapping coalition formation (OCF) game to overcome the under-utilization problem and to reduce security risk for cloud providers (CPs) is introduced by permitting a CP to participate into multiple small coalitions. First, the concepts of OCF game in cloud is presented and then, an algorithm for the stability in OCF game is introduced. We analyze the performance of the proposed approach with traditional coalition formation and no-coalition scenarios in perspective of payoffs, yielding higher payoffs for the participating CPs.

With the manifestation of the Internet of Things IoT and fog computing, the quantity of edge devices is escalating exponentially all over the world, providing better services to the end user with the help of existing and upcoming communication infrastructures. All of these devices are producing and communicating a huge amount of data and control information around this open IoT environment. A large amount of this information contains personal and important information for the user as well as for the organization. The number of attack vectors for malicious users is high due to the openness, distributed nature, and lack of control over the whole IoT environment. For building the IoT as an effective service platform, end users need to trust the system. For this reason, security and privacy of information in the IoT is a great concern in critical infrastructures such as the smart home, smart city, smart healthcare, smart industry, etc. In this article, we propose three information hiding techniques for protecting communication in critical IoT infrastructure with the help of steganography, where RGB images are used as carriers for the information. We hide the information in the deeper layer of the image channels with minimum distortion in the least significant bit lsb to be used as indication of data. We analyze our technique both mathematically and experimentally. Mathematically, we show that the adversary cannot predict the actual information by analysis. The proposed approach achieved better imperceptibility and capacity than the various existing techniques along with better resistance to steganalysis attacks such as histogram analysis and RS analysis, as proven experimentally.

Internet of Things (IoT) is a vision of connecting everything for providing better services efficiently. IoT consists of enormous number of heterogeneous computing devices with different capabilities to provide a diverse range of services situated around the globe. Increasing number of IoT devices and availability of different services in the edge of the networks makes it inevitable to interact among devices. As a result, this open, non-homogeneous and distributed environment also breaches the integrity of secure and reliable device to device (D2D) communication. Traditional access control mechanisms are not prolific to the itinerant, decentralized and dynamic scenarios in the IoT. Trust management is a proven technology for applications like P2P, Grid, and ad hoc network. Hence, this technology can also be used to increase the user reliability in IoT. In this paper, we propose a trust based D2D communication mechanism for accessing different services to meet the growing transactions and successful operations of IoT. We also analyze the effect of adaptive trust parameters for IoT device to device communication in order to access the services.

This paper presents an effective framework for market monitoring, supervision and price management in developing countries using Information and Communication Technology (ICT). This research also illustrates the possible ways to handle the price hike and also derives solutions to resist unethical and ill-motivated hoarding by integrating ICT. In this paper, we have presented an overview on the application of ICT in disseminating market price information to the consumers and also existent mechanisms in adopting ICT as a tool to stabilize the market price from the point of view of consumers' right. Rather than paying the price of any commodity in cash to the seller, the consumer is charged through the identification of his mobile phone within particular fair price margin by sending SMS. The seller also demands the price using SMS and confirmation is also made by the buyer through the same. This fair price margin should be market specific and be defined by the government or some legitimate agencies appointed to monitor the market as currently done to some extent for displaying the market price on the entrance of the markets in large cities. Since, the price margin is supposed to be defined balancing mutual interests of the producer, seller and buyer along with considerations of all other related phenomena like transport costs to the market, labor costs in that area etc, there may be legal bindings to come to an agreement. Illustrating ICT based promising and easiest framework of making the people aware of price of any product and thus maintaining a reasonable price for markets in developing country like Bangladesh is the main contribution of the paper.

The paper proposes a RGB channel based steganographic technique for images imparting better information security. This technique inserts the information into deeper layers of the selected RGB channel and the position is determined depending on the status of channel and value of the secret key. Pixels of the cover image are selected depending on the environment of the channels and hidden information. The ambiguity of pixel, channel and position selection process increases robustness of the steganographic system. The technique is also less vulnerable to unintentional attacks like image manipulation as data hides in the deeper layer of the pixels. The system uses the RGB channels of the stego-image and the secret key to extract the hidden information. The use of secret key gives another way to secure the information from malicious user. The experiment shows that on average 77.00% pixels of the cover image are used for hiding secret information and produces high PSNR value which indicates the capacity and imperceptibility of the technique respectively.

In this paper, we present a novel, principled approach to resolve the remained problem of substitution technique of audio steganography. Using the proposed method, the message bits are embedded into deeper layer depending on the environment of the host audio resulting increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden messages.

Wireless sensor nodes are use most embedded computing application. Multihop cluster hierarchy has been presented for large wireless sensor networks (WSNs) that can provide scalable routing, data aggregation, and querying. The energy consumption rate for sensors in a WSN varies greatly based on the protocols the sensors use for communications. In this paper we present a cluster based routing algorithm. One of our main goals is to design the energy efficient routing protocol. Here we try to solve the usual problems of WSNs. We know the efficiency of WSNs depend upon the distance between node to base station and the amount of data to be transferred and the performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. This algorithm makes the best use of node with low number of cluster head know as super node. Here we divided the full region in four equal zones and the centre area of the region is used to select for super node. Each zone is considered separately and the zone may be or not divided further that’s depending upon the density of nodes in that zone and capability of the super node. This algorithm forms multilayer communication. The no of layer depends on the network current load and statistics. Our algorithm is easily extended to generate a hierarchy of cluster heads to obtain better network management and energy efficiency.

The demand for data rate is increasing exponentially in the cellular networks due to the emergence of more and more data-driven applications and smart-devices. Currently, these demands cannot be met by cellular networks with any single technology. As a consequence, Quality-of-service (QoS) needs to be sacrificed by some users. To provide guaranteed QoS to the users, researchers are considering massive MIMO, LTE-A, cooperative communications etc as the suitable candidate for the next generation cellular network. In this paper, we propose a collaborative communication mechanism with incentive for downlink in the cellular network for providing guaranteed QoS by utilizing multiple connectivity of the user's smart equipments. We formulate the problem as an optimization problem first and afterwards, we solve this problem with the help of matching theory. Simulation results are shown to represent performance of the technique.

Cellular networks are facing the challenge of meeting aggressive data demand with limited licensed spectrum and LTE over the unlicensed band (LTE-U) has emerged as an effective way to defeat this hurdle. Using LTE-U along with superior techniques such as carrier aggregation (CA), one can boost the performance of existing cellular networks. Nevertheless, LTE-U can potentially deteriorate the performance of co-existing Wi-Fi systems operating over the unlicensed bands if not well-managed. Furthermore, single operator scenario is considered in most of the existing co-existence works. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is investigated. The object is to facilitate the cellular networks to use LTE-U with CA to reduce the gap between achieved rate and quality-of-service (QoS) of the user while protecting Wi-Fi users, considering multiple operators in a dense deployment scenario. To resolve this problem, a multi-gaming approach is used. A cooperative Nash bargaining game (NBG) is used for sharing time resource in unlicensed for LTE-U and Wi-Fi systems. Following, a bankruptcy game is used by operators to allocate unlicensed resource among LTE-U users. Simulation results show that the proposed approach is better than the comparing methods regarding per user achieved rate, and fairness. It also shows that the proposed technique defends Wi-Fi user greatly in dense deployment than basic listen-before-talk (LBT) does.