Number Of Mobile Devices Research Articles

Analisis Pengaruh Aplikasi Game Online Mobile Legend Di Kalangan Muda

Currently we see how the development of mobile phones has greatly influenced games or video games. Now with Android-equipped phones that can load more data stored on the phone, "games that previously could only be played offline are being developed, such as games on the Internet that can be played online indefinitely"¹, in this case. , games are also developed with the term "online games" which use data in their work. This continuously developing game then influences the lives of teenagers. The number of mobile devices is also increasing, this also has the influence of mobile legends which also increase the needs of children during the Covid pandemic, so the number of mobile phones also increases. In this magazine we will tell you about the effects of the Mobile Legends game. This research explains the influence of the Mobile Legends game on teenage behavior. This game is now widely spread among teenagers and has an impact on their socially unhealthy development in society.

Modeling Electromagnetic Field Radiation Variability for Minimization of Exposure Rate in Public Health Environments: A Machine Learning Approach

Public Health awareness of the exposure to radiofrequency electromagnetic fields (Rf-EMF) is necessary for epidemiological studies on possible bio-effects. The Knowledge will promote good health, reduce health risk factors, lower morbidity and mortality rates often associated with public health environment (PHE). Modeling EMF radiation variability and its application is vital given the prospective growth in both the number of mobile devices and equipment radiating electromagnetic fields (EMF) and the increasing concerns in the general public. The main goal of this work is to develop a framework based on Machine Learning (ML) approach to determine the exposure level on time-based variances within and during operational procedures of frequency bands used in mobile telecommunication, digital devices, transmitters, broadcasting terminals etc. The system adopts the measurements and instrumentation methodology, using a handheld Poniie pn8000 multi-field EMF metre and EME Spy exposimeter with a detection limit of 0.0066mW/m (2) with sampling at certain interval in a specific frequency ensemble. The measurements were synthesized as exposure coefficients with assessment based on EMF radiation from emitting devices and matching with International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards endorsed by WHO. Consequently, obtained data was processed and segmented into training and test sets on 70:30 ratios respectively. Computing modeling of the system was performed with R programming language using data obtained from field measurements and records. The prosed system used Ensemble ML-Random Forest Algorithm and Decision Tree algorithm (DTA). Performance evaluation and results obtained clearly demonstrates 87% based on the Confusion Matrix (CM) outcomes for Random Forest and Decision Tree presents 56%. This justifies the capability of ML techniques in accurately classifying EMF radiation to mitigate exposure rate and improve healthcare throughput. The model was transformed and deployed in a production system API environment with Graphical User Interface (GUI) for flexibility. This work revealed relating potential health bio-effects associated with EMF exposure and the different parameters that are currently used for evaluating, limiting, and mitigating the impact of the exposure on the general public.

A study of routing-based distributed mobility management in supporting seamless data transmission in smart cities

<p>The current trend of people having their multimedia-capable wireless and mobile devices roam within highly urbanized areas (i.e., smart cities) has led to the wide deployment of overlapping heterogeneous wireless network technologies. In this regard, various challenges have emerged, such as the number of mobile devices that connect or disconnect a wireless network domain, the diversity of time of their connections, frequent handovers as mobile devices frequently change their locations, the heterogeneity of wireless network technologies, cooperation challenges between the overlapping heterogeneous wireless network technologies, the increasing volume of multimedia traffic, security and privacy issues, and many more. This paper focuses on the deployment of a routing-based distributed mobility management (DMM) scheme to address the constraints and limitations of centralized wireless architectures for smart cities. The comparative analysis with centralized mobility management solutions shows significant alleviation in performance as to handover latency and packet losses, thus providing seamless handovers to maintain quality of service (QoS) for multimedia services.</p>

