Devices In Close Proximity Research Articles

Autoencoder-based efficient resource allocation in device-to-device communication

Device-to-device (D2D) communication offloads the base station (BS) traffic, enhances spectral efficiency, and provides efficient data transmission with low latency between devices in close proximity. However, there are many issues in D2D communication, such as mode selection, device discovery, security, privacy, resource allocation, and interference management. At the time of the resource allocation, there is an issue of interference and eavesdropping by the malicious user on D2D pairs. Here, an eavesdropper can decrypt the data exchanged between the D2D user devices. This paper focuses on the efficient resource allocation in D2D communication that improves sum rate, computational complexity, and information secrecy capacity by reusing cellular user’s (CU) resources. Here, we propose an efficient resource allocation scheme incorporating the artificial intelligence (AI) algorithm-based autoencoder that overcomes information ambiguity when there is the same weight in the data rate matrix of the Hungarian algorithm. First, we identify cellular and D2D users’ communication data rates and generate a data rate matrix using the channel gain, received signal, and signal-to-interference-plus-noise ratio (SINR) values. The generated data rate matrix is passed as an input into an autoencoder. An autoencoder performs the dimensionality reduction using latent space and reconstructs the optimal cost matrix. The AI model’s result shows that the autoencoder achieves 83% training and 90% validation accuracy using the mean squared error (MSE) loss function. Then, we determine the optimal cost using the Hungarian algorithm. As we aim to maximize the sum rate and reduce the computation cost, our simulation result shows that the resource allocation performs efficiently using the proposed scheme.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Ambient Virtio: IO Virtualization for Seamless Integration and Access of Devices in Ambient Computing

Ambient computing creates a digital environment where a variety of technologies, including hardware, software, and even machine learning, are integrated into most useful and autonomous digital device that we want to use right here right now. The main obstacle for this computing is a barrier of vendor-specific details, programming interfaces, and access procedure even though all the things come to have network interfaces and interact with each other through the Internet. However, this configuration defies the expectation that ambient computing can reduce the demand for human attention. In this article, we propose <i>Ambient Virtio</i>, a device virtualization technology that virtualizes devices with the <small>virtio</small> framework. This allows devices in close proximity to users to be accessed without human intervention and integrated into metadevices that users want to create and access on the fly. We newly defined a <small>virtio</small>-based backend device named <small>virtio-ambient</small> and also constructed an ambient device architecture for virtual machines to read and write the remote device only with built-in system calls. We completely implemented the system and performed comparative evaluation with the existing application-level data networking system.

Analysis of Error Rate and Capacity in the Presence of I/Q Imbalances Over an Impulsive Noise Channel

Many state-of-the-art unmanned aerial vehicle-assisted communication systems adopt quadrature amplitude modulation (QAM) and zero intermediate frequency (IF) architecture. In zero-IF QAM devices, in-phase/quadrature (I/Q) amplitude and phase imbalances cause severe performance degradation. In addition, the performance of the devices in close proximity to the impulsive noise sources can also be significantly degraded. In this article, we derive and analyze the symbol error rate (SER) of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -ary square QAM and the channel capacity of a system in the presence of I/Q imbalance over an impulsive noise channel. We first present an exact expression for the joint probability density function (PDF) of the demodulated I/Q signals affected by impulsive noise. By using the derived PDF, we then provide exact expressions for SER and SER floor of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -ary square QAM and average and asymptotic channel capacities of the system with I/Q amplitude and phase imbalances over an impulsive noise channel. We validate the theoretical results through computer simulations.

