Federated learning is an emerging machine technique that enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only updates without uploading the raw data. This provides a promising privacy preservation for devices while simultaneously ensuring high performance. The majority of existing work has focused on designing advanced algorithms with aim to achieve better However, challenges, such as incentive mechanisms participating in and...

The drastically increasing volume and the growing trend on types of data have brought in possibility realizing advanced applications such as enhanced driving safety, enriched existing vehicular services through sharing among vehicles analysis. Due to limited resources with vehicles, edge computing networks (VECONs) i.e., integration mobile networks, can provide powerful massive storage resources. However, road side units that primarily presume role servers cannot be fully trusted, which may...

In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research industrial attention with the explosive development of cities. By introducing Internet Things (IoT) technology, cities are able to achieve exquisite by ubiquitous monitoring reliable communications. However, long-term efficiency has become an important issue when using IoT-based network structure. this article, we focus on designing system based edge computing infrastructure...

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services edge of Internet Things (IoT) system. However, static server deployment may cause "service hole" in IoT networks which location and service requests User Equipments (UEs) be dynamically changing. In this paper, we firstly explore vehicle computing network architecture vehicles can act as mobile servers provide computation for nearby UEs. Then, propose vehicle-assisted offloading scheme UEs while considering...

It is envisioned that home networks will shift from current machine-to-human communications to the machine-to-machine paradigm with rapid penetration of embedded devices in surroundings. In this article, we first identify fundamental challenges M2M networks. Then present architecture decomposed into three subareas depending on radio service ranges and potential applications. Finally, focus QoS management networks, considering increasing number multimedia growing visual requirements a area....

Based upon cognitive radio technology, we propose a new Machine-to-Machine (M2M) communications paradigm, namely Cognitive M2M (CM2M) communication. We first motivate the use of technology in from different point views, including technical, applications, industry support, and standardization perspectives. Then, our CM2M network architecture machine model are presented systems coexistence TV white spaces is discussed. After that, for smart grid presented, which also an energy-efficiency...

This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a two-stage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In paper, an algorithm for estimating matrix that can be viewed as extension DUET and TIFROM methods developed. Standard clustering algorithms (e.g., K-means method) also used if are sufficiently sparse. Compared DUET, methods, standard...

Tensor networks have in recent years emerged as the powerful tools for solving large-scale optimization problems. One of most popular tensor network is train (TT) decomposition that acts building blocks complicated networks. However, TT highly depends on permutations dimensions, due to its strictly sequential multilinear products over latent cores, which leads difficulties finding optimal representation. In this paper, we introduce a fundamental model represent large dimensional by circular...

The current centrally controlled power grid is undergoing a drastic change in order to deal with increasingly diversified challenges, including environment and infrastructure. next-generation grid, known as the smart will be realized proactive usage of state-of-the-art technologies areas sensing, communications, control, computing, information technology. In an efficient reliable communication architecture plays crucial role improving efficiency, sustainability, stability. this article, we...

This paper considers finite-time distributed state estimation for discrete-time nonlinear systems over sensor networks. The Round-Robin protocol is introduced to overcome the channel capacity constraint among nodes, and multiplicative noise employed model fading. In order improve performance of estimator under situation, where transmission resources are limited, fading channels with different stochastic properties used in each round by allocating resources. Sufficient conditions average...

In a smart grid network, demand-side management plays significant role in allowing consumers, incentivized by utilities, to manage their energy consumption. This can be done through shifting consumption off-peak hours and thus reducing the peak-to-average ratio (PAR) of electricity system. this paper, we begin proposing scheduling scheme for household appliances that considers PAR constraint. An initial optimization problem is formulated minimize cost consumers determination optimal usage...

Reinforcement learning (RL) has been successfully employed as a powerful tool in designing adaptive optimal controllers. Recently, off-policy emerged to design controllers for systems with completely unknown dynamics. However, current approaches tracking control either result bounded error, rather than zero or require partial knowledge of the system Moreover, they usually collect large set data learn solution. To obviate these limitations, this paper applies combination and experience-replay...

Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular by employing resources at wireless access networks. In this paper, we study efficient task offloading schemes The vehicles perform time selection, communication, resource allocations optimally, mobility maximum latency tasks considered. minimize system costs,...

The ubiquitous information from multiple-view data, as well the complementary among different views, is usually beneficial for various tasks, example, clustering, classification, denoising, and so on. Multiview subspace clustering based on fact that multiview data are generated a latent subspace. To recover underlying structure, successful approach adopted recently has been sparse and/or low-rank clustering. Despite existing approaches may numerically handle by exploring all possible...

The lack of the computation services in remote areas motivates power Internet Things (IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) technology. However, will be significantly affected by UAVs' capacities, and distinct IoT applications. In this paper, we firstly propose a cooperative UAV-enabled MEC network structure which UAVs are able help other execute tasks. Then, offloading scheme is presented while considering interference mitigation from devices. To...

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make right driving decisions and autonomously obey traffic rules by correctly recognizing signs. However, machine learning-based sign recognition on Internet Vehicles (IoV), a large amount data from distributed needed be gathered in centralized server model training, which brings serious privacy leakage risk because containing lots location information. To address this issue, we...

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and power grid, which provides flexible demand response management (DRM) for the reliability of smart grid. EV mobility is a unique inherent feature V2G system. However, inter-relationship DRM not obvious. In this paper, we focus on exploration to impact in systems We first present dynamic complex network model mobile networks, considering fact that EVs travel across multiple districts, hence...

Vehicular social network (VSN) is envisioned to serve as an essential data sensing, exchanging and processing platform for the future Intelligent Transportation Systems. In this paper, we aim address location privacy issue in VSNs. traditional pseudonym-based solutions, privacy-preserving strength mainly dependent on number of vehicles meeting at same occasion. We notice that individual vehicle actually has many chances meet several other vehicles. most occasions, there are only few...

Nonnegative matrix factorization (NMF) algorithms often suffer from slow convergence speed due to the nonnegativity constraints, especially for large-scale problems. Low-rank approximation methods such as principle component analysis (PCA) are widely used in factorizations suppress noise, reduce computational complexity and memory requirements. However, they cannot be applied NMF directly so far result factors with mixed signs. In this paper, low-rank is introduced (named lraNMF), which not...

In this paper, the electricity cost minimization problem is considered for a residential microgrid which consists of multiple households (users) equipped with renewable-based distributed energy resource (DER). Each user has set nonshiftable and shiftable loads. Bidirectional transactions are allowed, dynamic pricing model purchasing/selling from/to grid proposed. order to reduce cost, following decisions needed be made: 1) scheduling loads; 2) each at time slot; 3) amount purchased/sold by...

In cognitive radio networks, spectrum sensing is a crucial component in the discovery of opportunities for secondary systems (or unlicensed systems). The performance characterized by both accuracy and efficiency. Currently, significant research effort has been made on improving accuracy. Several exemplary techniques include energy detectors, feature cooperative sensing. these schemes, either one or multiple users (SUs) perform single same channel during each period. This strategy...

With the increasing availability of various sensor technologies, we now have access to large amounts multi-block (also called multi-set, multi-relational, or multi-view) data that need be jointly analyzed explore their latent connections. Various component analysis methods played an increasingly important role for such coupled data. In this paper, first provide a brief review existing matrix-based (two-way) joint with focus on biomedical applications. Then, discuss extensions and...