Interference management in 5G and beyond networks: A comprehensive survey

During the last decade, wireless data services have had an incredible impact on people’s lives in ways we could never have imagined. The number of mobile devices has increased exponentially and data traffic has almost doubled every year. Undoubtedly, the rate of growth will continue to be rapid with the explosive increase in demands for data rates, latency, massive connectivity, network reliability, and energy efficiency. In order to manage this level of growth and meet these requirements, the fifth-generation (5G) mobile communications network is envisioned as a revolutionary advancement combining various improvements to previous mobile generation networks and new technologies, including the use of millimeter wavebands (mm-wave), massive multiple-input multiple-output (mMIMO) multi-beam antennas, network densification, dynamic Time Division Duplex (TDD) transmission, and new waveforms with mixed numerologies. New revolutionary features including terahertz (THz) communications and the integration of Non-Terrestrial Networks (NTN) can further improve the performance and signal quality for future 6G networks. However, despite the inevitable benefits of all these key technologies, the heterogeneous and ultra-flexible structure of the 5G and beyond network brings non-orthogonality into the system and generates significant interference that needs to be handled carefully. Therefore, it is essential to design effective interference management schemes to mitigate severe and sometimes unpredictable interference in mobile networks. In this paper, we provide a comprehensive review of interference management in 5G and Beyond networks and discuss its future evolution. We start with a unified classification and a detailed explanation of the different types of interference and continue by presenting our taxonomy of existing interference management approaches. Then, after explaining interference measurement reports and signaling, we provide for each type of interference identified, an in-depth literature review and technical discussion of appropriate management schemes. We finish by discussing the main interference challenges that will be encountered in future 6G networks and by presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution. This review will provide a first-hand guide to the industry in determining the most relevant technology for interference management, and will also allow for consideration of future challenges and research directions.

Social media use among the population after the COVID-19 pandemic

Introduction: With the development of new technologies and easier access to the Internet, the number of mobile devices is growing. According to statistics, in 2023, 5.18 billion people worldwide used the Internet, which was 64.6% of the world's population. The use of the Internet, including social media, has many health effects, such as Internet addiction. The COVID-19 pandemic has exacerbated this problem. Home quarantine and increasing social distance have clearly increased the level of emotional stress among humanity. Aim of the work: The aim of the work is to analyze the use of social media after the COVID-19 pandemic in Poland and the health consequences of this fact. Materials and methods: A proprietary online questionnaire was prepared, containing19 questions. The survey was conducted in the Swietokrzyskie and Mazowieckie provinces from April 2023 to June 2023. 330 results were collected. The anonymity of participants taking part in the survey was preserved. Research results: The respondents included 219 women, accounting for 66.36%, and111 men, 33.64% of the group. More than half of the respondents, 52.73%, believe that the COVID-19 pandemic has increased the amount of time spent on social media. The largest number of citizens, 153 (46.36%), spent between 2 and 4 hours a day on social media, while the smallest number, only 21 (6.36%), spent more than 6 hours a day. Only 13.64% of respondents log out of social media. 71.82% of respondents deny the statement that using social media in stressful moments can bring solace. Conclusions: This study proves the adverse impact of the COVID-19 pandemic on the frequency of social media use, causing its prolongation. Research also shows the growing problem of Internet addiction and its many health consequences. The problem mainly affects young people. It is important to conduct educational campaigns on the effects of Internet and social media addiction.

Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities

The rapid expansion of the Internet and communication technologies is leading to significant changes in both society and the economy. This development is driving the evolution of smart cities, which utilize cutting-edge technologies and data analysis to optimize efficiency and reduce waste in their infrastructure and services. As the number of mobile devices and embedded computers grows, new technologies, such as fifth-generation (5G) cellular broadband networks and the Internet of Things (IoT), are emerging to extend wireless network connectivity. These cities are often referred to as unmanned aerial vehicles (UAVs), highlighting their innovative approach to utilizing technology. To address the challenges posed by continuously varying perturbations, such as unknown states, gyroscopic disturbance torque, and parametric uncertainties, an adaptive recursive sliding mode control (ARSMC) has been developed. The high computational cost and high-order nonlinear behavior of UAVs make them difficult to control. The controller design is divided into two steps. First, a confined stability analysis is performed using controllability and observability to estimate the system’s stability calculation. Second, a Lyapunov-based controller design analysis is systematically tackled using a recursive design procedure. The strategy design aims to enhance robustness through Lyapunov stability-based mathematical analysis in the presence of considered perturbations. The ARSMC introduces new variables that depend on state variables, controlling parameters, and stabilizing functions to minimize unwanted signals and compensate for nonlinearities in the system. The paper’s significant contribution is to improve the controlled output’s rise time and stability time while ensuring efficient robustness.

