Communication Algorithms Research Articles

TEACHING USING THE ARDUINO PLATFORM APPLIED TO THE CONCEPT Of IoT AS SUPPORT TO MICROCONTROLLER CLASS

Resumo With the emergence of the concept of IoT - the Internet of Things, it has become virtually impossible not to contextualize it in programming classes of electronic devices, such as processors and microcontrollers today. Thus, this work shows the analysis of a teaching methodology aimed at including in one of these courses an introduction to IoT, not excluding traditional hardware clarifications, such as the use of old microcontrollers such as 8051 (INTEL) and/or PIC (Microchip), thus not impairing, initial objectives proposed. The ARDUINO Platform, a powerful and free development tool, was used to support the implementation of this proposal. This system allows a hardware abstraction through modules called Shields for industrial applications and can run communication algorithms in real-time, allowing to embark as a product some of the concepts of IoT. This article discusses the experience obtained with the use of such methodology applied to the microcontrollers class in a Course Technology in Industrial Automation of a Federal School, synthesized after the analysis of questionnaires to teachers and students involved in this course. The results of this methodology were positive, motivating the desire to better understand new hardware devices and how to program them, besides creating an innovative spirit in the projects of the graduates of this course concerning previous classes.

An optimisation of allreduce communication in message-passing systems

Collective communication, namely the pattern allreduce in message-passing systems, is optimised based on measurements at the installation time of the library. The algorithms used are set up in an initialisation phase of the communication, as so-called persistent collective communication, introduced in the message-passing interface (MPI) standard. Part of our allreduce algorithms are the patterns reduce_scatter and allgatherv which are also considered standalone. For the allreduce pattern for short messages the existing cyclic shift algorithm (Bruck’s algorithm) is applied with a prefix operation. For allreduce and long messages our algorithm is based on reduce_scatter and allgatherv, where the cyclic shift algorithm is applied with a flexible number of communication ports per node. The algorithms for equal message sizes are used with non-equal message sizes together with a heuristic for rank reordering. Medium message sizes are communicated with an incomplete reduce_scatter followed by allgatherv. Furthermore, an optional recursive application of the cyclic shift algorithm is applied. All algorithms are applied at the node level. The data is gathered and scattered by the cores within the node and the communication algorithms are applied across the nodes. In general, our approach outperforms the non-persistent counterpart in established MPI libraries by up to one order of magnitude or shows equal performance, with a few exceptions of number of nodes and message sizes.

An optimized power allocation algorithm for cognitive radio NOMA communication

The primary objective of cognitive radio network is to effectively utilize the unused spectrum bands. In cognitive radio networks, spectrum sharing between primary and secondary users is accomplished using either underlay or interweave cognitive radio approach. Non orthogonal multiple access (NOMA) is the proven technology in the present wireless developments, which allows the coexistence of multiple users in the same orthogonal block. The new paradigm cognitive radio NOMA (CR-NOMA) is one of the potential solutions to fulfill the demands of future wireless communication. This paper emphasizes on practical implementation of NOMA in cognitive radio networks to enhance the spectral efficiency. The goal is to increase the throughput of the secondary users satisfying the quality of service (QOS) requirements of primary users. To achieve this, we have presented the optimized power allocation strategy for underlay downlink scenario to support the simultaneous transmission of primary and secondary users. Furthermore, we have proposed QOS based power allocation scheme for CR-NOMA interweave model to support the coexistence of multiple secondary networks. Also, the changes adopted in implementing superposition coding (SC) and successive interference cancellation (SIC) for CR-NOMA are highlighted. Finally, simulation results validate the mathematical expressions that are derived for power allocation coefficient and outage probability.

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives.

In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks.

Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment

The performance prediction of an optical communications link over maritime environments has been extensively researched over the last two decades. The various atmospheric phenomena and turbulence effects have been thoroughly explored, and long-term measurements have allowed for the construction of simple empirical models. The aim of this work is to demonstrate the prediction accuracy of various machine learning (ML) algorithms for a free-space optical communication (FSO) link performance, with respect to real time, non-linear atmospheric conditions. A large data set of received signal strength indicators (RSSI) for a laser communications link has been collected and analyzed against seven local atmospheric parameters (i.e., wind speed, pressure, temperature, humidity, dew point, solar flux and air-sea temperature difference). The k-nearest-neighbors (KNN), tree-based methods-decision trees, random forest and gradient boosting- and artificial neural networks (ANN) have been employed and compared among each other using the root mean square error (RMSE) and the coefficient of determination (R2) of each model as the primary performance indices. The regression analysis revealed an excellent fit for all ML models, indicative of their ability to offer a significant improvement in FSO performance modeling as compared to traditional regression models. The best-performing R2 model found to be the ANN approach (0.94867), while random forests achieved the most optimal RMSE result (7.37).

Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

We advocate a new resource allocation framework, which we call resource rationing, for wireless federated learning (FL). Unlike existing resource allocation methods for FL, resource rationing focuses on balancing resources across learning rounds so that their collective impact on FL performance is explicitly captured. This new framework can be integrated seamlessly with existing resource allocation schemes to optimize the convergence of FL. In particular, a novel “lat-er-is-better” principle is at the front and center of resource rationing and is validated empirically in several instances of wireless FL. We also point out technical challenges and research opportunities that are worth pursuing. Resource rationing highlights the benefits of treating the emerging FL as a new class of service that has its own characteristics, and designing communication algorithms for this particular service.

Role of the radiologist at HCC multidisciplinary conference and use of the LR-TR algorithm for improving workflow.

Multidisciplinary conferences (MDCs) play a major role in management and care of oncology patients. Hepatocellular carcinoma (HCC) is a complex disease benefiting from multidisciplinary discussions to determine optimal patient management. A multitude of liver-directed locoregional therapies have emerged allowing for more options for treatment of HCC. A radiologist dedicated to HCC-MDC is an important member of the team contributing to patient care in multiple ways. The radiologist plays a key role in image interpretation guiding initial therapy discussions as well as interpreting post-treatment imaging following liver-directed therapy. Standardization of image interpretation can lead to more consistent treatment received by the patient as well as accurate assessment of transplant eligibility. The radiologist can facilitate this process using structured reporting that is also supported by stakeholders involved in interdisciplinary management of liver diseases. The Liver Imaging Reporting and Data System (LI-RADS), is a living document which offers a standardized reporting algorithm for consistent communication of radiologic findings for HCC screening and characterization of liver observations in patients at risk for HCC. The LI-RADS post-treatment algorithm (LR-TR algorithm) has been developed to standardize liver observations following liver-directed locoregional therapy. This review article focuses on the role of the radiologist at HCC-MDC and implementation of the LR-TR algorithm for improving workflow.

Improving Performance in Trust Based Multi-Link-Rat Communication for V2x Heterogeneous Vehicular Network

The dynamic nature of vehicular networks imposes a lot of challenges in multi hop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Here propose a distributed heterogeneous V2V communications algorithm that allows each vehicle to dynamically select the RAT that is more suitable at each point in time. Multi-link is the capability of a device to communicate using multiple wireless links simultaneously. Multi-RAT is the capability of a device to communicate using different RATs. To propose a Predictive Routing based on Markov Model (PRM) to ensure more reliable and timely data transmissions in VANETs. In the case of accident management, emergency messages may be sent to a pre-determined road rescue site upon the occurrence of an accident, such as a crash on the highway during a snow day and a car spontaneous combustion due to the stored explosives. PRM can facilitate the transmission of real-time information from vehicles to a road traffic controller for more efficient traffic management. Rather than using passive traffic detection through sensors, the real-time reports of traffic data through V2V and V2I can avoid the costs of installing and maintaining a large number of sensors.

