Real-time Network Applications Research Articles

Hybrid Packet Loss Concealment for Real-Time Networked Music Applications

Hybrid Packet Loss Concealment for Real-Time Networked Music Applications

Freshness-Aware Age Optimization for Multipath TCP Over Software Defined Networks

Recently increasing real-time network applications have raised a new requirement for the future 6G network transmission, which desires the information-update packets arrive at the receiver as timely as possible. Multipath TCP (MPTCP) can be employed in the real-time update transmission system by making use of multiple paths so as to promote the information status update. However, current optimization solutions of MPTCP focus on traditional transmission performance such as throughput, delay, loss, jitter, etc. None of these works considers the freshness metric for MPTCP to satisfy the real-time demands. What's more, most works about transmission model in Software Defined Network (SDN) are based on packet-level analysis without considering flow-level situation. In order to fill these gaps, this paper proposes a novel freshness-aware age optimization solution for MPTCP (FAMT) over SDN. FAMT introduces a new four-tuples to describe the transmission state in the SDN architecture. Then FAMT classifies the whole transmission process into two phases: connection establish phase and data transmission phase. Based on the two phases, FAMT evaluates and defines the age of information for different situations. In addition, FAMT designs an innovative age-assisted fluid model of MPTCP which can realize the four goals effectively.

Robust Image Forgery Detection Over Online Social Network Shared Images

In servers and mobile users, an image is sent over the social network or exchanged. Due to the fact that it contains delicate personal information, the privacy of that data is crucial. A hacker may use social information about a person to discredit them if their image is hacked. Text-based encryption can be used in mobile cloud computing under the current architecture. There are various ways to store data securely utilizing mobile computing, including end-to-end encryption of data transmission and dynamic credential generation that only generates text. We'll be creating a brand-new wavelet watermarking technique called the discrete wavelet transform for use in real-time social network applications like Facebook, and this study suggests an efficient image forgery detection method that recognizes a manipulated foreground or background. Using this technique, images can be used and safely kept on servers. We categorize the image as either common or delicate, and we also enhance the project to include copy right implementation. Run copyright algorithms referred to as wavelet transform algorithms when employing sensitive techniques. After that, provide the receiver secure access to download the pictures. Using C#.NET as the front end and SQL SERVER as the back end, experimental results show the efficiency of current algorithms in real-time social network contexts and a comparison of their privacy rates

A Hadoop Based Approach for Community Detection on Social Networks Using Leader Nodes

Community detection is the most common and growing area of interest in social and real-time network applications. In recent years, several community detection methods have been developed. Particularly, community detection in Local expansion methods have been proved as effective and efficiently. However, there are some fundamental issues to uncover the overlapping communities. The maximum methods are sensitive to enable the seeds initialization and construct the parameters, while others are insufficient to establish the pervasive overlaps. In this paper, we proposed the new unsupervised Map Reduce based local expansion method for uncovering overlapping communities depends seed nodes. The goal of the proposed method is to locate the leader nodes (seed nodes) of communities with the basic graph measures such as degree, betweenness and closeness centralities and then derive the communities based on the leader nodes. We proposed Map-Reduce based Fuzzy C- Means Clustering Algorithm to derive the overlapping communities based on leader nodes. We tested our proposed method Leader based Community Detection (LBCD) on the real-world data sets of totals of 11 and the experimental results shows the more effective and optimistic in terms of network graph enabled overlapping community structures evaluation.

A Hadoop Based Approach for Community Detection on Social Networks Using Leader Nodes

Community detection is the most common and growing area of interest in social and real-time network applications. In recent years, several community detection methods have been developed. Particularly, community detection in Local expansion methods have been proved as effective and efficiently. However, there are some fundamental issues to uncover the overlapping communities. The maximum methods are sensitive to enable the seeds initialization and construct the parameters, while others are insufficient to establish the pervasive overlaps. In this paper, we proposed the new unsupervised Map Reduce based local expansion method for uncovering overlapping communities depends seed nodes. The goal of the proposed method is to locate the leader nodes (seed nodes) of communities with the basic graph measures such as degree, betweenness and closeness centralities and then derive the communities based on the leader nodes. We proposed Map-Reduce based Fuzzy C- Means Clustering Algorithm to derive the overlapping communities based on leader nodes. We tested our proposed method Leader based Community Detection (LBCD) on the real-world data sets of totals of 11 and the experimental results shows the more effective and optimistic in terms of network graph enabled overlapping community structures evaluation.

Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network

A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes’ function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods.

A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Networks.

The fog computing-based device-driven network is a promising solution for high data rates in modern cellular networks. It is a unique framework to reduce the generated-data, data management overheads, network scalability challenges, and help us to provide a pervasive computation environment for real-time network applications, where the mobile data is easily available and accessible to nearby fog servers. It explores a new dimension of the next generation network called fog networks. Fog networks is a complementary part of the cloud network environment. The proposed network architecture is a part of the newly emerged paradigm that extends the network computing infrastructure within the device-driven 5G communication system. This work explores a new design of the fog computing framework to support device-driven communication to achieve better Quality of Service (QoS) and Quality of Experience (QoE). In particular, we focus on, how potential is the fog computing orchestration framework? How it can be customized to the next generation of cellular communication systems? Next, we propose a mobility management procedure for fog networks, considering the static and dynamic mobile nodes. We compare our results with the legacy of cellular networks and observed that the proposed work has the least energy consumption, delay, latency, signaling cost as compared to LTE/LTE-A networks.

A Scheduling Method Based on Packet Combination to Improve End-to-End Delay in TSCH Networks with Constrained Latency

Wireless sensor networks (WSN) are networks for gathering data from sensor nodes that have been applied in industry for a long time. In real-time industrial applications with tight latencies, schedulability is one of the most critical issues. Some authors have proposed centralized scheduling algorithms for time-slotted channel hopping (TSCH) networks for real-time applications in industry, however, they have some disadvantages such as schedulability and high data traffic. In this paper, we improve the schedulability, latency, data traffic by dynamically prioritizing packets which are based on number duplex-conflicts and dynamically combining the packets. As a result, we show that the packet-combining algorithm improves schedulability and minimizes the amount of traffic in a network when compared with existing approaches.

Empirical Performace Determination on Community Detection Techniques in Social Networks

Community identification is the high common and extending field of interest in social and real-time network applications. In recent years, many community detection methods have been developed. This paper describes various community discovery methods such as InfoMap, Clique Guided, Louvain, Newman and Eigen Vector that have already been developed and also compares the experimental results of those proposed techniques. The proposed work in this paper experiments these community mining algorithms on the two real-world datasets Twitter and DBLP (Computer Science Bibliography) networks. The identified communities by all the community mining algorithms for these two data sets are described in this proposed work. The quality of the derived communities is evaluated by using standard Extended Modularity metric. The experiment results show that the InfoMap algorithm produces a good modularity score than other community mining algorithms for different sizes of communities on both data sets.

A QoS Model for Real-Time Application in Wireless Network Using Software Defined Network

Network convergence has vital importance due to limited network resources. Though it decreases network cost, it also reduces Quality of Service (QoS) as well. Due to the increase in real-time network applications, such as video conferencing and voice over IP calls, there is a need to achieve fairness in user's demand through QoS. QoS measurement tasks are more challenging and increased network losses in infrastructure less network. QoS is not yet implemented in Wireless Local Area Network (WLAN) using Software Defined Network (SDN). We have proposed a model to support QoS in WLAN using SDN architecture for real-time traffic. It is efficiently utilizing bandwidth and reducing flow starvation due to centralize control. We named it, an Adaptive QoS model. This model allocates queues dynamically to flows using real-time traffic analysis in SDN architecture. A specific queue is allocated to a specific type of traffic. Flows are dynamically switched based on the increase in traffic demand if other queues are under-use. We achieved handsome improvement in Adaptive QoS model rather than a standard QoS model in terms of throughput and losses.

On Combining Social Media and Spatial Technology for POI Cognition and Image Localization

With fast development of information engineering and social network, people’s locations can be conveniently sensed by spatial technology, such as global positioning systems (GPS), base stations, Wi-Fi access points and even from the appearances of the photos they have taken. The social networks and the online shopping platforms have been gathering billions of users, who share a large amount of images taken in places they live in and visit. We can leverage the social networks to express our opinions about the services and places of interest (POIs). The interactions among users, and user and POIs or services generate big social media data, which have rich information for user, location, and service cognition. Many real-time network applications rely heavily on the accurate social users’ locations. How to sense the locations from multisource social media data is very important and challenging. Thus, in this paper, we give a systematic review of the works that combine social media and spatial technology for POI cognition and image localization.

