Non-real Time Research Articles

Bio-inspired Optimization Routing Technique Using DNA Sequencing Algorithm for Wireless Sensor Networks

Routing optimization is a promising platform in wireless sensor network (WSN) for many researchers to work on various problems related to the balancing of performance parameters required by an application. In a sensor network, multipath routing protocol design is influenced by many factors, which includes hardware constraints, scalability, operating environment, reliability, topology, fault tolerance, transmission media, and power consumption which are addressed by many researchers. Balancing of these factors plays vital role in designing a routing protocol because they are application specific and vary from one application to another. All these performance metrics which are considered as application requirement must be measurable, independent and comparable. This paper focuses on designing a framework that balances the various performance metrics to get near optimal solution for multipath routing in WSNs. This framework is a hybrid approach that combines the dynamic programming model of DNA sequence algorithm with the existing Multipath routing algorithms to get the optimized routing sequence to balance the real time traffic and non-real time traffic on the multiple paths with improved energy and throughput parameters. Simulation results confirms the significant improvement in throughput performance and packet delivery ratio, substantial reduction in energy consumption against standard Multipath routing protocols.

A Study on Machine Learning: Elements, Characteristics and Algorithms

Machine learning algorithms are used immensely for performing most important computational tasks with the help of sample data sets. Most of the cases Machine learning algorithms will provide best solution where the programming languages failed to produce viable and economically cost-effective results. Huge volume of deterministic problems are addressed and tackled by using the available sample data sets. Because of this now a days machine learning concepts are extensively used in computer science and many other fields. But still we need to explore more to implement machine learning in a specific field such as network analysis, stock trading, spam filters, traffic analysis, real-time and non-real time traffic etc., which may not be available in text books. Here I would like to discourse some of the key points that the machine learning researchers and practitioners can make use of them. These include shortcomings and concerns also.

Swarm Based Cross Layer Optimization Protocol for WMSN

Wireless Multimedia Sensor Network (WMSN) is comprised of tiny, low cost multimedia devices such as video cameras and microphones. These networks can transfer scalar as well as multimedia data into real time as well as non-real time applications. However addition of such devices exposes additional challenges on both QoS assurance and energy efficiency for efficient use of resources. This paper presents cross layer based AntSenseNet protocol to meet various QoS requirements such as throughput, jitter, lifetime and packet delivery ratio in order to improve network lifetime. Cross layer routing protocol utilizes scheduling algorithm and AntSenseNet protocol builds hierarchical structure and able to use multipath routing protocol. Simulation results shows Cross layer based AntSenseNet protocol outperforms Ant Sense routing protocol and cross layer routing protocol in terms of throughput and packet delivery ratio

A (<inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math> </inline-formula>, <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math> </inline-formula>) Authentication Scheme Against Mobile Sink Replicated Attack in Unattended Sensor Networks

In some non-real time applications, data is collected by mobile sinks (MSs). This kind of network is vulnerable to MSs replicated attack. In this attack, replicated MSs can collect data from sensor nodes by establishing pairwise keys with them using keys information obtained from captured sensors. In this letter, a ( ${M}$ , ${m}$ ) authentication scheme against the attack is proposed. The analysis and simulation results indicate that the scheme can improve networks’ resilience against MS replicated attack as compared with existing schemes.

Multiple-to-single sound source localization by applying single-source bins detection

This paper proposes a novel localization scheme for multiple sound sources that imposes the relaxed sparsity constrains (not all time-frequency coefficients are overlapped) on the source signals. First, a “DOA convergence” assumption is proposed, which means that if most of the time-frequency (T-F) bins in a T-F zone are derived from only one source – defined as single source bins (SSBs), the corresponding direction of arrival (DOA) estimates are relatively concentrated with a heavy density. This assumption is validated through statistical analysis by applying a quantitative measure of convergence. Accordingly, by applying the “DOA convergence” assumption, the detection of SSBs is converted to a clustering problem, K-means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms are utilized to complete the task in this paper. The cross distortions (localization error due to the cocktail party phenomenon) in localization caused by multiple simultaneously occurring sources is significantly weakened by conducting DOA estimation among these SSBs, i.e., the multiple source localization is rewritten to a single source one among these SSBs. Moreover, the proposed SSBs detection is applicable to other localization methods and not limited to specific microphone topology. Experimental results demonstrate the localization accuracy of the proposed method outperforms the state-of-the- art localization approaches which are based on single source zone detection. However the proposed method is capable of real-time processing, the accuracy is insufficient in the current system. If non-real time processing is allowed, our method can be realized with higher accuracy than the conventional ones.

