End Latency Research Articles

Differential diagnosis value of sympathetic skin response and cutaneous silent period on early-stage multiple system atrophy and Parkinson disease

PurposeEarly differentiation between Parkinson's disease (PD) and Multiple system atrophy (MSA), particularly the parkinsonian subtypes (MSA-P), is challenging due to similar clinical symptoms. We aimed to evaluate Sympathetic skin response (SSR) and Cutaneous silent period (CSP) parameters in patients with MSA-P and PD to identify possible biomarkers that could distinguish the two groups of patients in early stage. Methods22 individuals with early-stage MSA-P, 29 with early-stage PD, and 28 healthy controls were recruited from Guangdong Provincial People's Hospital. Demographic data was collected for all participants. Their SSR and CSP were evaluated using clinical electromyography equipment. Data were compared between different groups. The diagnostic accuracy of SSR and CSP parameters was calculated using the ROC curve. Logistic regression was used to produce an integration model to enhance diagnostic utility. ResultsFoot amplitude, CSP end latency and duration distinguished MSA-P from PD with the area under the curve (AUC) 0.770, 0.806, and 0.776, respectively. Foot and hand SSR amplitude distinguished PD from HC with the AUC 0.871 and 0.768, respectively. Foot SSR amplitude, hand SSR amplitude, and CSP end latency distinguished MSA-P from HC with the AUC 0.964, 0.872, and 0.812, respectively. The combination of SSR and CSP parameters differentiation between MSA-P and PD, PD and HC with the AUC 0.829 and 0.879, respectively. ConclusionsAnalysis of SSR and CSP parameters showed excellent diagnostic accuracy in discriminating patients with early-stage MSA-P from HC and good diagnostic accuracy in discriminating patients with MSA-P from PD with early stages.

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

Identification of small diameter nerve fiber damage in hemodialysis patients' hands using the cutaneous silent period

BackgroundThe arteriovenous fistula is the most effective vascular access option for hemodialysis patients. An important and largely unreported concern detected at follow-up is the complaints of tingling or numbness in the hands of those patients. Furthermore, the cutaneous silent period qualifies as a straightforward evaluation of small nerve fiber function. This study aims to evaluate the function of small-diameter nerve fibers in the hands of patients undergoing continuous hemodialysis (HD) on the side with the arteriovenous fistula (AVF) and on the contralateral side.ResultsA cross-sectional study of 40 randomly chosen patients with chronic kidney disease on regular hemodialysis three times weekly the cutaneous silent period (CSP) was recorded on the hand with and without AV fistula. The cutaneous silent period (CSP) CSP was elicited by electrical square pulse stimulation using standard bipolar electrodes. The average cutaneous silent period onset and end latencies demonstrated significant prolongation in the hemodialysis patient group (arteriovenous fistula and non-arteriovenous fistula) compared with the control group. Correlation between cutaneous silent period with hemodialysis duration and adequacy of hemodialysis (KT\\V). There was a statistically negative correlation observed between cutaneous silent period onset latency on the arteriovenous side and a positive correlation with KT\\V on non-arteriovenous fistula. side.ConclusionsHemodialysis patients recommended to undergo regular neurophysiological testing to check for small nerve fiber affection. In particular, measuring the cutaneous Silent period that provides a quick and noninvasive way to rule out small A-delta nerve malfunction. In addition, less nerve injury results from hemodialysis's increased efficiency.

Peripheral Nerve Injury Induced by Japanese Encephalitis Virus in C57BL/6 Mouse.

