Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

CHALLENGES FACED BY STUDENTS IN STUDYING TARKÎB WITH ZOOM PLATFORM DURING PANDEMIC

This paper discusses the difficulties of Arabic department students when dealing with Tarkîb al-Marfû’ât subject and the using of Zoom/forced technology due to the Covid-19 pandemic. Using the qualitative approach, this paper aimed to explore whether students were negatively impacted by switch of learning mode to online. Interviews were conducted to nine students who live in remote areas, and it revealed that those students experienced delay in learning the subject due to their lack of the internet availability. Teaching activities were not optimal due to signal interference so that the exposure to the relevant material was not comprehensive. Moreover the students also lacked understanding to the subject because of the specific difficulty level of the subject. The students missed additional explanations and material from the lecturer, even equipped examples. This paper contributes to educational policies in the ways Indonesian government addresses distance learning for students in Islamic higher education.

Minimization of errors in discrete wavelet filtering of signals during ultrasonic measurements and testing

Error minimizing methods for discrete wavelet filtering of ultrasonic meter signals are considered. For this purpose, special model signals containing various measuring pulses are generated. The psi function of the Daubechies 28 wavelet is used to generate the pulses. Noise is added to the generated pulses. A comparative analysis of the two filtering algorithms is performed. The first algorithm is to limit the amount of detail of the wavelet decomposition coefficients in relation to signal interference. The minimum value of the root mean square error of wavelet decomposition signal deviation which is restored at each level from the initial signal without noise is determined. The second algorithm uses a separate threshold for each level of wavelet decomposition to limit the magnitude of the detail coefficients that are proportional to the standard deviation. Like in the first algorithm, the task is to determine the level of wavelet decomposition at which the minimum standard error is achieved. A feature of both algorithms is an expanded base of discrete wavelets ‒ families of Biorthogonal, Coiflet, Daubechies, Discrete Meyer, Haar, Reverse Biorthogonal, Symlets (106 in total) and threshold functions garotte, garrote, greater, hard, less, soft (6 in total). The model function uses random variables in both algorithms, so the averaging base is used to obtain stable results. Given features of algorithm construction allowed to reveal efficiency of ultrasonic signal filtering on the first algorithm presented in the form of oscilloscopic images. The use of a separate threshold for limiting the number of detail coefficients for each level of discrete wavelet decomposition using the given wavelet base and threshold functions has reduced the filtering error.

Flexoelectricity Driven Fano Resonance in Slotted Carbon Nanotubes for Decoupled Multifunctional Sensing

Multifunctionality, interference-free signal readout, and quantum effect are important considerations for flexible sensors equipped within a single unit towards further miniaturization. To address these criteria, we present the slotted carbon nanotube (CNT) junction features tunable Fano resonance driven by flexoelectricity, which could serve as an ideal multimodal sensory receptor. Based on extensive ab initio calculations, we find that the effective Fano factor can be used as a temperature-insensitive extrinsic variable for sensing the bending strain, and the Seebeck coefficient can be used as a strain-insensitive intrinsic variable for detecting temperature. Thus, this dual-parameter permits simultaneous sensing of temperature and strain without signal interference. We further demonstrate the applicability of this slotted junction to ultrasensitive chemical sensing which enables precise determination of donor-type, acceptor-type, and inert molecules. This is due to the enhancement or counterbalance between flexoelectric and chemical gating. Flexoelectric gating would preserve the electron–hole symmetry of the slotted junction whereas chemical gating would break it. As a proof-of-concept demonstration, the slotted CNT junction provides an excellent quantum platform for the development of multistimuli sensation in artificial intelligence at the molecular scale.

Tinjauan Filosofis Egoisme dalam Komunikasi Politik di Era Pandemi Covid-19: Tantangan Kampanye Daring Pilkada Serentak Tahun 2020 di Indonesia

Indonesia has held simultaneous regional elections on 9th December 2020. In contrast to previous years, when the campaign became a moment for citizens to gather with their prospective leaders, the Covid-19 pandemic forced the limitation of face-to-face meetings in order to implement Covid-19 health protocols. The government through the General Elections Commission (KPU) has issued General Election Commission Regulation (PKPU) 13/2020 which has explicitly encouraged candidate to use digital media in political campaigns. This is an effort to encourage candidates to take advantage of the digital space in campaigning. Currently, the candidates already have social media, but its use has not become the main information channel in political communication. During this campaign period, the candidates have used social media as a channel of political communication, but the social media used is limited to conveying invitations or information that the candidate has attended an activity, so social media has not become the main information channel in campaigning. The lack of organizing an online campaign by this candidate viewed from a philosophical perspective of egoism, based on the idea that the public or prospective voters are more focused on themselves. Participating in a online campaign for prospective voters requires extra sacrifices such as paying for internet quota fees, it is more troublesome because prospective voters have to learn to operate a online video application, and there are no direct benefits. The challenges faced in implementing an online campaign include, firstly, because it is preferred by the community, it is believed that the community prefers to meet face-to-face with the prospective leader directly because it provides direct benefits to the community, secondly, it is right on target because an online election campaign will not attract people, new voters because it will only be followed by voters who firmly support the candidate. Thirdly, because of the lack of creativity from the campaign team due to the lack of innovation from the candidates for not building a team that campaigns boldly, in addition to innovation, infrastructure problems that the evaluation of signal interference and the uneven distribution of digital infrastructure in the regions have hampered the implementation of bold campaigns.