Optimal Power Control and Scheduling for Real-Time and Non-Real-Time Data

We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose two sum-rate-maximizing algorithms that satisfy these constraints as well as achieving the system's capacity region. In both algorithms, the power allocation policy has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users, which we call the Lambert-power policy, differs in structure from the water-filling policy for the NRT users. The first algorithm is optimal for the on–off channel model with a polynomial-time scheduling complexity in the number of RT users. The second, on the other hand, works for any channel fading model, which is shown through simulations to have an average complexity that is close to linear. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.

Hybrid routing algorithm for improving path selection in sustainable network

Sustainable network are a type of network with sustainable energy. They consist of components and sensors that work in a cooperative manner. Multi-path routing optimization is a promising platform in wireless sensor network (WSN) with performance parameters that are application specific. In this network the sensor nodes generates vast amount of data in the applications like event monitoring, object tracking etc. These sensor data are forwarded to the node designated as sink that consumes lot of energy. It depends on factors like communication path, number of hops, network bandwidth support. Previous studies on optimal multipath routing in WSN are restricted to generate the optimal path using more number of random parametric values. There is limited work focusing on categorizing the paths that are used to route the critical data like traffics related to the real time and non-real time. We devised a hybrid routing algorithm that is a modified version of bio-inspired dynamic programming model of DNA sequence algorithm that results in selection of optimal path using node specific deterministic values from the numerous paths between source and the sink node. Our approach is tested and evaluated through simulation set up with mobility support and compared with evolutionary algorithms ACO, PSO and AOMDV routing protocols. Simulation results are analysed by varying the number of traffics and node density that confirms the critical change in throughput execution and packet delivery ratio, substantial reduction in energy consumption against standard multi-path routing protocols.

Non-Real Time Ultrasound-Guided Spinal/Epidural Anesthesia for C-Section in an Obstetric Patient with Instrumented Lumbar Spine-A Single-Operator Reliable Method: A Case Report

Non-Real Time Ultrasound-Guided Spinal/Epidural Anesthesia for C-Section in an Obstetric Patient with Instrumented Lumbar Spine-A Single-Operator Reliable Method: A Case Report

Simulation of Packet Scheduling in Cognitive Long Term Evolution-Advanced

<p>Real Time (RT) and Non-Real Time (NRT) multimedia content demand on mobile devices are increasing at a high pace. Long Term Evolution-Advanced (LTE-A) is expected to cater these demands. However, LTE-A operates at fixed spectrum which leads to spectrum scarcity. Cognitive Radio (CR) is one the promising technologies that is used to overcome spectrum scarcity and implementation of CR into LTE-A will improve spectrum availability and efficiency of the network. Furthermore, with addition of Packet Scheduling (PS) in the cognitive LTE-A, QoS requirement of the mobile users can be guaranteed. However, the study on the stated is very limited. Thus, this paper models, simulates and evaluates performance of five well-known PS algorithms for supporting the RT and NRT multimedia contents. The simulation results show that Maximum- Largest Weighted Delay First (M-LWDF) is the best candidate for implementation in the cognitive LTE-A.</p>

Seamless vertical handoff using modified weed optimization algorithm for heterogeneous wireless networks

In the global scenario, a variety of wireless access networks are available. Different types of applications such as real time, nonreal time, and high bandwidth availability are used for heterogeneous wireless networks. Therefore, it is necessary for a service provider to make an appropriate connection support. For better performance, connections are to be exchanged among the different networks using seamless vertical handoff (VHO). The proposed algorithm shows the effect of optimization technique, which involves handoff decision process using vertical handoff triggering and selection of the network. The handoff triggering is initiated by using the received signal strength (RSS). In traditional method, handoff triggering is initiated by using RSS only. This method, modified weed optimization (M-WO) algorithm, reduces the unnecessary handoff by considering both RSS and velocity of the mobile node in handoff triggering. The parameters such as battery lifetime, handoff call dropping rate, load, dynamic weights adaptation and so on are to be considered individually or combined to make an effective network selection process. This paper highlights a novel effect ofM-WOalgorithm for decision making during the VHO. Our effort is to essentially optimize the system load, so that it reduces the handoff call dropping rate and the battery power consumption of the mobile node (MN). Weight of each QoS metrics is adjusted along with the networks changing conditions to trace the M-WO. Therefore, the novel VHO decision-making algorithm is superior to the existing SSF and OPTG methods. The simulation results show that the performance ofM-WOalgorithm is far better than SSF andOPTGmethods in terms of load, handoff call dropping rate and battery lifetime of MN.