Japanese encephalitis virus (JEV), with neurotoxic and neuroinvasive properties, is the major cause of human viral encephalitis in Asia. Although Guillain-Barré syndrome caused by JEV infections is not frequent, a few cases have been reported in recent years. To date, no existing animal model for JEV-induced peripheral nerve injury (PNI) has been established, and thus the pathogenic mechanism is not clarified. Therefore, an animal model is urgently required to clarify the correlation between JEV infection and PNI. In the present study, we used JEV GIb strain of NX1889 to establish a mouse model of JEV infection. The general neurological signs emerged on day 3 of modeling. The motor function continued to deteriorate, reaching a maximum at 8 to 13 days postinfection (dpi) and gradually recovered after 16 dpi. The injuries of 105 PFU and 106 PFU groups were the most severe. Transmission electron microscopy and immunofluorescence staining showed varying degrees of demyelination and axonal degeneration in the sciatic nerves. The electrophysiological recordings demonstrated the presence of demyelinating peripheral neuropathy with reduced nerve conduction velocity. The decreased amplitudes and the prolonged end latency revealed axonal-type motor neuropathy. Demyelination is predominant in the early stage, followed by axonal injury. The expression level of JEV-E protein and viral RNA was elevated in the injured sciatic nerves, suggesting that it may cause PNI at the early stage. Inflammatory cell infiltration and increased inflammatory cytokines indicated that neuroinflammation is involved in JEV-induced PNI. IMPORTANCE JEV is a neurotropic flavivirus belonging to the Flaviviridae family and causes high mortality and disability rates. It invades the central nervous system and induces acute inflammatory injury and neuronal death. Thus, JEV infection is a major global public health concern. Previously, motor dysfunction was mainly attributed to central nervous system damage. Our knowledge regarding JEV-induced PNI is vague and neglected. Therefore, a laboratory animal model is essential. Herein, we showed that C57BL/6 mice can be used to study JEV-induced PNI through multiple approaches. We also demonstrated that viral loads might be positively correlated with lesion severity. Therefore, inflammation and direct virus infection may be the putative mechanisms underlying JEV-induced PNI. The results of this study laid the foundation for further elucidation of the pathogenesis mechanisms of PNI caused by JEV.

C-QoS-AOMDV: A cluster based QoS aware multipath routing protocol for MANET using hybrid soft computing techniques

Mobile ad hoc networks (MANETs) favour flexible routing protocols because a single route exceeds certain limits. It has many advantages over one-way routing approaches, including high productivity, low end latency, high energy efficiency, and long network life expectancy. As MANET terminals are powered by batteries, energy efficiency is a crucial requirement for many applications. C-QoS-AOMDV is a cluster-based QoS-aware multipath routing protocol for MANET that uses hybrid soft computing techniques to further improve QoS. The main contributions of proposed C-QoS-AOMDV routing protocol are given as follows. In C-QoS-AOMDV routing protocol, we present a modified bird mating optimization (MBMO) clustering technique which reduces the total energy consumption of the network. We compute cluster head (CH) based on the multiple constrains for data transmission between cluster members to corresponding CH node. Then, the mine blast optimization (MBO) algorithm is used to optimize multiple constrains to compute trust degree and maximum trust degree to select CH. Third, we illustrate hierarchical decision tree based deep neural network (HDT-DNN) which is used to compute the next intermediate node for further data transfer between one CH to another CH. Finally, the proposed C-QoS-AOMDV routing protocol is implemented in Network Simulator (NS-3) and the results are compared to the cutting-edge technology protocols AOMDV, AOMDV-QoS, and GE-QoS-AOMDV.

A MEC architecture for a better quality of service in an Autonomous Vehicular Network

Designing 5G networks and related applications such as the Internet of Things (IoT), Cellular and autonomous vehicular networks (AVNET) is a challenge. Indeed, these networks are subjected to a number of constraints that play a crucial role in the network’s quality of service (QoS). Among these constraints, we have the management of computing, storage, bandwidth resources and low latency requirements. If in the past cloud computing has been used to avoid networks being affected by some of the previous constraints, that technology negatively affects the low latency requirements required by AVNETs for example. Multi-access Edge Computing (MEC) has recently emerged as a palliative solution to cloud computing. MEC aims to bring computing and storage resources from Cloud Data Center to Edge Data Center nearer to the User Equipment (UEs) in order to reduce the UEs’ requests latency for an improvement of the network’s QoS. Many MEC architectures have been proposed for AVNET. These solutions make use of Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC) or Network Slicing (NS) technologies. Some of these techniques combine partially these technologies while others do not. But to the best of our knowledge, none of them combines all these technologies to obtain a better QoS. In this paper, we combine the SDN, NFV, SFC and NS technologies to efficiently manage the MEC server resources for guaranteeing the QoS requirements in AVNETs. The QoS parameters we consider are latency, computing, storage and bandwidth resources. In that way, we first present a MEC server mathematical resource management model, then, we propose a new MEC architecture adapted to AVNETs that uses the aforementioned technologies also as the mathematical model we first present to manage the bandwidth, computing, and storage resources. The simulation results show that these resources are well managed, resulting to a low end to end latency for autonomous vehicles (AVs) requests executing on edge/cloud servers when the direct communication of AVs with cloud server takes long end to end delays, ensuring a better QoS for the AVNET.

