Wireless Multimedia Networks Research Articles

Bayesian decision model based reliable route formation in internet of things

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.

MPIGA – Multipath Selection Using Improved Genetic Algorithm

The Wireless Multimedia Networks (WMNs) have developed due to the extensive applications of wireless devices and increasing availability of lower cost hardware. The WMNs are used to transmit the multimedia content like audio and video streaming and they can be deployed within a lower budget. These networks can also be used in real-time data applications that demand energy-efficient management and awareness of Quality of Service (QoS). The energy resources are limited in the wireless devices that lead to the significant threats on the QoS for WMNs. An energy-efficient routing technique is needed to handle the dynamic topology of WMN that includes a vital resource as energy. The energy-efficient routing method was proposed in this work for the purpose of data communication based on a cluster head selection from each cluster in addition to the multipath route selection to reduce the network overhead and energy consumption. The cluster heads for each cluster are selected based on Node Coverage & average residual energy parameters.In this work, the proposed energy efficient routing algorithm uses improved genetic algorithm (IGA)based on a cost function for dynamic selection of the best path. The proposed cost function uses link lifetime &average link delay parameters to estimate the link cost. The proposed algorithm’s performance compared with other previous routing methods based on extensive simulation analysis. The results showed that the proposed method achieves better performance over three other routing techniques.

Multiple-symbol noncoherent learning detection of coded QAM signals in IEEE 802.15.3 Wireless Multi-media Networks

We consider the noncoherent deep learning problem for coded signal detection under the phase noncoherent channels for remote home healthcare applications with high data rate. In particular, a multiple-symbol noncoherent learning detection (MNLD) scheme based on neural networks is proposed for low-density parity-check (LDPC) coded noncoherent quadrature amplitude modulation (QAM) signals in IEEE 802.15.3 Wireless Multi-media Networks. Our derivation shows that extensive operations for the first kind zero-order modified Bessel function is unavoidable for the implementation of the optimal bit log-likelihood ratio (LLR) for decoding in traditional multiple-symbol detection (MSD) scheme. The perfect estimation of the channel state information (CSI), i.e., a priori information about the variance of the additive white Gaussian noise (AWGN), is also required for the receiver. This is clearly not computationally practical for Wireless Multi-media Networks. Consequently, we developed an improved approach based on feed-forward neural networks to accurately calculate the bit LLR. Furthermore, to decrease the generation size of training set and thus increase the training speed of the proposed neural networks, we uniformly quantize the continuous carrier phase offset (CPO), which is random and unknown, into discrete status. Our simulation results verify the learning efficiency of this simplified training-set generation configuration. The decoding convergence is successfully accelerated and much performance gain is finally achieved when compared with traditional decoding using the perfect bit LLR. This is clearly critical for high reliable transmission of home healthcare information.

A Reliable Link Selection and Data Aggregation Strategy for Wireless Multimedia Networks

The reliable transmission and energy efficiency are required for wireless multimedia networks for maintaining the Quality of Service (QoS). The major objective of the many routing protocols is considered energy savings in wireless networks. The additional challenges would be imposed with the introduction of imaging and video. The massive multimedia data could not be able to handle efficiently by the conventional schemes of data aggregation. This letter proposes a data aggregation factor based efficient data aggregation (DAFLS) that makes use of data aggregation factor (DAF) for link selection and minimizes the data transmission in the network. For selecting the optimal link set, the transmission frequency, transmission energy, and link quality have been used in the proposed DAF model for data aggregation. At the intermittent nodes, the data fusion rate is increased by the proposed model and the data transmission amount is minimized in a network. The proposed DAFLS method is evaluated in terms of effectiveness based on the simulation experiments than the previous data aggregation techniques. The data aggregation ratio and network lifetime have been improved by the proposed method.

Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services

In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network’s data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies.

Optimization Simulation of Balanced Distribution of Multimedia Network Modular Teaching Resources

The balanced distribution of multimedia wireless network modular teaching resources is improved by optimizing and adjusting the balanced distribution of multimedia wireless network modular teaching resources. Based on block grid balanced scheduling and resource spectrum fusion, a balanced distribution method of multimedia wireless network modular teaching resources is proposed. A blockchain Internet of Things accesses control network architecture system for the balanced allocation of multimedia wireless network modular teaching resources established using the priority business scheduling method. Modular resource allocation of multimedia wireless networks and adaptive forwarding are adjusted to control the data packets. The bandwidth guarantee mechanism is used for the equitable allocation of multimedia wireless network modular teaching resources information flow. Multi-service hierarchical management of multimedia wireless network modular teaching resources spectrum is controlled by block grid balanced scheduling and resource spectrum fusion. The high-energy physical computing system structure is used to allocate the resource spectrum balance of the modular teaching resources of the multimedia wireless networks, ensuring the bandwidth requirements of various services. The classified management and balanced allocation of the multimedia wireless networks’ teaching storage, system, and teaching spectrum resources are made possible. The results of the tests show that this method effectively reduces the delay in the allocation of modular multimedia wireless network teaching resources, improves the utilization rate of modular multimedia wireless network teaching resources, and significantly improves their reading and writing performance.