Exploring public space through social media: an exploratory case study on the High Line New York City

A public space is a daily life environment for peoples’ social, cultural and recreational activities. Understanding people’s use and experience of urban public space is essential to create a better ground to design and improve the everyday spaces of people. Conventional methods for post-occupancy evaluation, like surveys, have common limitations: high-cost, time-consuming and non-real time interactions. Can social media data provide real-time and valuable insights about public space uses more effectively and promptly? This data-driven qualitative study explores the potential use of social media data for public space evaluation, focusing on the utilization of user-generated contents from social media as the source of user feedbacks. The High Line in New York City was selected as a case, and its related 9974 tweets were collected from Twitter over 14 months (August 2014–Oct 2015). The Twitter data were pre-processed through text-mining techniques and, for analysis, advanced computational techniques in social media analytics were performed. The research findings help us identify opportunities and challenges of using social media data analytics that can be adapted for research and practice in urban design, as part of public space evaluation in particular.

Non-preemptive Queueing Model of Spectrum Handoff Scheme Based on Prioritized Data Traffic in Cognitive Wireless Networks

In this study, a non-preemptive M/G/1 queueing model of a spectrum handoff scheme for cognitive wireless networks is proposed. Because spectrum handoff gives secondary users an opportunity to carry on their transmissions, it is crucially important to determine the actions of primary users. In our queueing model, prioritized data traffic is utilized to meet the requirements of the secondary users. These users’ packets are categorized into three different priority classes: urgent, real-time, and non-real time. Urgent data packets have the highest priority, while non-real time data packets have the lowest priority. Riverbed (OPNET) Modeler simulation software was used to simulate both reactive and proactive decision spectrum handoff schemes. The simulation results were consistent with the analytical results obtained under different load and traffic conditions. This study also revealed that the cumulative number of handoffs can be drastically decreased by exploiting priority classes and utilizing a decent spectrum handoff strategy, such as a reactive or proactive decision-based strategy.

Real Time- Optimized Scheduler Using Earliest Due Date Mechanism in LTE Downlink System

Long Term Evolution (LTE) is standardized by the Third Generation Partnership Project (3GPP) as the most recent standard in the mobile network technology tree with flat architecture in order to conduct a new model aiming to offer higher-data-rate, lower-latency, and packet-optimized radio access technology called Evolved Universal Terrestrial Radio Access Network (E-UTRAN). However, the schedulers aiming to deal with the Real Time (RT) and the Non-Real Time (NRT) traffic have not defined by the 3GPP specifications. Accordingly, it is very important to design and implement optimum scheduling schemes for achieving the required Quality of Service (QoS) prerequisites such as packet delay and Packet Loss Rate (PLR) of RT multimedia services in E-UTRAN. Thus, the proposed scheduler is presented in this paper as well as the performance of the new scheduling strategy which is based on the Earliest Due Date (EDD) approach. The new proposed scheduler provides better results as compared with the conventional scheduling strategies, especially for multimedia applications in downlink framework of LTE system. The proposed scheduler achieves the highest good-put as well as the lowest PLR especially for the video users.

Game‐theoretic beamforming techniques for multiuser multi‐cell networks under mixed quality of service constraints

We propose a game-theoretic approach for the downlink beamformer design for a multiuser multi-cell wireless network under a mixed quality of services criterion. The network has real time users that must attain a specific set of signal-to-interference-plus-noise ratios, and non-real time users whose signal-to-interference-plus-noise ratios should be balanced and maximized. We propose a mixed QoS strategic noncooperative game wherein base stations determine their downlink beamformers in a fully distributed manner. In the case of infeasibility, we have proposed a fall back mechanism which converts the problem to a pure max-min optimization. We further propose the mixed QoS bargain game to improve the Nash equilibrium operating point through Egalitarian and Kalai-Smorodinsky bargaining solutions. We have shown that the results of bargaining games are comparable to that of the optimal solutions.