A Survey of Resource Management in D2D Communication for B5G Networks

Due to the vast rise of users, the variety of application requirements, and the enormous necessity for ubiquitous connectivity anytime and anywhere, integration of new technologies and ideas are required. Therefore, Device to Device (D2D) communication technology has been regarded as a critical concern for Beyond Fifth Generation (B5G) networks. In the D2D technology, devices in close proximity may connect without using a Base Station (BS). Consequently, improving the capacity and performance of traditional networks. The major objectives are to accomplish a high geared data rate, increase the network coverage with low latency, and improve Energy Efficiency (EE) and Spectrum Efficiency (SE). However, the coexistence of D2D technology in the traditional network can pose many issues and challenges, such as power control, interference, spectrum access, security, and mode selection. Consequently, a comprehensive survey that includes an investigation of the current issues in D2D technology is required. In this survey, we present a new categorization of the most important aspects of resource management in B5G D2D communication, such as power allocation, spectrum allocation, interference management, and mode selection. Furthermore, we discuss the limitations and open issues of the current techniques and propose a model approach and future directions for D2D communication in B5G wireless networks. Our survey aims to assist researchers in exploring and improving D2D communication within the framework of forthcoming wireless networks.

Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) assists clouds to handle enormous tasks from mobile devices in close proximity. The edge servers are not allocated efficiently according to the dynamic nature of the network. It leads to processing delay, and the tasks are dropped due to time limitations. The researchers find it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes. The challenge relies on the offloading decision on selection of edge nodes for offloading in a centralized manner. This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge servers. Initially, a task-offloading problem needs to be formulated based on the communication and processing. Then offloading decision problem is solved by deep analysis on task flow in the network and feedback from the devices on edge services. The significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory (b-LSTM). The simulation is done in the Edgecloudsim environment, and the outcomes show the significance of the proposed idea. The processing time of the anticipated model is 6.6 s. The following performance metrics, improved server utilization, the ratio of the dropped task, and number of offloading tasks are evaluated and compared with existing learning approaches. The proposed model shows a better trade-off compared to existing approaches.

Design and Applications of Integrated Transducers in Commercial CMOS Technology

Monolithic integration of Microelectromechanical Systems (MEMS) directly within CMOS technology offers enhanced functionality for integrated circuits (IC) and the potential improvement of system-level performance for MEMS devices in close proximity to biasing and sense circuits. While the bulk of CMOS-MEMS solutions involve post-processing of CMOS chips to define freely-suspended MEMS structures, there are key applications and conditions under which a solid, unreleased acoustic structure composed of the CMOS stack is preferred. Unreleased CMOS-MEMS devices benefit from lower barrier-to-entry with no post-processing of the CMOS chip, simplified packaging, robustness under acceleration and shock, stress gradient insensitivity, and opportunities for frequency scaling. This paper provides a review of advances in unreleased CMOS-MEMS devices over the past decade, with focus on dispersion engineering of guided waves in CMOS, acoustic confinement, CMOS-MEMS transducers, and large signal modeling. We discuss performance limits with standard capacitive transduction, with emphasis on performance boost with emerging CMOS materials including ferroelectrics under development for memory.

P2P mobility management for seamless handover using D2D communication in B5G wireless technology

Since the steep rise in mobile users and data rates, handling the higher rates of data is of major concern for any future wireless networks, mobile service providers are striving hard to handle such huge data with the efficient handover. Also, they are seeking techniques to establish communication between the user equipment (UE) without routing through the base station. Device-to-Device(D2D) technique can be used to establish the connection without routing through the base station. Therefore, the Peer-to-Peer(P2P) method which utilizes the D2D concept is expected to solve this problem with the seamless handover for future beyond 5G (B5G) wireless technology. In this paper, P2P mobility management using D2D communication for seamless handover is proposed. It is expected to be a key feature in the upcoming B5G technology. It allows direct communication between two devices in close proximity to each other. The seamless handover in P2P considers two operating modes, either Direct or D2D. In the latter, ground eNB (gNB) is identifying one nearby UE which is already either in connection or not active to configure as a relaying device to transfer data to the UE from the gNB. During the handover, the gNB is identifying the best operating mode (D2D or direct) for transferring the data towards the receiver UE. The proposed direct and D2D mode handover is verified and validated. The performance validation shows that D2D mode based handover provides better handover with negligible latency compared to the direct mode.