Network evaluation of virtualisation a real time network applications

The basic use of virtualisation is the optimisation of technical resources, improved service and reduce the cost. Server consolidation based on virtualisation technology that implement redundancy to achieve consolidation of computing resources and provide usability, flexibility, simplify system administration, reduce the cost physical infrastructure. This technology improves utilisation in today's internet service-oriented industry data centres. This service relies on performance of different hardware subsystems. Vendor's researches have shown that performance can be achieved with virtualisation as on physical hardware. Most work done on applications performance testing in virtual solutions is conducted by vendors. In this paper, we present a critical analysis and comparison on virtual solution performance using telecommunication applications to meet high quality of service. We have mainly used in this experiment a real data in a real-time network application.

Network evaluation of virtualisation a real time network applications

The basic use of virtualisation is the optimisation of technical resources, improved service and reduce the cost. Server consolidation based on virtualisation technology that implement redundancy to achieve consolidation of computing resources and provide usability, flexibility, simplify system administration, reduce the cost physical infrastructure. This technology improves utilisation in today's internet service-oriented industry data centres. This service relies on performance of different hardware subsystems. Vendor's researches have shown that performance can be achieved with virtualisation as on physical hardware. Most work done on applications performance testing in virtual solutions is conducted by vendors. In this paper, we present a critical analysis and comparison on virtual solution performance using telecommunication applications to meet high quality of service. We have mainly used in this experiment a real data in a real-time network application.

Adaptive Region Construction for Efficient Use of Radio Propagation Maps

The efficient construction of contours of a radio propagation map is crucial in using radio propagation maps in a number of real-time communication and network applications. In this research work, we first propose an adaptive region construction (ARC) technique capable of constructing contours of different resolutions of a radio propagation map. Next, the process of implementing the ARC technique for real-time execution on a GPU is presented. The drawbacks of the first implementation using only the global memory are discussed, and optimization techniques to improve the performance are discussed and implemented. Simulations are performed with varying sizes of radio propagation maps, and the suitability of the ARC technique for real-time operation is presented. A speedup of 25× is achieved with the shared version of the GPU compared to the sequential CPU implementation. Also, the contour constructed using the ARC technique is compared to that constructed using the convex hull approach demonstrating the higher accuracy of the contour from the ARC technique.

Implementation of 4kUHD HEVC-content transmission

The Internet of things (IoT) has received a great deal of attention in recent years, and is still being approached with a wide range of views. At the same time, video data now accounts for over half of the internet traffic. With the current availability of beyond high definition, it is worth understanding the performance effects, especially for real-time applications. High Efficiency Video Coding (HEVC) aims to provide reduction in bandwidth utilisation while maintaining perceived video quality in comparison with its predecessor codecs. Its adoption aims to provide for areas such as television broadcast, multimedia streaming/storage, and mobile communications with significant improvements. Although there have been attempts at HEVC streaming, the literature/implementations offered do not take into consideration changes in the HEVC specifications. Beyond this point, it seems little research exists on real-time HEVC coded content live streaming. Our contribution fills this current gap in enabling compliant and real-time networked HEVC visual applications. This is done implementing a technique for real-time HEVC encapsulation in MPEG-2 Transmission Stream (MPEG-2 TS) and HTTP Live Streaming (HLS), thereby removing the need for multi-platform clients to receive and decode HEVC streams. It is taken further by evaluating the transmission of 4k UHDTV HEVC-coded content in a typical wireless environment using both computers and mobile devices, while considering well-known factors such as obstruction, interference and other unseen factors that affect the network performance and video quality. Our results suggest that 4kUHD can be streamed at 13.5 Mb/s, and can be delivered to multiple devices without loss in perceived quality.

LiveWSN: A memory-efficient, energy-efficient, reprogrammable, and fault-tolerant platform for wireless sensor network

Memory optimization, energy conservation, over-the-air reprogramming, and fault tolerance are the critical challenges for the proliferation of the wireless sensor network. To address these challenges, a new wireless sensor network platform termed LiveWSN is presented in this article. In LiveWSN, several new design concepts are implemented, such as the hierarchical shared-stack scheduling and the pre-linking native-code reprogramming. By doing so, the data memory cost of the LiveWSN scheduling system can be optimized by 25% if compared with that of the traditional multithreaded MANTIS OS. Moreover, the application reprogramming code size can be decreased by 72.6% if compared with that of the Contiki dynamic-linking reprogramming. In addition to the new design concepts, the new research approach which addresses the energy conservation and fault tolerance challenges by combining the software technique and the multi-core hardware technique is applied in LiveWSN. By means of the multi-core hardware infrastructure, the lifetime of the LiveWSN nodes can be prolonged by 34% if compared with the single-core Live node. Moreover, the fault-tolerant performance of the wireless sensor network node can be optimized significantly. With the above features, LiveWSN becomes memory efficient, energy efficient, reprogrammable, and fault tolerant, and it can run on the high resource-constrained nodes to execute the outdoor real-time wireless sensor network applications.