Recording cutaneous silent period parameters in hereditary and acquired neuropathies.

Cutaneous silent period (CSP) is the interruption in muscle activity after painful stimulation of a sensory nerve. The aim of the present study is to assess CSP changes in patients with polyneuropathy (PNP). The present study was carried out to assess CSP in individuals with diabetes (DM) and Charcot-Marie-Tooth (CMT) disease. The sample comprised 24 individuals with DM, 10 individuals with CMT1 disease, and 10 individuals with CMT2 disease. The control group (CG) consisted of 59 individuals. The mean latencies recorded for the upper limbs in the CG were 79.2 milliseconds (onset latency), 69.3 milliseconds (50% reduction latency), 112.2 milliseconds (end latency), and 33.1 milliseconds (CSP duration). On the other hand, the mean latencies recorded for the lower limbs were 99.0 milliseconds (onset latency), 85.0 milliseconds (50% reduction latency), 136.9 milliseconds (end latency), and 38.2 milliseconds (CSP duration). The mean latencies recorded for the CG were significantly lower than the ones recorded for other groups, both in the upper and lower limbs. Cutaneous silent period values recorded for the CG in the present study were close to the ones reported in studies available in the literature. Abnormal CSP parameters were observed in the group of individuals with PNP. The end latency in the lower limbs helped differentiating the demyelinating subgroup from the axonal one.

Performance Benchmarking and Metrics Evaluation of Apache Accumulo

This paper aims at measuring the performance of Apache Accumulo by using Storage Benchmark Kit (SBK). Apache Accumulo is based on the Google’s BigTable design with addition of two unique features. One feature is the iterator framework that embeds user-programmed functionality (server-side programming) into different Log-structured Merge Tree (LSM-tree) stages. The second is the cell-level security that enables fine-grain data access control. The SBK (Storage Benchmark Kit) is an open source software framework for the performance benchmarking of any storage system. We can measure the maximum throughput performance of any storage device/system using SBK. The SBK itself is a very high-performance benchmark tool/framework. It massively writes the data to the storage system and reads the data from the storage system. The SBK supports multi writers and readers and also the End to End latency benchmarking.

Performance Analysis in Memory Distribution Key Value Store and Design of Storage Driver for SBK Framework

This paper aims to write the Memcached storage driver and performance analysing the Memcached. Memcached is a free and open source software, high performance, distributed memory object caching system. It is often used to speed up dynamic database-driven websites by caching data and objects in RAM to reduce the number of times, an external data source must be read. The SBK itself is a high- performance benchmark tool/framework. It massively writes the data to the storage system and reads the data from the storage system. The SBK supports multi writers and readers and also the End to End latency benchmarking. The main objective is to performance benchmark the Memcached storage driver using Storage Benchmark Kit(SBK), to speed up the performance of websites and web applications using SBK software, to extract the results of the operations which were performed on the data. The performance benchmarking is done by adding SBK driver which specifies the input/output operations of storage systems. SBK delivers the throughput and latency values for a every specific interval for live performance analytics. Memcached is a free and open-source software, high performance, distributed memory object caching system. The SBK itself is a high-performance benchmark tool/framework. The SBK supports multi writers and readers and also the End-to-End latency benchmarking. Memcached is a key-value store that keeps data in the DRAM i.e., data is not persistent. Clients communicate with Memcached through a simple set of APIs: Set, Add, replace are used to store data, Get or Remove are used to retrieve or remove data.