Intelligent vertical handoff decision strategy based on network performance prediction and consumer surplus value for next generation wireless networks

Vertical handoff (VHO) decision services play an essential role in the current wireless networks. Many algorithms designed aim at quality of service while granting seamless roaming amongst a number of heterogeneous access networks. In this study, the authors proposed an intelligent VHO decision strategy targeting to maximise user satisfaction without compromising on quality of service for non-real time mobile based services. The proposed strategy applies consumer surplus value (CSV)-based pricing scheme and also a prediction system to approximate network performances at various intervals. Finally, a fuzzy rule-based behavioral system is proposed to find optimal network by considering both CSV as well as network performance as stimulus parameters. Simulation is carried out using OM Net++ and MATLAB, and the results are acknowledged with discussion and analysis.

VERTICAL HANDOVER DECISION MANAGEMENT ON THE BASIS OF SEVERAL CRITERIA FOR LVQNN WITH UBIQUITOUS WIRELESS NETWORKS

Vertical handover decision management is an essential to keep the seamless ubiquitous heterogeneous wireless networks since each access network has different operations in the next generation. In this paper, WCDMA, LTE and WLAN are cooperated in the architecture of mobile IP regional registration (MIP-RR). We propose the Learning Vector Quantization Neural Networks (LVQNNs) approach in order to maintain uninterrupted communication that depends on received signal strength indicator, data rate requirement, monetary cost of service and mobile terminal device speed metrics are considered as multi-criteria to initial handover. Furthermore, the multi-criteria are dynamic to influence for real-time and non-real time services in different networks and the users select the optimal target network which is the highest handover factor score in order to balance against the network condition and user preference. To ensure the Always Best Connected (ABC) demands, the simulation results illustrate that our proposed algorithm provided outperform the performance in term of unnecessary handover, the call dropping probability, data packet delay and network utilization compared with conventional method as fuzzy logic and neural network based machine learning.

Optimized secondary user selection for quality of service enhancement of Two-Tier multi-user Cognitive Radio Network: A game theoretic approach

Cognitive Radio Networks (CRN) is the solution for the spectrum scarcity problem, that has surfaced with the exponential increase in number of mobile users. In CRN, the cognitive devices sense the spectrum, and transmits, on detecting an available channel. Thus CRN resourcefully utilize the idle spectrum bands. In CRN, the users are classified into two types - Primary users and Secondary users. Secondary users are further classified into two tiers- Real Time users (RT) and Non-Real Time users (NRT). Users from both the tiers share a single channel for transmission, resulting in efficient spectrum usage. However, with ever growing number of secondary mobile users having diverse QoS requirements, the cognitive interactions among them need to be optimized. This paper uses game theory to study the conflict and cooperation among the two tiers of secondary users. An auction game model is proposed that analyzes the complex decision making process and efficiently allocates an idle channel to a pair of RT and NRT secondary users from a pool of users. The game model is further extensively analyzed mathematically, using Markov chain, showing a reduction in user Blocking probability, user Dropping probability, Channel Saturation probability and increase in user Acceptance probability, compared to the Basic One-Tier CRN and Non-Optimized Two-Tier CRN. The optimized network model is then simulated using Qualnet 7.1. The simulation result shows improved Bandwidth Utilization Efficiency, Spectral efficiency and increased number of secondary users served, compared with respect to Basic One-Tier CRN and Non-Optimized Two-Tier CRN.