ЗАХИСТ НЕСТРУКТУРОВАНОЇ ІНФОРМАЦІЇ НА МОБІЛЬНОМУ ПРИСТРОЮ

Information technology (IT) and computer systems are becoming an integral part of modern man's life, irreplaceable assistants. The development of IT happens at lightning speed and has a great impact on our society, every day we could observe new technical and engineering inventions. Moreover, it can be both a certain integration into the work process - automation of the daily routine, and part of leisure - communication and entertainment, etc. The entire complex of technologies and systems improve and facilitate the performance of everyday tasks, open new opportunities and remove borders. It is difficult to imagine a modern person without a mobile phone or tablet, which has almost continuous and unlimited access to the Internet or the worldwide global network. Today, a mobile device has a certain sacred meaning, it can contain a lot of different information or data. The nature and importance of this information may vary, but protection is a priority. And considering the developed dynamics and the uncertainty of some processes, engineers and developers face a difficult and important task to protect information systems and ensure reliable operation. To some extent, the mobile device can be considered the most problematic component. This device has a few advantages and features - this is exactly what makes it vulnerable to potential attackers and user actions can cause a few problems. This article is devoted to the problems of using mobile devices and protecting unstructured information. Protecting unstructured information on a mobile device is an important task from a cybersecurity perspective. Unstructured information can be in the form of text documents, emails, photos, videos, voice recordings, and other content stored on mobile devices. The main problems include loss or theft of a mobile device; operating system and software vulnerabilities; insufficient level of encryption and insufficient level of complexity of passwords; neglect of cyber hygiene. Existing engineering and architectural solutions do not always fulfill the task and may have problems with integration. Do not forget about the economic component. The analysis of the application area and the research results indicate that information protection in general is important and there is a demand for quick and effective solutions regarding the security of information systems in general and mobile devices. Only an integrated approach can provide an effective solution. The future of protecting unstructured information on mobile devices will be based on a combination of technological innovations and increased user awareness of cybersecurity issues. Some areas of development include the use of artificial intelligence (AI) and machine learning: AI can help develop advanced defense mechanisms that automatically detect anomalous or suspicious activity and block potential threats; biometric authentication, improvements in biometric authentication technologies, such as facial recognition, fingerprints, can provide more reliable protection of unstructured information; application of blockchain technologies: blockchain can play an important role in ensuring information security, as it allows data to be stored distributed and protected from changes; development of quantum-resistant encryption algorithms: with the development of quantum computers, there is a need for new encryption algorithms that can withstand the power of quantum hacking; privacy by design (Privacy by Design): designing mobile devices and applications with user privacy in mind will help protect unstructured information on mobile devices; user education and awareness: increasing users' awareness of cyber security and teaching them effective methods of protecting information on mobile devices will help reduce the risk of security incidents; development of the legislation and regulatory framework, improvement of the legislation and regulatory framework in the field of cyber security will contribute to the creation of a reliable environment for the protection of unstructured information. Securing unstructured information on mobile devices will rely on a combination of technological advances, user education, and collaboration between various parties such as developers, service providers, and lawmakers. Ensuring data security and privacy in the digital world remains a priority as the number of mobile devices and their use continues to grow. The modern user of mobile devices has certain requirements related to ergonomics and the level of information awareness. The development of the industry requires fast, reliable, and effective solutions that fall not only in the economic plane, but also in the plane of cyber security. The use of mobile devices can adapt to the fast-paced modern security needs of an organization and can be easily configured to meet specific requirements. A mobile device is convenient and affordable for many users, it is usually close to the user and can be used as an additional security element.