HLF-Kubed: Blockchain-Based Resource Monitoring for Edge Clusters


 
 
 In the past several years, there has been an increased usage of smart, always- connected devices at the edge of the network, which provide real-time contextual information with low overhead to optimize processes and improve how companies and individuals interact, work, and live. The efficient management of this huge pool of devices requires runtime moni- toring to identify potential performance bottlenecks and physical defects. Typical solutions, where monitoring data are aggregated in a centralized manner, soon become inefficient, as they are unable to handle the increased load and become single points of failure. In addition, the resource-constrained nature of edge devices calls for low-overhead monitoring systems. In this paper, we propose HLF-Kubed, a blockchain-based, highly available framework for monitoring edge devices, leveraging distributed ledger technology. HLF-Kubed builds upon Kubernetes container orchestrator and HyperLedger Fabric frameworks and implements a smart contract through an external chaincode for resource usage storing and querying. Our experimental results show that our proposed setup forms a low-overhead monitoring solution, with an average of 448 MB of memory and 6.8% CPU usage, while introducing 1.1s end-to- end latency for store operation and 0.6s for ledger querying respectively.
 
 

Evaluation of the Cutaneous Silent Period in a Healthy Brazilian Population.

To establish cutaneous silent period (CSP) normative values and investigate the variables that may influence them. We tested 41 healthy subjects. All subjects underwent nerve conduction studies, and we evaluated the CSP in both arms. Four subjects did not have CSP and were excluded. The analyses were performed in the healthy group composed of 23 women and 14 men, with a mean age of 35 (range, 19-64) years. The CSP median duration was 23.2 milliseconds (ms), with 2 to 98th percentile at 11.3 and 48.7 ms. The median onset latency was 87.9 (range, 72.9-109) ms, and the median end latency was 112 (range, 93.8-138) ms. The CSP onset latency positively correlated with height, whereas CSP end latency and duration were weakly but significantly associated with age. Some measurements of ulnar nerve conduction study also correlated with CSP measures. The interrater coefficients for the primary measures of onset and end latency demonstrates the reproducibility of the method. The CSP with the fifth digit stimulation and recording from the abductor digiti minimi muscle is a valid diagnostic tool that can be used in clinical practice.

Secure 5G-Assisted UAV Access Scheme in IoBT for Region Demarcation and Surveillance Operations

This article proposes a generic scheme that integrates blockchain (BC) and unmanned aerial vehicles (UAVs) through a fifth-generation (5G) Tactile Internet (TI) service to leverage responsive and secure communications in the Internet-of-Battlefield-Things (IoBT)-based ecosystems. UAVs are deployed with camera sensors to monitor and transfer ultra-high resolution (UHR) images and real-time live video feeds of region demarcation and surveillance to ground control stations (GCS). Currently, UAVs operate through Long-Term Evolution-Advanced (LTE-A) services and face bottlenecks in terms of bandwidth and end latency for live feeds. As the feed is sent through open channels, it is vulnerable to security attacks by an adversary. The proposed scheme addresses the dual issues of responsive network orchestration, and induces trust, immutability, and transparency in shared data among UAVs and GCSs via BC as a key solution. Through a case study, the scheme is compared to baseline LTE services. In the simulation, transactions through BC achieve 31.85 percent improvement over cloud-based GCS, an average frame loss of 18.42 percent in 5G-TI compared to 94.07 percent in 4G-LTE-A channel, and processing latency of 0.1061 s in 5G-TI, compared to 2.2133 s in 4G-LTE, which indicates the viability of the proposed scheme.

Comparison of long-latency reflex and mixed nerve silent period responses in various hypokinetic movement disorders

Long-latency reflex and mixed nerve silent period responses are electrophysiological methods to study the sensorimotor functions of the central nervous system. Here we aimed to study long-latency reflexes and mixed nerve silent period responses in different types of hypokinetic movement disorders in order to find an electrophysiological landmark to distinguish them. We included 39 patients with idiopathic Parkinson's disease (IPD), 12 patients with multiple system atrophy (MSA), 10 patients with corticobasal syndrome (CBS), 5 patients with progressive supranuclear palsy (PSP) and 26 healthy participants. We recorded the segmental reflex, the long-latency reflexes and the mixed nerve silent period responses for each participant. C reflex, long-latency reflex-I and long-latency reflex-III responses were not obtained in any patients with PSP. Long-latency reflex amplitude/ F amplitude ratio was significantly lower in patients with IPD and PSP compared to healthy individuals (p=0.036, p=0.006 respectively). The mixed nerve silent period end latencies were significantly longer in IPD, MSA, CBS groups compared to the healthy individuals (p=0.026, p=0.050, p=0.008 respectively). We suggest that recording long-latency reflex, particularly C reflex responses may provide promising results in distinction of CBS and MSA from PSP. Prospective studies with clinical findings and brainstem reflexes may offer more information.