SPEED-MAC: speedy and energy efficient data delivery MAC protocol for real-time sensor network applications

Although existing wakeup scheduling techniques suggest end-to-end delay guarantees for real-time applications, their fixed wakeup schedules may not meet such constraints when multiple sensors compete for the event delivery at the same time. In this paper we propose a new MAC protocol called SPEED-MAC that can provide real-time delay guarantees with much lower energy consumption for both single-source and multi-source events. The main ideas underlying the protocol are twofold. First, we introduce a novel wakeup technique called signaling wakeup, which is small only enough to detect the event occurrence. By employing the signaling wakeup we can minimize the event report latency as well as the idle listening. Second, to resolve the collisions and contentions incurred by multi-source events, the protocol employs adaptive wakeup that combines static scheduling with contention-based media access control depending on the type of traffic. The result of our analytic evaluation confirms that SPEED-MAC can provide real-time delay guarantees even for a large-scale network assuming a low event rate. We have implemented SPEED-MAC on both NS-2 and MICA2 platforms, and evaluated both the energy and the network performance of the protocol for various scenarios including multi-source, multi-sink, source-to-sink, sink-to-source, multi-cast and broadcast traffic. Our experimentation results show that SPEED-MAC can achieve an order of magnitude energy savings while providing near optimal latency compared to the existing solutions.

Recent outbreaks of infectious syphilis, United Kingdom, January 2012 to April 2014.

Six outbreaks of infectious syphilis in the United Kingdom, ongoing since 2012, have been investigated among men who have sex with men (MSM) and heterosexual men and women aged under 25 years. Interventions included case finding and raising awareness among healthcare professionals and the public. Targeting at-risk populations was complicated as many sexual encounters involved anonymous partners. Outbreaks among MSM were influenced by the use of geospatial real-time networking applications that allow users to locate other MSM within close proximity.

A Uniform Energy Consumption Algorithm for Wireless Sensor and Actuator Networks Based on Dynamic Polling Point Selection

Recent research has indicated that using the mobility of the actuator in wireless sensor and actuator networks (WSANs) to achieve mobile data collection can greatly increase the sensor network lifetime. However, mobile data collection may result in unacceptable collection delays in the network if the path of the actuator is too long. Because real-time network applications require meeting data collection delay constraints, planning the path of the actuator is a very important issue to balance the prolongation of the network lifetime and the reduction of the data collection delay. In this paper, a multi-hop routing mobile data collection algorithm is proposed based on dynamic polling point selection with delay constraints to address this issue. The algorithm can actively update the selection of the actuator's polling points according to the sensor nodes' residual energies and their locations while also considering the collection delay constraint. It also dynamically constructs the multi-hop routing trees rooted by these polling points to balance the sensor node energy consumption and the extension of the network lifetime. The effectiveness of the algorithm is validated by simulation.

CAMEO: A novel context-aware middleware for opportunistic mobile social networks

Mobile systems are characterized by several dynamic components such as user mobility, device interoperability, and interactions among users and their devices. In this scenario, context-awareness and the emerging concept of social-awareness become a fundamental requirement to develop optimized systems and applications. In this paper we present CAMEO, a light-weight context-aware middleware platform for mobile devices designed to support the development of real-time mobile social network (MSN) applications. MSNs extend the paradigm of online social networks with additional interaction opportunities generated by user mobility and opportunistic wireless communications among users which share interests, habits, and needs. Specifically, CAMEO is designed to collect and reason upon multidimensional context information, derived by the local device, the local user, and their physical interactions with other devices and users. It provides a common application programming interface to MSN applications through which they can exploit context- and social-aware functionalities to optimize their features. CAMEO has been implemented on an Android platform together with a real example of an MSN application. Validation and performance evaluation have been conducted through an experimental testbed.