Fog-Enabled Joint Computation, Communication and Caching Resource Sharing for Energy-Efficient IoT Data Stream Processing

Fog/edge computing has been recently regarded as a promising approach for supporting emerging mission-critical Internet of Things (IoT) applications on capacity and battery constrained devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation, communication and caching resource sharing (i.e., 3C resources), it enables great potentials in low-latency and energy-efficient IoT task execution. To efficiently exploit 3C resources of fog devices in proximity, we propose F3C, a fog-enabled 3C resource sharing framework for energy-efficient IoT data stream processing by solving an energy cost minimization problem under 3C constraints. Nevertheless, the minimization problem proves to be NP-hard via reduction from a Generalized Assignment Problem (GAP). To cope with such challenge, we propose an efficient F3C algorithm based on an iterative task team formation mechanism which regards each task's 3C resource sharing as a subproblem solved by the elaborated min cost flow transformation. Via utility improving iterations, the proposed F3C algorithm is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our F3C algorithm can achieve superior performance in energy saving compared to various benchmarks.

Secrecy capacity and energy efficiency in D2D-enabled cellular networks

Device-to-device (D2D) communication is introduced in cellular networks to allow devices in close proximity to communicate directly with each other, freeing up spectral and other system resources. We propose a scheme that promotes the coexistence of cellular users (CUs) and D2D users (DUs). DUs are allowed to access the CU channel for D2D communications, enhancing the spectral efficiency. On the other hand, DU is expected to protect the secrecy of CU’s data by transmitting artificial noise to jam any potential eavesdroppers near the CU link, which may include other DUs. This enhances the cellular secrecy capacity at the expense of DU’s energy, which motivates us to assess the CU’s secrecy capacity and DU’s energy efficiency of this scheme. We have identified the causes and conditions of increased energy efficiency under certain secrecy capacity requirement, e.g. an optimal DU’s sensing duration that results in maximal energy efficiency. Such information is important for the planning of an efficient access control and scheduling scheme for the DU links in cellular networks.

Exploiting Proxy Sensing for Efficient Monitoring of Large-Scale Sensor Networks

Large networks of IoT devices, each consisting of one or more sensors, are being increasingly deployed for comprehensive real-time monitoring of cyber-physical systems. Such networks form an essential component of the emerging edge computing paradigm and are expected to increase in complexity and size. The physical phenomenon sensed by different sensors (within the same or different IoT devices in close proximity) often have relationships that makes them correlated. This is a form of proxy sensing that can be exploited for achieving better energy efficiency and higher robustness in monitoring. In this article, we explore how a set of sensors can optimize its data collection rates efficiently in a semi-distributed manner and yet provide the advantages of autonomy, relative isolation, and distributed control that is essential in a large-scale network.

Incentive-Aware Micro Computing Cluster Formation for Cooperative Fog Computing

Fog computing is envisioned as a promising approach for supporting emerging computation-intensive applications on capacity and battery constrained mobile Internet of Things (IoT) devices. Technically speaking, a massive crowd of devices in close proximity can be harvested and collaborate for computation and communication resource sharing. Hence fog computing enables significant potentials in low-latency and energy-efficient mobile task execution. However, without an efficient incentive mechanism to stimulate resource sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. In particular, to efficiently achieve mutually beneficial task execution, the proposed mechanism groups the devices into multiple micro computing clusters (MCCs). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of their performances no worse than local execution or execution in the fog server. The solution to the MCC formation is devised by both centralized and decentralized schemes and further proven to admit nice properties such as top coalition, core solution, individual rationality and computational efficiency. Extensive numerical studies demonstrate the superior performance of our MCC formation mechanisms.

MOERA: Mobility-Agnostic Online Resource Allocation for Edge Computing

To better support emerging interactive mobile applications such as those VR-/AR-based, cloud computing is quickly evolving into a new computing paradigm called edge computing. Edge computing has the promise of bringing cloud resources to the network edge to augment the capability of mobile devices in close proximity to the user. One big challenge in edge computing is the efficient allocation and adaptation of edge resources in the presence of high dynamics imposed by user mobility. This paper provides a formal study of this problem. By characterizing a variety of static and dynamic performance measures with a comprehensive cost model, we formulate the online edge resource allocation problem with a mixed nonlinear optimization problem. We propose MOERA, a mobility-agnostic online algorithm based on the “regularization” technique, which can be used to decompose the problem into separate subproblems with regularized objective functions and solve them using convex programming. Through rigorous analysis we are able to prove that MOERA can guarantee a parameterized competitive ratio, without requiring any a priori knowledge on input. We carry out extensive experiments with various real-world data and show that MOERA can achieve an empirical competitive ratio of less than 1.2, reduces the total cost by $4 \times$4× compared to static approaches, and outperforms the online greedy one-shot solution by 70 percent. Moreover, we verify that even being future-agnostic, MOERA can achieve comparable performance to approaches with perfect partial future knowledge. We also discuss practical issues with respect to the implementation of our algorithm in real edge computing systems.