LearningCC: An online learning approach for congestion control

AbstractIt is noticed that traditional loss based algorithms are not moving data very well in today's Internet. To get bottleneck bandwidth fully utilized, these algorithms will keep increasing the congestion window and maintain a standing queue of packets in routers, which cause large end to end latency. Such long latency is quite annoying to these ever growing delay sensitive applications. Recently, much effort has been devoted by researchers from both academia and industry to design better congestion control protocols and make a better Internet. In this article, we present an online learning based approach named LearningCC for congestion control. Instead of adjusting the congestion window with fixed rule as these traditional algorithms do, there are several options for an endpoint to choose. Each option is mapped as an arm of a bandit machine. The exploration and exploitation scheme is used to guide the selection on congestion window update rule. Through trial and error, an endpoint improves its performance by choosing action with better reward. Experiments are conducted on ns3 platform to verify the effectiveness of LearningCC. Results indicate it achieves lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant throughput improvement in link suffering from random loss.

An Elastic System Architecture for Edge Based Low Latency Interactive Video Applications

5G and edge computing have brought great changes to video industry. Interactive video is becoming an emerging application form of multimedia service, which provides attraction beyond typical scenarios like cloud gaming and remote virtual reality (VR), and puts forward great challenges in resource capacity, response latency, and function flexibility to its service system. In this paper, we propose an elastic system architecture with low latency features to accommodate generic interactive video applications on near user edges. To increase system flexibility, we firstly design a dynamic Directed Acyclic Graph (dDAG) model for efficient task representation. Secondly, based on the model, we present the elastic architecture together with its scalable workflow pipeline. Thirdly, we propose a set of novel latency measurement metrics to analyze and optimize the performance of an interactive video system. Based on the proposed approaches, we disassemble a real world free-viewpoint synthesis application and benchmark its performance with the metrics. Extensive experimental results show the flexibility of our system to handle the stochastic human interactions during a video service session, with less than 5 ms additional scheduling latency introduced. End to end latency is kept within 43 ms for complex functions, and 28 ms for simpler scenarios, which satisfies the restrictions of most interactive video applications provided by an edge. Client of the architecture serves as a pure video player, which is also friendly to power limited terminals such as 5G phones. Efficiency and stability analyses of the system show superiorities over existing work, and also reveal potential optimization directions for future research.

Self-Pruning based Probabilistic Approach to Minimize Redundancy Overhead for Performance Improvement in MANET

The Broadcast storm problem causes severe interference, intense collision and channel contention, which greatly degrades the QoS performance metrics of the routing protocols. So, we suggest a neighbourhood coverage knowledge probabilistic broadcasting model (NCKPB) integrating with AODV protocol with knowledge on 2-hop neighbourhood coverage; a connectivity function to control a node’s forwarding probability of retransmission to alleviate significant overhead redundancy. Our objective is to minimize the broadcast RREQ overhead while ensuring fair retransmission bandwidth. We considered two more important measures called Saved Rebroadcast and Reachability. The outcomes of NCKPB, Fixed probability (FP) and Flooding (FL) routing schemes are examined under three major operating conditions, such as node density, mobility and traffic load. The NS-2 results demonstrate the efficacy of the proposed NCKPB model by illustrating its performance superiority over all key metrics such as redundancy overhead, end to end latency, throughput, reachability, saved rebroadcast and collision contrast to FP and FL.

Unified energy efficient distributed network management protocol to forward the data packets and to improve QoS in Wireless Sensor Networks