Adaptive Clustering Algorithm for Stable Communication in Vanet

Clustering in VANET is important for recent days due to more number of vehicle present in urban area. In future Intelligent Transportation System (ITS), VANET is capable of providing safety related applications, Internet accessing and various user applications for drivers and passengers. Clustering based topology an efficient data interaction among vehicles, can be applied to the groups of vehicle nodes in geographical vicinity together that supports direct interaction between the intercluster data interaction through cluster heads (CHs). In existing method, so many algorithms developed Related to clustering based VANET communication, but due to high mobility of vehicle and their parameters like position, velocity and acceleration is improve the complexity and reduce the performance. In this research, we propose an adaptive self-learning approach based hybrid clustering. In hybrid clustering, the process divided in three steps. In first step Type1, it used for Road side unit (RSU) act as a static cluster head. In second step, divide the multiple zones with whole area and apply zone sensitive based clustering algorithm. It is used for predict the efficient centroid/weight value for each Vehicles. Finally in the third step, from the above algorithm, in neuro- fuzzy training samples we got. Neuro fuzzy prediction based cluster head selection was applied. Percentages of cluster-Head for each zone assign the purpose of reduces the end-to-end delay and reduce congestion. In simulation results shows that demonstrate the efficiency of proposed algorithm compare to existing method.

A Study on A Smart Firefighting Helmet Capable Of Video / Audio Transmission Based On The Firefighting Standard Disaster System

In this paper, in order to effectively respond and rescue lives in a disaster scene, the site and the command center design a smart firefighting helmet to exchange information and make accurate decisions, and propose an algorithm that can transmit and receive video and audio through it. do. Recently, there have been cases in which a rapid response has not been made due to the situation inside the disaster site and delay in communication. So, we propose a design and communication algorithm of a smart firefighting helmet that can transmit voice and video using a firefighting helmet and communicate without a separate radio operation.

Strengthening and supporting nurses' communication with mechanically ventilated patients in the intensive care unit: Development of a communication intervention.

Nurse-patient communication in intensive care units is challenged by the fact that patients are voiceless due to intubation and mechanical ventilation. Difficult communication affects nurses negatively, and it requires knowledge and expertise to facilitate communication in this complex and technologically tense setting. Augmentative and alternative communication has been suggested as a way of optimising communication; several approaches can be combined in a multi-component intervention. Also, a communication algorithm has been proposed as a way of providing structure in patient communication. To enhance transparency and avoid poorly reported interventions, this paper describes the process, rationale and reflections behind developing a communication intervention called the ICU-COM. To present the development process of a communication intervention prototype that aims to support and strengthen nurses' communication with mechanically ventilated patients in an intensive care unit. The Medical Research Council's framework for developing complex interventions in health was applied. The approach was target-population centred. The intervention was developed and tailored to four intensive care unit departments at Aarhus University Hospital in Denmark. Intensive care nurses and various experts, namely, speech-language pathologists, graphic designers, a software company, the local Centre for E-learning and nurse specialists were involved in its development. An intervention consisting of: 1) a multi-component communication bundle, 2) delivery of the bundle via a teaching session and 3) initial implementation via nurse communication guides was developed. The communication bundle contained: 1) a communication strategy with a BASIS framework and algorithm, 2) a nurse education programme and 3) low-tech and high-tech communication tools. A systematic approach was applied in the development process. However, the acceptability and feasibility of the intervention is at present unknown.

Slam algorithm for multi-robot communication in unknown environment based on particle filter

To solve the SLAM (simultaneous localization and map building) of mobile robots, a multi-robot SLAM algorithm based on particle filter for communication in unknown environment is proposed. In the standard particle filter, the incremental map construction method based on point-line consistency is introduced to preserve the hypothesis of the line segment feature map in each particle, and the importance function is introduced to the observation information. On this basis, sampling map fusion algorithm extracts ORB (oriented FAST and rotated BRIEF) features of local map constructed by a single robot and finds the optimal matching points, calculates the homography matrix of local map optimal matching point set, determines the optimal affine transformation parameters among point sets, so as to complete the local map fusion, and realize the multi-robot communication based on SLAM in unknown environment. The results show that the algorithm achieves high-precision map fusion and it is an effective SLAM construction algorithm for multiple machines.