Non-Orthogonal Resource Sharing Optimization for D2D Communication in LTE-A Cellular Networks: A Fractional Frequency Reuse-Based Approach

To handle the fast-growing demand for high data rate applications, the capacity of cellular networks should be reinforced. However, the available radio resources in cellular networks are scarce, and their formulation is expensive. The state-of-the art solution to this problem is a new local networking technology known as the device-to-device (D2D) communication. D2D communications have great capability in achieving outstanding performance by reusing the existing uplink cellular channel resources. In D2D communication, two devices in close proximity can communicate directly without traversing data traffic through the evolved-NodeB (eNB). This results in a reduced traffic load to the eNB, reduced end-to-end delay, and improved spectral efficiency and system performance. However, enabling D2D communication in an LTE-Advanced (LTE-A) cellular network causes severe interference to traditional cellular users and D2D pairs. To maintain the quality of service (QoS) of the cellular users and D2D pairs and reduce the interference, we propose a distance-based resource allocation and power control scheme using fractional frequency reuse (FFR) technique. We calculate the system outage probability, total throughput and spectrum efficiency for both cellular users and D2D pairs in terms of their signal-to-interference-plus-noise ratio (SINR). Our simulation results show that the proposed scheme reduces interference significantly and improves system performance compared to the random resource allocation (RRA) and resource allocation (RA) without sectorization scheme.

Stable Device Pairing for Collaborative Data Dissemination With Device-to-Device Communications

With the rapid expansion of Internet-of-Things (IoT), tremendous traffic produced by a vast number of IoT devices is being injected into networks and straining their capacity. To address the challenge, device-to-device (D2D) communications offer a promising technique that relieves network overloading by localizing traffic between devices. In this paper, we investigate how to exploit D2D communications to support data dissemination and offload traffic. In particular, we focus on an important problem that aims to effectively pair request devices with cache devices in close proximity. Due to the interference among D2D links, we prove that this device pairing problem is NP-hard and thus requires an approximation algorithm to solve it efficiently. Here, we propose a three-step approach, in which the first step uses Lagrangian relaxation to obtain an upper bound solution, the second step derives a feasible solution from the initial pairing and further augments it, and the last step uses a swapping algorithm to refine the pairing to guarantee its stability. The proposed approach is proved to converge to a two-sided exchange stable matching. Extensive simulation results show that our three-step approach performs closely to the optimal solution and achieves significant performance gain over the existing schemes.

Attack and Countermeasure on Interlock-Based Device Pairing Schemes

In recent years, researchers have proposed several secure device pairing schemes that allow mobile devices in close proximity to establish a trusted communication channel between them without sharing any secret in advance. These schemes use the correlation of some physical measurements (magnetic field, acceleration, etc.,) made independently by the two pairing devices to reconcile them. Their security against a Man-in-the-Middle (MitM) attacker relies on the difficulty for the MitM attacker to obtain a measurement data similar to the two pairing devices. As a key step in the reconciliation process, an interlock protocol is used in several recent schemes (e.g., Magpairing and ShaVe) to ensure that the measurement data is not leaked. However, the present paper points out that these schemes apply the interlock protocol improperly, making themselves vulnerable to MitM attacks. The analysis and experimental results show that the proposed MitM attack almost surely succeeds with very low computation overhead. We also propose countermeasures on the presented attack.