A Study on Remote Detection of Turkey Digital Identity Card Hologram Element

Technological advances and the pandemic have caused an explosion in demand for remote services. In critical processes such as banking, insurance and notary public, the service to be provided remotely has to be provided as quickly and safely as the service provided in presence. In such sensitive processes, the identity card authenticity detection must be done accurately and securely. One of the techniques used in accuracy detection is the use of mobile devices that support contactless chip technology. However, the number of mobile devices using this method is limited today. This situation hinders the effective and widespread use of remotely provided services and resources. Therefore, a different solution method was needed that would be an alternative to contactless chip technology, remove the obstacles to widespread use and reduce incidents such as counterfeiting. In this study, it was investigated whether the hologram, which is one of the security elements in the identity, can be used in determining the accuracy of Turkey's digital identity cards. For this purpose, a special function and program has been developed on the MATLAB application by using 2-D neighborhood methods to detect the crescent on the hologram. 227 digital ID cards were first evaluated on a mobile device over certain threshold values such as color level and similarity ratio. Then, each card used in the test was examined one by one with the naked eye. Approximately 99.56% similar results were obtained between the test findings made over the application and the findings obtained after the controls made with the naked eye. It has been concluded that the secure hologram image, one of the security elements on the digital ID card with this high success rate, can be used as computer aided in remote services. With this solution method, it has contributed to overcome an important obstacle in front of services that are thought to be provided remotely in many fields such as banks, notaries and education.

SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems

Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.

A Radio Environment Map Updating Mechanism Based on an Attention Mechanism and Siamese Neural Networks.

A radio environment map (REM) is an effective spectrum management tool. With the increase in the number of mobile devices, the wireless environment changes more and more frequently, bringing new challenges to REM updates. Traditional update methods usually rely on the amount of data collected for updating without paying attention to whether the wireless environment has changed enough. In particular, a waste of computational resources results from the frequently updated REM when the wireless environment does not change much. When the wireless environment changes a lot, the REM is not updated promptly, resulting in a decrease in REM accuracy. To overcome the above problems, this work combines the Siamese neural network and an attention mechanism in computer vision and proposes an update mechanism based on the amount of wireless environmental change starting from image data. The method compares the newly collected crowdsourced data with the constructed REM in terms of similarity. It uses similarity to measure the necessity of the REM to be updated. The algorithm in this paper can achieve a controlled update by setting a similarity threshold with good controllability. In addition, the effectiveness of the algorithm in detecting changes of the wireless environment has been demonstrated by combing simulation data.

Investigating High Performance Implementations of Sparse Code Multiple Access (SCMA) Codebook Design

The number of mobile devices, IoT devices, and their applications is increasing day by day in recent years and further going to increase in coming years. These applications and devices demand large connectivity, high throughput, and low latency which can be possible only in the B5G or 6G. The effective utilization of the spectrum is done by multiple access techniques which facilitate the spectrum to accommodate a large number of users. The 5G has different new technologies to support the massive user demand and high-speed data rate. The non-orthogonal multiple access scheme (NOMA) is considered for the B5G or 6G. The NOMA offers a massive number of user connectivity in contrast to the OMA which is an orthogonal multiple access scheme. The performance evaluation of the SCMA is investigated and recent advancement in the field of NOMA is discussed in this paper. The BER performance of Capacity-based codebook design and codebook design based on lattice constellation is evaluated on the two different channels i.e. AWGN and Rayleigh fading channel. The behavior of capacity-based codebook design is better than lattice-based codebook design. The performance of both codebook designs surpasses AWGN in contrast to the Rayleigh fading channel by the multipath available in the Rayleigh Fading channel.

Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

The Performance Analysis and Security Aspects of Manet

Mobile Ad hoc NETwork (MANET) is a type of network that is built by connecting a number of mobile devices together in a temporary manner through impermanent connections. Through broadcasting, the information should be distributed to all nodes in the network. A MANET is a network of self-configurable mobile nodes that are connected wirelessly. The security aspect of MANET is a major challenge, and there is a great deal of research being done in this area. The availability of energy is a critical criterion for a decentralised network. The protocol for AODV and DSR routing, as well as the security of the black hole node, are all investigated in this study. Protocols that are used during the route discovery process are particularly vulnerable to attack by the black hole. The goal of this survey, as a result, is to thoroughly investigate black hole attacks while also evaluating the performance of AODV and DSR during black hole attack scenarios. With Network Simulator 3, the work is completed by simulating both protocols under normal operation as well as under a black hole attack. The work is completed with Network Simulator 3 by simulating both protocols under a black hole attack (NS-3). AODV is more vulnerable to a black hole attack than the DSR, according to the results of simulations, and this vulnerability is greater under normal operating conditions. It has been discovered that MANET attacks are carried out with the assistance of a "black hole," according to simulation results.

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing, to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasizing critical aspects such as demographics, health and body features, activity and behavior recognition, and so forth. In addition, we review popular metrics in the literature to quantify the degree of privacy and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

Design and Analysis of Network Virus Defense System Based on Multimodal Data Mining Technology

This study analyzes and designs a network security management system based on the K-means algorithm. Through the investigation of network security managers, this article uses structured modeling technology to derive the system’s logical business functions, which are data acquisition function, data analysis function, and post-processing function. This study uses a three-tier architecture to design the system, describes the system data business processing flow, and gives the key flow of the K-means algorithm application. In addition, the system database is designed to provide support for system data processing. This study proposes a dual-network text matching model based on local interactive components. This model composes two modalities of single-modal short text data: a positional structural modal for describing local interactions and a global semantic understanding modal for extracting global semantic information. Two heterogeneous networks are used to extract two modalities. The principle of complementarity of modalities is realized by constructing the differences of each modal. At the same time, through the attention mechanism, the position information of the position structure modal is transferred to the global semantic understanding modal to obtain consistent comprehensive information so as to realize the principle of modal consistency. On this basis, by designing low-order interaction functions and high-order interaction functions, and using long- and short-term memory networks, we have, respectively, improved the position structure modal and global semantic understanding modal. The theoretical analysis and simulation results of the model show that the propagation characteristics of the network worm are completely determined by the threshold R0 required for its existence. When R0 >1, the network worm will become popular even with a small initial infection number; conversely, even if the initial infection machine is large, the network worm will eventually become extinct. The research results of this article also show that the introduction of mobile devices has an important impact on the defense technology of network worms. To curb network worms that use both the network and mobile devices to spread, and to increase the proportion of machines that have never been infected with the virus, the most effective method is to control the number of mobile devices and reduce the possibility of cross-infection between mobile devices and machines. The simulation results show that, regardless of whether dynamic isolation and antivirus software are considered, the local area network-based choke method given in this study can effectively curb intelligent network worms that have slow scanning rates and clear scanning targets. In addition, the choke method presented in this article can determine the choke threshold without affecting the normal network scanning behavior of users in the local area network.

Beamforming Performance Analysis of Millimeter-Wave 5G Wireless Networks

With the rapid growth in the number of mobile devices and user connectivity, the demand for higher system capacity and improved quality-of-service is required. As the demand for high-speed wireless communication grows, numerous modulation techniques in the frequency, temporal, and spatial domains, such as orthogonal frequency division multiplexing (OFDM), time division multiple access (TDMA), space division multiple access (SDMA), and multiple-input multiple-output (MIMO), are being developed. Along with those approaches, electromagnetic waves’ orbital angular momentum (OAM) is attracting attention because it has the potential to boost the wireless communication capacity. Antenna electromagnetic radiation can be described by a sum of Eigen functions with unique eigenvalues, as is well known. In order to address such issues, the millimeter-wave (mmWave) communication is proposed which is considered as one of the potential technology for 5G wireless networks. The intrinsic feature of all electromagnetic waves is OAM. The OAM beams’ unique qualities have led to a slew of new uses. Broadband OAM generators, on the other hand, have gotten very little attention, especially in the mmWave frequency band. The use of OAM in conjunction with mmWave can reduce the beam power loss, enhance the received signal quality, and hence increase the system capacity. The transmitter and receiver antennas must be coaxial and parallel to achieve precise mode detection. The proposed mmWave integrated with OAM system model is discussed in this study. The channel model is created using the channel transition characteristics. The simulation results demonstrate that the proposed system model is a good way to boost the system capacity.