HEPSO: an efficient sensor node redeployment strategy based on hybrid optimization algorithm in UWASN

In Underwater Acoustic Sensor Network (UWASN), node redeployment strategy is utilized to handle the reliable network coverage. The sensors deployed in the underwater are used to intellect the area and collected information is moved to the sink node. Node redeployment strategy is essential for the nodes which are placed outside the monitoring area in UWASN. In this paper, the node redeployment strategy is performed based on the hybrid Emperor Penguin Optimization (EPO) algorithm with Particle Search Algorithm (PSO) for better underwater acoustic communication and the proposed method is named as HEPSO. This hybridization is performed to reduce node failure rate and network energy consumption rate by optimally place the sensor nodes in underwater acoustic communication. The stability of the network topology is guaranteed by this algorithm and it enhances the node redeployment strategy by calculating the fitness function for each and every node. The implementation of the proposed algorithm is carried out by the MATLAB platform. The performance parameters like network coverage rate, network connectivity rate, network lifetime, number of nodes outside monitored space and total movement distance of nodes are evaluated and related with current methods like NRBSCT (Node Redeployment Based on Stratified Connected Tree) and MRNR (Moving Redundancy Nodes Redeployment) strategy.

Algorithm for secure communication of wireless network based on node energy control

This paper analyzes the security algorithm and energy cost of wireless sensor networks in depth, and designs and implements a series of energy-optimized security solutions to ensure the secure establishment and operation of wireless sensor networks. This paper proposes an improved Ad hoc network routing protocol based on energy control. It introduces a low-energy balanced routing algorithm to reduce routing transmission energy consumption, balance network traffic, and improve the energy control performance of network routing protocols. The protocol uses a cross-layer design in this way, the route selection combines the information of the link the remaining energy. A joint function is formed by the transmit power level remaining energy. The joint function of all nodes on the path is used as the basis for route selection and applied to the route discovery stage. At the same time, the protocol introduces edge degree parameters in the establishment process, the idea of minimum energy consumption path and the introduction of energy consumption ratio parameters in the cluster skull backbone network generation process are adopted to realize the energy optimization of the path establishment process. At the same time, the protocol uses the message interaction mechanism in the path establishment process to implement a node security authentication scheme based on secret shared information without adding any routing communication messages, which effectively prevents the passive and active attacks of the attacker on the network. The results of simulation experiments prove that the secure routing protocol achieves the network’s balanced energy consumption while ensuring the secure communication of the network, and solves the energy problem.

Reconfiguration algorithms for high precision communications in time sensitive networks: Time-aware shaper configuration with IEEE 802.1Qcc

As new networking paradigms emerge for different networking applications, e.g., cyber-physical systems, and different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical traffic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet-switched networking protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) Task Group is developing protocol standards to provide deterministic properties, i.e., eliminates non-deterministic delays, on Ethernet based packet-switched networks. In particular, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time-Aware Shaper (TAS), can be used to manage and control Scheduled Traffic (ST) streams with periodic properties along with Best-Effort (BE) traffic on the same network infrastructure. We investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and precisely configure TAS enabled switches (with transmission windows governed by Gate Control Lists (GCLs) with Gate Control Entries (GCEs)) ensuring ultra-low bounded latency, zero packet loss, and minimal jitter for ST TSN traffic. We examine both a centralized network/distributed user model (hybrid model) and a fully-distributed (decentralized) 802.1Qcc model on a typical industrial control network with the goal of maximizing the number of ST streams.

Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient’s information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an Adaptive Genetic Algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The proposed model can also prevent attacks, such as steganalysis or RS attacks.