Research on Ultra-Wideband (UWB) Accurate Positioning under Signal Interference Based on Deep Learning

UWB (Ultra-Wideband) technology is also called "Ultra-Wideband", also known as pulse radio technology. UWB-based positioning technology has real-time indoor and outdoor accurate tracking capabilities, with high positioning accuracy, which can achieve centimeter-level or even millimeter-level positioning. Based on the provided anchor point ranging information, this paper analyzes normal and abnormal data and establishes an accurate positioning model. For task 1, preprocess the data, export the file, delete invalid data, and fill in missing values. For task 2, it is required to establish models for normal and abnormal data respectively. For task 3, it is consistent with the model obtained in task 2. The only difference is that the coordinates of the four anchor points used for the test data have changed. When the target coordinates are calculated, the anchor point coordinates can be replaced to obtain the model required by task 3. For task 4, use the processed data in task one to establish a mathematical model, and train the model through the integrated learning method to determine whether the collected signal is interference. For task 5, first use the integrated learning model in task four to eliminate the interference data in the data, import the eliminated data into the positioning model of task two for positioning, and add Kalman based on interference recognition to the static estimation algorithm.

Integration of fault-tolerant feature to OMIEEPB routing protocol in wireless sensor network

PurposeWhereas a human operator is hard to observe the networking infrastructure and its functions on a continuous basis, wireless sensor network (WSN) nodes must overcome faults and route the perceived data to the sink/base stations (BS). The main target of this research article is to ensure the fault-tolerance (FT) capability, especially for transmission of sensed data to its destination without failure. Thus, through this paper, a fuzzy-based subordinate support (FSS) system is introduced as an additional feature to the existing optimized mobile sink improved energy efficient Power-Efficient Gathering in Sensor Information Systems (PEGASIS)-based (OMIEEPB) routing protocol, which lacks focus on ensuring the FT capabilities to the selected leaders of the corresponding chain. The central focus of FSS is to prevent the incident of fault, especially to the cluster heads.Design/methodology/approachWSNs encounter several issues owing to random events or different causes such as energy exhaustion, negative influences of the deployed region, signal interference, unbalanced supply routes, instability of motes due to misalignments and collision, which ultimately intends the failure of the network. Throughout the past investigation periods, researchers gain an understanding of fault-tolerant strategies that may improve the data integrity or reliability, precision, energy efficiency, the life expectancy of the system and reduce/prevent the failure of deployed components. If that is the case, the maximum chances of data packets (sensed) needed to be transferred reliably and accurately to the sink node or BS are degraded.FindingsThe FSS system utilizes the fuzzy logic concepts that have been proved to be beneficial since it permits several parameters to be combined effectively and evaluated. Here, near-point, residual energy, total operation time and past average processing time are considered as vital parameters. Moreover, the FSS system ensures the key performance activities of the network, such as optimization of response time, enhancing the data transmission reliability and accuracy. Simulation-based experiments are carried out through the Mannasim framework. After several experimental executions, the outcome of the proposed system is analyzed through elaborated comparison with the advanced existing systems.Originality/valueFinally, it has been observed that the FSS system not only enhanced the FT features to OMIEEPB but also assures the improved accuracy level (>95%) with optimized response time (<0.09 s) during data communication between leaders and the normal nodes.

Construction of Quality Virtual Backbones with Link Fault Tolerance in Wireless Sensor Networks

Wireless sensor networks (WSNs) are extensively utilized in various circumstances. For applications, the construction of the virtual backbones (VBs) of WSNs has attracted considerable attention in this field. Generally, a homogeneous WSN is formulated as a unit disk graph (UDG), and the VB of the corresponding WSN is modeled as a connected dominating set (CDS) in the UDG. In certain applications, communication between sensors in a network may fail for various reasons, such as sensor movement, signal interference, and the appearance of obstacles. Consequently, a CDS in a UDG should possess fault tolerance on the edges. In this paper, we introduce a new concept called the 2 edge-connected 2 edge-dominating set ( 2 , 2 -ECDS); then, we design an approximation algorithm for computing 2 , 2 -ECDSs in UDGs, the performance ratio of which is 30.51. By means of simulations, we compare our algorithm and existing algorithms in terms of the CDS size, running time, success rate, and network lifetime. The simulation results indicate that our algorithm exhibits better performance and is more suitable for constructing a VB with edge fault tolerance in a WSN.

GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.