Background: Enhancing quality of service in forwarding the data packets from source to destination in WSN is a major task in contemporary networks. Everywhere the usage of WSN is increasing in the real world. Data sensing, data capturing and data forwarding is a routine process in modern era. Some of the dynamic problems arise as link failure, routing issues, packet delay etc that lead to the entire network shutdown. Hence, there is a need for development or enhancement of the protocol that finds the finest route to forward data packets in an optimal way. Objectives: The main objective of this study is to propose a unified energy efficient protocol to minimize the delay which will result in successful delivery of data packets from source to destination to improve the quality of service in WSN. Methods: This study proposes Unified Energy Efficient Distributed Network Management Protocol (UEEDNMP) focuses mainly on forwarding the data packets and finite routing to minimize the end to end delay. Parametric search technique along with CAFSM method is utilized to design the proposed protocol, to meet out the objective by forwarding the packets with better path from source to destination. Findings: NS2 Version 35 simulator is used to calculate the performance of the proposed UEEDNMP protocol. The simulation result shows UEEDNMP outperforms the existing protocols EH-WSN and ECO-LEACH in terms of data arrival rate, packet drop ratio, packet delivery ratio, energy consumption, network traffic, delay and network lifetime. Novelty: Comprehensive analyses signify that UEEDNMP has greater performance in forwarding the data packets with the finest routing in distributed networks, minimizing energy consumption, minimize the end to end latency time and maximize the life span of the network. Keywords: Energy efficient protocol; delay; wireless sensor networks; data forwarding

Novel trust evaluation using NSGA-III based adaptive neuro-fuzzy inference system

Recently Mobile adhoc networks (MANETs) have received the great attention of researchers as these models provide a wide range of applications. But MANET nodes are prone to various security threats. To overcome this issue, many trust management frameworks have been implemented in the literature. It has been found that the use of machine learning can predict trust values more efficiently. However, machine learning performance suffers from the hyper-parameters tuning and over-fitting issues. Therefore, in this paper, novel trust management is proposed. initially, the Adaptive neuro-fuzzy inference system (ANFIS) is used to train the trust prediction model. Thereafter, a non-dominated sorting genetic algorithm-III (NSGA-III) is used to tune the hyper-parameters of the ANFIS model. Precision, recall, and root mean squared error metrics are used to design a multi-objective fitness function. Optimized link state routing (OLSR) protocol is used for comparative analyses purpose. Three different attacks are applied on the designed network i.e., link spoofing, jellyfish, and gray hole attacks to obtain the dataset. Comparative analysis reveals that the proposed trust evaluation model outperforms the competitive trust evaluation models in terms of various performance metrics such as routing overheads, average end to end latency, packet delivery ratio, and throughput. Thus, the proposed protocol is more secure against various security threats.

Smooth and Low Latency Video Streaming for Autonomous Cars During Handover

Self-driving vehicles are expected to bring many benefits among which are enhancing traffic efficiency and reliability, and reducing fuel consumption which would have a great economic and environmental impact. The success of this technology heavily relies on the full situational awareness of its surrounding entities. This is achievable only when everything is networked, including vehicles, users and infrastructure, and exchange the sensed data among the nearby objects to increase their awareness. Nevertheless, human intervention is still needed in the loop anyway to deal with unseen situations or compensate for inaccurate or improper vehicle's decisions. For such cases, video feed, in addition to other data such as LiDAR, is considered essential to provide humans with the real picture of what is happening to eventually take the right decision. However, if the video is not delivered in a timely fashion, it becomes useless or likely produces catastrophic outcomes. Additionally, any disruption in the streamed video, for instance during handover operation while traversing inter-countries cross borders, is very annoying to the user and possibly cause damage as well. in this article, we start by describing two important use cases, namely Remote Driving and Platooning, where the timely delivery of video is of extreme importance [1]. Thereafter, we detail our implemented solution to accommodate the aforementioned use cases for self-driving vehicles. Through extensive experiments in local and LTE networks, we show that our solution ensures a very low endto- end latency. Also, we show that our solution keeps the video outage as low as possible during handover operation.

IMPROVING THE DATA TRANSMISSION SPEED IN CLOUD MIGRATION BY USING MAPREDUCE FOR BIGDATA

Cloud acts as a data storage and also used for data transfer from one cloud to other. Here data exchange takes place among cloud centers of organizations. At each cloud center huge amount of data was stored, which interns hard to store and retrieve information from it. While migrating the data there are some issues like low data transfer rate, end to end latency issues and data storage issues will occur. As data was distributed among so many cloud centers from single source, will reduces the speed of migration. In distributed cloud computing it is very difficult to transfer the data fast and securely. This paper explores MapReduce within the distributed cloud architecture where MapReduce assists at each cloud. It strengthens the data migration process with the help of HDFS. Compared to existing cloud migration approach the proposed approach gives accurate results interns of speed, time and efficiency.