Secure and Sustainable Framework for Cattle Recognition Using Wireless Multimedia Networks and Machine Learning Techniques

Towards the successful operation of any farm, effective livestock management is necessary. The efficiency, affordability, and scalability of management solutions play a crucial role in modern farming. In this paper, a novel low-cost framework is proposed to recognize for health monitoring of individual cattle based on their sensed behavioral activity data and muzzle (nose) pattern image database using incremental decision tree classification techniques and accelerometer-based activity monitoring method. The proposed system performs image prepossessing on the captured muzzle point images of cattle to mitigate the noise, enhance the contrast, and increase quality. We extract the minutiae features to improve the system’s accuracy for recognizing the cattle for accurate and early classification of behavioral activity for health monitoring. The proposed system uses a support vector machine and incremental decision tree classifiers to classify the extracted feature of cattle’s muzzle images and health database. The server has encrypted databases that consist of captured muzzle images, owner database, health sensor database provided by the owners and systems. We use a similarity score measurement using incremental decision tree classification for matching and classify the muzzle point featutes with the database for cattle identification and health monitoring. We also developed a prototype for evaluating the accuracy of the proposed system with 97.99% accuarcy for unique identification of individual cattle.

Resource Allocation using Genetic Algorithm in Multimedia Wireless Networks

Resource allocations in wireless networks is a very challenging task, at one hand wireless networks have scarce resources and suffers from many limitations. At the other hand, typical resource allocation problems requires extensive amount of computations and are usually NP-hard problems. Hence, there is dire need for effective and feasible solutions. Resource allocation problems are concerned in distributing the available network’s resources to all active users in a fair way. Although fairness is hard to define, this work considers the fairness aspects for both, the users and the network operator (service provider). Bio-inspired algorithm are used in many context to provide simple and effective solution tochallenging problems. This works employs Genetic Algorithm to provide effective solution to resource allocation problem for multimedia allocation in wireless networks. The performance of the proposed solution is evaluated using simulation. The obtained simulation results show that the proposed solutionachieved better performance.

Multimedia Wireless-Network-Based Model for Smart Interactive Translation Teaching

With the acceleration of the internationalization process, the demand for English-speaking talents in all walks of life in China is also increasing. However, the current students’ translation ability is weak and cannot meet the demand for talents. This article combines interactive translation teaching mode with multimedia teaching theory to provide an interactive translation teaching method for English translation teaching. This article studies the impact of multimedia-centered interactive translation teaching methods on college students’ English learning ability from five dimensions: classroom interaction, learning motivation, learning autonomy, language skills application, and English performance. From the results of the previous test, the English translation ability of the students in the two classes is almost at the same level before the experiment. However, after adopting the interactive method based on multimedia wireless network in one semester, the English translation scores of the two classes differed greatly. Most students can actively practice English translation in their spare time. As a result of collecting information and reading materials on the Internet and preparing daily speeches in their spare time, students gradually accumulate enough written materials, which can alleviate the anxiety caused by insufficient content. In addition, students also actively communicate with their classmates after completing the translation to improve their composition level. Studies have shown that the number of students who complete translation tasks only after class has dropped from 75% to 31%.

Construction of Modularized English Teaching Platform for Multimedia Wireless Network Based on NS2 Simulation Software

Using the NS2 network simulation tool to teach the “wireless network” course can enhance students’ understanding of wireless network protocols through visualization and can also improve students’ ability to analyze problems hands on. We built a multimedia wireless network modularized English teaching platform based on NS2 simulation software; made full use of the rich components and network resources provided by NS2 to achieve a high degree of code reuse and reduce the time for developing simulation codes; designed the overall framework of this multimedia wireless network modular English teaching platform based on the B/S structure; and solved the weight coefficient and importance value of the learning input evaluation index, based on the feedback elite teaching optimization algorithm, the improved teaching formula, the increase in the number of exchanges, and the improvement in the learner’s performance. The experimental results show that the LEACH and LEACH-C protocols of the clustered NS2 simulation software have more surviving nodes most of the time, which indicates that the clustering routing protocol has better performance.