Trust-based lightweight security protocol for device to device multihop cellular communication (TLwS)

D2D communication is an integral part of LTE-Advance and 5G cellular networks, has gained a lot of popularity in recent years as it offers high speed, extended coverage, and ubiquitous connectivity. Besides basic function that is direct communication without the need of Base Station (BS), D2D allows small communicating device to act as intermediate device not only for forward the traffics but also permits the sharing of resources such as spectrum, services, data and social contents for any users that are near the proximity range. With the provision of devices to provide such services and act as authorization devices give rise to vulnerabilities in terms of security and trust, therefore secure mutual authentication is mandatory to mitigate any security threats. In this paper, certificateless trust based lightweight security scheme (TLWS) for D2D multihop communication is proposed. This proposed scheme utilized elliptic curve and Elgamal cryptosystem assisted with secure hashing algorithm, timestamps, and blindfold challenge for secure communication and key agreements. In addition, we prove that TLWS provides secure mutual authentication using the broadly accepted Burrows–Abadi–Needham logic and shows that the proposed scheme is protected against replay attacks and Man in the middle attack. Overall, TLWS offers better security and functionality features, and the communicationandcomputationaloverheadsarecomparablewiththerelatedschemes.Therefore, TLWS is applicable to mobile environment efficiently.

WITHDRAWN: Machine learning based wireless acoustic communication with enhanced performance

Abstract Wireless Acoustic Communication (WAC) is an area that has been developed with different applications. The acoustic channel underwater is very unique and depends on the environment. The WAC channel has limited bandwidth and a strong Doppler effect in relation to wireless radio communication. Machine learning based channel estimation and equalisation has been shown in recent literature to have advantages over conventional (equalising) technology in WAC communication. Since it was hard to build algorithms for WAC communication, ML can be advantageous, because it was difficult to find general channel models. This research is intended to study whether ML-based estimation and equalisation can give enhanced efficiency as part of an advanced physical layer structure. In the analysis the supervised ML is used to increase the bit error rate using a deep neural network and recurring neural network. For the analysis of a wide variety of channels a channel simulator with environmental feedback is used. The simulations are used for the testing of ML environments. It is demonstrated that ML conducts conventional techniques in extremely time-changing channels when trained by the channel beforehand. However, the use of ML without previous channel knowledge did not improve efficiency.

Adaptive Beam forming algorithms for wireless Communication using LABVIEW

Adaptive antenna arrays are being developed to cope with the high capacity requirement of the 5G wireless communications systems. As adaptive antenna arrays focus narrow high gain beams towards the desired users and nulls towards the unwanted interferers, both coverage and capacity of the network can be improved. The capacity of a Wireless network depends on the size of the antenna array, the beam pattern used and the speed with which the beam-former and Direction of Arrival estimator. To achieve the capacity demand required by a growing number of subscribers, smart antenna systems are used in the present cellular systems. In these systems cell-splitting and sectoring techniques are adopted to improve the capacity. The main disadvantage of these techniques is that decrease in trunking efficiency. It has been established that the smart antenna systems will provide a better solution for the capacity problem. Development of smart antenna includes design of antenna array and adjusting the incoming signal by changing the weights of the amplitude as well as phase using efficient Digital Signal processing algorithms using LabVIEW.

Fluid dynamics-based distance estimation algorithm for macroscale molecular communication

Many species, from single-cell bacteria to advanced animals, use molecular communication (MC) to share information with each other via chemical signals. Although MC is mostly studied in microscale, new practical applications emerge in macroscale. It is essential to derive an estimation method for channel parameters such as distance for practical macroscale MC systems which include a sprayer emitting molecules as a transmitter (TX) and a sensor as the receiver (RX). Due to the similarity between sneezing/coughing and spraying mechanisms, these practical systems have the potential to be applied in modeling airborne pathogen (viruses, bacteria, etc.) transmission with a MC perspective where an infected human emitting pathogen-laden droplets is considered as a TX. In this paper, a novel approach based on fluid dynamics is proposed for the derivation of the distance estimation in practical MC systems. According to this approach, transmitted molecules are considered as moving and evaporating droplets in the MC channel. With this approach, the Fluid Dynamics-based Distance Estimation (FDDE) algorithm which predicts the propagation distance of the transmitted droplets by updating the diameter of evaporating droplets at each time step is proposed. FDDE algorithm is validated by experimental data. The results reveal that the distance can be estimated by the fluid dynamics approach which introduces novel parameters such as the volume fraction of droplets in a mixture of air and liquid droplets and the beamwidth of the TX. Furthermore, the effect of the evaporation is shown with the numerical results.