Cross Layered Network Condition Aware Mobile-Wireless Multimedia Sensor Network Routing Protocol for Mission Critical Communication

The high pace emergence in wireless technologies have given rise to an immense demand towards Quality of Service (QoS) aware multimedia data transmission over mobile wireless multimedia sensor network (WMSN). Ensuring reliable communication over WMSN while fulfilling timely and optimal packet delivery over WMSN can be of great significance for emerging IoT ecosystem. With these motivations, in this paper a highly robust and efficient cross layered routing protocol named network condition aware mobile-WMSN routing protocol (NCAM-RP) has been developed. NCAM-RP introduces a proactive neighbour table management, congestion awareness, packet velocity estimation, dynamic link quality estimation (DLQE), and deadline sensitive service differentiation based multimedia traffic prioritization, and multi-constraints based best forwarding node selection mechanisms. These optimization measures have been applied on network layer, MAC layer and the physical layer of the protocol stack that eventually strengthen NCAM-RP to enable QoS-aware multimedia data transmission over WMSNs. The proposed NCAM-RP protocol intends to optimize real time mission critical (even driven) multimedia data (RTMD) transmission while ensuring best feasible resource allocation to the non-real time (NRT) data traffic over WMSNs. NCAM-RP has outperform RPAR based routing scheme in terms of higher data delivery, lower packet drops and deadline miss ratio. It signifies that NCAM-RP can ensure minimal retransmission that eventually can reduce energy consumption, delay and computational overheads. Being the mobility based WMSN protocol, NCAM-RP can play significant role in IoT ecosystem.

표면파 기반의 풍력발전기 블레이드 표면상태 실시간 모니터링에 관한 연구

현재 풍력발전은 우리나라에서 가장 주목 받고 있는 신재생에너지 분야로 많은 연구가 진행 중이다. 풍력발전을 구성하는 요소 중 핵심 요소인 블레이드에 손상이 발생할 경우에 발전 효율에 직접적인 영향을 미치므로 효율적인 유지보수를 위해 초기에 결함을 측정하는 기술이 매우 중요한 상태이다. 그러나 기존의 초음파 비파괴 검사 및 열화상 비파괴 검사는 소요시간이 길고 실시간 모니터링이 어려우므로 초기 결함 측정이 불가능하다. 기존의 문제를 보완하고자 본 논문에서는 표면파를 이용한 블레이드 표면상태 실시간 모니터링에 관한 연구를 수행하였다. 압전센서 기반의 시스템을 구성하여 블레이드 표면샘플에 대해 공정 변수 별 기초 성능 실험을 하였고, 소형 블레이드에 대해 공정변수 별 실험을 통해 블레이드의 크랙, 벗겨짐, 장애물 등을 실시간으로 모니터링이 가능한지 연구 하였다. These days much efforts are being dedicated to wind power as a potential source of renewable energy. To maintain effective and uniform generation of energy, defect preservation of turbine blade is essential because it directly takes effects on the efficiency of power generation. For the effective maintenance, early measurements of blade defects are very important. However, current technologies such as ultrasonic waves and thermal imaging inspection methods are not suitable because of long inspection time and non-real time inspection. To supplement the problems, the study introduced a method for real time defect monitoring of a blade surface based on surface wave technology. We examined the effect of various parameters such as micro-cracks and peelings on the propagation of surface wave.

Using LDM to Achieve Seamless Local Service Insertion and Local Program Coverage in SFN Environment

Layered division multiplexing (LDM) is a spectrum efficient non-orthogonal multiplexing technology that has been adopted in the Advanced Television Systems Committee (ATSC) 3.0 Physical Layer Standard as a baseline technology. This paper studies a two-layer LDM with one layer used for providing a global service through a single frequency network (SFN), and the other for providing local coverage/services, such as location targeted advertising or local content insertion. The pilot boosting effect on SNR and co-channel interference is also analyzed. The LDM upper layer can be used to deliver time-division multiplexed mobile-HD and 4k-UHD services. The LDM lower layer with a negative SNR threshold can reliably provide seamless local coverage/service from each SFN transmitter without coverage gaps among adjacent SFN transmitter service areas. No directional receiving antenna is required for the local service reception and receivers simply tune into the stronger signal. In such LDM systems, while the upper layer is operating in a traditional SFN mode, the lower layer operates in a special form of distributed MIMO or gap-filler mode to provide targeted local coverage. For implementing the two-layer system introduced in this paper, only ATSC 3.0 baseline technologies are used, i.e., there is no need to modify the ATSC 3.0 standard. Given the upper and lower layers’ data rate requirements and the SNR thresholds, the lower layer power, with respect to the upper layer (injection level), can be optimized to maximize upper and lower layer performance and coverage. Since the advertisement time of the local service is typically less than 20% of the program time, nonreal time could be used to play-back the local content at five times the transmission bit rate for better (audio/video) service quality.