Performance evaluation of campus network involving VLAN and broadband multimedia wireless networks using OPNET modeler

Modern technology is essential for all healthy, economic, and educational sectors. Using modern technology involves high-performance networks in terms of quality of service (QoS) parameters such as delay, throughput, bandwidth, and security. A campus network involves wire and the wireless environment with and without virtual local area network (VLAN) technology has been simulated using optimized network engineering tools (OPNET). The simulation model includes four scenarios that involve heavy-loaded File transfer protocol (FTP) and web browsing applications with two logical groups of users. The simulation results show that the VLAN overcome LAN networks in terms of bandwidth and security. This has been done by reducing the throughput in both sending and receiving levels to the confidential servers. The VLAN has also reduced the broadcast domain which results in a high-power efficiency. Moreover, the VLAN network technology has been proved to have lower values of delay in transferring files and packets than LAN network technology. In addition, VLAN technology generally has been decreased values of throughput regardless of the way of connection between servers and workstations. This, in turn, allows taking advantage of wireless connection features in terms of flexibility of installation and speed of configuration.

Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks

A traditional big data network is a wireless multimedia network, as multiple video/audio streaming account for 70 percent of mobile traffic and the fifth generation wireless networks are projected to rise 500 times. However, it does raise various new and transparent challenges for multimedia big data communication over 5G wireless systems because multimedia big data services provide timely, throughput broadcasts through time-sensitive communication networks with restricted wireless resources. In order to address the above issues, we suggest in this paper information-centric virtualization architectures for a statistical latency consistency of content delivery across 5G wireless large media platforms for technology. In particular, three successful nominee strategies to promise the mathematical time limit for interactive large-data communications are implemented in our proposed scheme: information-centered network which extracts an optimum in-network storage location for multimedia big data; a virtualization of the network feature which transforms PHY architecture into many virtualized channels. In our architectures we build the 3 primary computer-generated machine collection besides power distribution plans to collectively simplify the application of NFV and SDN techniques in the ICN architecture: to improve performance for single users, to integrate effective combining capability with equity allocation for all users besides under non-cooperative betting Via models and computational assesses, we prove that our suggested designs and applications greatly perform better other existing systems to enable QoS provisioning with statistical delays over 5G immersive Large Data Wireless communication.

Solving the Problem of Target k-Coverage in WSNs Using Fuzzy Clustering Algorithm

The purpose of the present research was to introduce an algorithm to solve the coverage problem in wireless multimedia networks that can be used to optimize energy consumption and network lifetime. In this regard, the problem of target k-coverage in WSNs was solved by dividing the environment into the proportional area and random selection. This can be done using a fuzzy clustering algorithm. It is worth noting that the results of the proposed algorithm were compared with previous methods such as genetic and annealing algorithm. The simulation results and comparison with other algorithms show a 27% superiority of the proposed algorithm. It is hoped that this method can be used in networks with larger dimensions in the future

A Cross-Layer Architecture with Service Adaptability for Wireless Multimedia Networks

A Cross-Layer Architecture with Service Adaptability for Wireless Multimedia Networks

Optimized Dynamic Routing in Multimedia Vehicular Networks

Intelligent transport system is one of thriving research domain and in particular multimedia vehicular networks gains more attention. The deployment of multimedia services in vehicle ad-hoc networks (VANETs) provides real time support to users and provides better traffic management with high safety measures. Most of the vehicles has multimedia applications such as weather sensors, real time map applications etc., and it requires adequate resources to process. Resource allocation to a static network is simple and various routing models are evolved. In case of dynamic network like VANETs, routing is still in progress in order to obtain a better model. Routing directly related to quality of services of network and user, it is essential to develop a better dynamic routing strategy for multimedia wireless networks. The proposed work aims to provide an optimized dynamic routing strategy for multimedia networks. For efficient routing, k-means clustering is used to organize the clusters and exchanges the routing information and inverted ant colony optimization is used to obtain the optimal path for multimedia access. Proposed model is experimentally verified and compared to conventional ant colony optimization and grey wolf optimization in terms of parameters such as end to end delay, throughput, target used, average computation time and efficiency.