Dynamic Computation Offloading With Energy Harvesting Devices: A Graph-Based Deep Reinforcement Learning Approach

We study a joint partial offloading and resource allocations (JPORA) problem for the mobile edge computing (MEC) with energy harvesting (EH), where the number of MDs, computation task, and harvested energy of mobile device (MD) are highly dynamic. It is critical to acquire an algorithm that optimally adapts JPORA decisions. Recent studies employ deep deterministic policy gradient (DDPG) agent to tackle the JPORA problem. However, traditional DDPG cannot generalize well to different MEC network scale, due to the deep neural networks in DDPG can only extract latent representation from Euclidean data, with the characteristics of MEC network structural information ignored. To this end, by taking advantage of the graph-based relationship deduction ability form graph convolutional networks (GCN) and the self-evolution ability from the experience training of DDPG, we propose a centralized GCN-DDPG agent to learn making decisions for MDs, including offloading ratio, local computation capacity, and uplink transmission power. Experimental results show that the proposed GCN-DDPG provides significant performance improvement over a number of state-of-the-art DRL agents.

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

The popularity of Android devices has increased exponentially with an increase in the number of mobile devices. Millions of online apps are used in these devices. Energy consumption of a device is a major concern for end-users, who want a long usage time on a single battery charge. The energy consumed by the app must be optimized by developers, and the available APIs must be used carefully. A wake-lock is used in apps to control the power state of the Android device and often leads to energy leakage. In this study, we detected wake-lock leaks in Android apps using machine learning. We pre-processed apps by extracting wake-lock related APIs to obtain the structural information of wake-lock usage and oversampled the data using the synthetic minority oversampling technique (SMOTE) to balance the dataset. The machine learning algorithms used to detect wake-lock leaks were first optimized using grid search to determine the best parameters. These parameters were then used in training to detect wake-lock leaks in these apps. We employed various machine learning algorithms and divided them into simple and ensemble algorithms to evaluate their efficacy. The support vector machine (SVM) and stochastic gradient boosting (SGB) were the most effective, producing 97 % and 98 % accuracy, respectively.

An Incentive Mechanism for Privacy-Preserving Crowdsensing via Deep Reinforcement Learning

With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way toward an emerging paradigm, i.e., crowdsensing that facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants and to incentivize them to improve sensing quality. In this article, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data, and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multileader-multifollower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., Q-learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with Q-learning in a dueling network architecture, i.e., double deep Q network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.

Energy Efficiency Maximization Under Minimum Rate Constraints in Multi-Cell MIMO Systems With Finite Buffers

Having recognized the dramatic increase in the number of mobile devices and infrastructure nodes, standards organizations and regulatory bodies have adopted energy efficiency (EE) as a key performance metric for fifth-generation networks. Recent works on multiple input multiple output (MIMO) systems have suggested that it is important to use finite-buffer models, because they may lead to better transceiver designs and more accurate performance analyses than full-buffer traffic models. Therefore, this article addresses the MIMO transceiver design problem for EE maximization in the downlink of finite-buffer multicell systems. Unlike previous works, our problem formulation takes into account per-user minimum rate requirements. We arrive at a nonconvex fractional optimization problem, which is hard to tackle. By exploiting the properties of fractional programming, and using Dinkelbach's method, the resulting fractional form optimization problem is transformed to an equivalent optimization problem in subtractive form. Next, the nonconvexity of this problem is handled using successive convex approximation, leading to iterative centralized and decentralized resource allocation solutions. Finally, considering a realistic channel model with space, frequency and time correlations, numerical results confirm the effectiveness of the proposed algorithms and indicate significant performance gains in terms of achieved EE over existing solutions for full and finite-buffer models.