Drop computing: Ad-hoc dynamic collaborative computing

Mobile applications nowadays generally consist of a frontend component running on the device, and a backend component running on the cloud that performs the larger computations. However, this usage model leads to high costs for developing the application (since a cloud infrastructure that should scale to the number of users must be maintained), and to a potentially bad user experience (if the latency is high or the users employ mobile broadband they pay for). Thus, we introduce the Drop Computing paradigm, which proposes the concept of decentralized computing over multilayered networks, combining cloud and wireless technologies over a social crowd formed between mobile and edge devices. Mobile devices and people interconnect to form ad-hoc dynamic collaborations to support the equivalent of a crowd-based edge multilayered cloud of clouds, where the capabilities of any mobile device are extended beyond the local technology barriers, to accommodate external resources available in the crowd of other devices. Thus, instead of every data or computation request going directly to the cloud, Drop Computing employs the mobile crowd formed of devices in close proximity for quicker and more efficient access. Devices in the mobile crowd are leveraged for requesting already downloaded data or performing computations, and the cloud acts as the second (or even third) option. We present a proof-of-concept implementation of Drop Computing and show, through simulations, that it is feasible for real-life usage, since it is able to drastically reduce costs while not affecting or even improving the user experience.

Energy-Efficiency Maximization with Non-linear Fractional Programming for Intelligent Device-to-Device Communications

With the exponential growth of wireless users and their traffic demands, it is greatly increasing for the demand of the scarce spectrum resources in the communication networks. In order to enhance the performance of the wireless networks such as end-to-end delay, energy efficiency and throughput, the device-to-device (D2D) communication has been attracted more attention because the two devices in close proximity can communicate directly without traversing the central base station. However, most of users are very sensitive to the battery. Therefore, we aim to maximize the energy efficiency of wireless communication system in the context of underlaying device-to-device communication in this paper, We focus on the formulated power control and resource allocation problem which is non-convex in the fractional form. We reduce it from the power allocation of all users to the joint power and subchannel allocation of D2D users. Then, we tackle it by an iterative approximation algorithm leveraging to the properties of fractional programming. There are two studied cases for the subchannel allocation. One can be solved by the penalty function approach, and the other can be solved by the dual decomposition as well as sub-gradient method. Accordingly, we propose a dual-based algorithm in general. Numerical simulations demonstrate that the proposed algorithms outperform the conventional algorithm in terms of the energy efficiency.

Array Termination Impacts in Advanced SRAM

An essential goal of the static random access memory (SRAM) array termination design is to both terminate as well as maintain a homogeneous environment for the active edge cells in the array. Local layout effects (LLEs) in the array termination design can exert influence on the active array SRAM devices in close proximity to the termination region, which can lead to undesirable inhomogenuities in the array. The impact of LLEs, originating from the array termination design, on SRAM read performance and $V_{\min }$ fail count, are examined using a 14-nm FinFET technology. Large-scale SRAM read performance statistics are analyzed to identify elevated read currents and low-voltage fail counts associated with the array termination. The root cause and modulating factors are explored, and potential solution paths are discussed.

Electromagnetic Compatibility and Current Pulse of Railgun

Electromagnetic (EM) interference and its suppression are one of the major issues in most current systems design. The electric device such as railgun generates the EM fields that unintentionally propagate away from the structure. Railgun that have significant amounts of radiated emissions may interfere with the normal operation of other devices in close proximity. Also during the accelerating run of rail launchers, and particularly during the ignition of the armature, the radiated EM fields can compromise the life of electronic devices, can influence the signal to noise ratio, and can generate electrical shock hazard. The short duration pulse signal, such as current pulse of railgun, switching and arcing between components of railgun is typically rich in frequency spectral contents. It should be noted that the propulsive force of current pulse in each time push the armature in motion direction and change the propagation parameters of railgun as a time varying antenna and can affect on the radiation pattern in the various frequency ranges. Furthermore, in each component of current pulse, armature is in special position in direction of motion along the barrel and the impedance of railgun that power source see in front is different with previous stage and radiation pattern is different too. In this paper, the relationship between pulse form of input current and electric field around railgun are analyzed and simulated with finite-element method, then by attention of the armature position in each time it will be investigated the radiation pattern of railgun.