A novel energy-efficient data aggregation protocol for cognitive radio based wireless multimedia networks

In recent days, wireless multimedia cognitive radio networks (WMCRN) become a hot research topic due to its wide range of delay intolerant and time-critical applications. However, the increasing requirement of multimedia applications by cognitive radio networks (CRN) poses several design issues like energy efficiency, interferences, computational speed and memory. Due to the battery operated sensor nodes in WMCRN, it is very difficult to upload all multimedia information to the user in remote areas. Therefore, it is highly needed to design an energy-efficient data aggregation technique to minimize the data traffic and lengthen the network lifetime. But the conventional data aggregation schemes are not adaptable to the requirements of WMCRN due to the handling of massive sized multimedia data. In this view, this paper presents a new energy efficient Link Selection with Data Aggregation (EELSDA) Protocol for WMCRN. The proposed EELSDA model makes use of data aggregation factor in the link selection process of the graph routing technique. The proposed EELSDA model increases the possibility of data fusion at the intermittent routing nodes and minimizes the amount of data transmission in the network. The proposed EELSDA model utilizes link quality, communication energy and transmission frequency to select the optimal set of links. Consequently, reliability links to parent nodes with maximum remaining energy will be chosen for ensuring the success ratio for data transmission and total energy balancing of the network. Moreover, the relation among the transmission frequencies of the child as well as parent nodes represents the significant packet delay for data fusion in the specific parent node. A series of simulation results are carried out to ensure the effective performance of the EELSDA protocol over the compared methods in a significant way. The maximum network lifetime has been attained by the EELSDA model than all other models under diverse number of nodes. For sample, under the presence of 350 nodes, the EELSDA approach has obtained maximum lifetime of 2240 while the SPEED, MMSPEED, RAP and SAP technologies have resulted in lower lifetime of 1400, 1690, 1790 and 2100 correspondingly.

Design of Multimedia Education Network Security and Intrusion Detection System

The existing multimedia wireless network security detection technology cannot meet the requirements of wireless network security detection. Therefore, based on the special security requirements of multimedia wireless networks, this paper constructed a multimedia wireless network security detection system that meets the actual situation of the organization. The system was designed using the B/S architecture and used Django as the framework for system development. The system presentation layer mainly presents the system page to the user and passes the user request. Simultaneously, in this paper, the application of PrefixSpan algorithm in anomaly detection was studied and experimental analysis was carried out. The experimental results are in line with expectations. This verifies the effectiveness of the proposed system and provides a theoretical reference for subsequent related research.

Performance simulation of broadband multimedia wireless networks simulation based on OPNET

<p>As the rapid growth of multimedia application over the Internet, it is essential to preserve the Quality of Service (QoS), which is certifying the guaranteed service through the Internet and representing the biggest challenges for the current IP based services. Multimedia traffic usage has been increased in relation to the streaming media such as video conferencing using OPNET, the performance can be simulated based on heavy and light scenarios for video conferencing including web traffic. The overall WLAN load data are obtained for such scenarios, also the performance of simulated overall Delay in the three scenario networks is measured<strong><em>.</em></strong></p>

Research on Video Encryption and Privacy Protection in Cloud Monitoring System Based on H.264

: Cloud monitoring technology is an important maintenance and management tool for cloud platforms.Cloud monitoring system is a kind of network monitoring service, monitoring technology and monitoring platform based on Internet. At present, the monitoring system is changed from the local monitoring to cloud monitoring, with the flexibility and convenience improved, but also exposed more security issues. Cloud video may be intercepted or changed in the transmission process. Most of the existing encryption algorithms have defects in real-time and security. Aiming at the current security problems of cloud video surveillance, this paper proposes a new video encryption algorithm based on H.264 standard. By using the advanced FMO mechanism, the related macro blocks can be driven into different Slice. The encryption algorithm proposed in this paper can encrypt the whole video content by encrypting the FMO sub images. The method has high real-time performance, and the encryption process can be executed in parallel with the coding process. The algorithm can also be combined with traditional scrambling algorithm, further improve the video encryption effect. The algorithm selects the encrypted part of the video data, which reducing the amount of data to be encrypted. Thus reducing the computational complexity of the encryption system, with faster encryption speed, improve real-time and security, suitable for transfer through mobile multimedia and wireless multimedia network.

A real plug-and-play fog: Implementation of service placement in wireless multimedia networks

Initially as an extension of cloud computing, fog computing has been inspiring new ideas about moving computing tasks to the edge of networks. In fog, we often repeat the procedure of placing services because of the geographical distribution of mobile users. We may not expect a fixed demand and supply relationship between users and service providers since users always prefer nearby service with less time delay and transmission consumption. That is, a plug-and-play service mode is what we need in fog. In this paper, we put forward a dynamic placement strategy for fog service to guarantee the normal service provision and optimize the Quality of Service (QoS). The simulation results show that our strategy can achieve better performance under metrics including energy consumption and end-to-end latency. Moreover, we design a real Plug-and-Play Fog (PnPF) based on Raspberry Pi and OpenWrt to provide fog services for wireless multimedia networks.