MULTIMEDIA STREAMING PLATFORM BANDWITH CONTROLL CONGESTION DETECTION AND BANDWIDTH ADAPTATION

We propose a platform for distributed multimedia systems. The proposed platform is implemented using the Netscape Portable Runtime (NSPR) and the Cross-Platform Component Object Model (XPCOM). This ensures system portability, flexibility and performance. The platform is equipped with a congestion detection algorithm and a bandwidth control mechanism thus controlling the transfer rates between the communication parties. Using this kind of bandwidth management this platform allows real-time streaming across multiple networks.real-time multimedia platform; XPCOM components; congestion detection; bandwidth control

SIGNAL STRENGTH AND ENERGY AWARE RELIABLE ROUTE DISCOVERY IN MANET

Frequent changes in network topology and confined battery capacity of the mobile devices are the main challenges for routing in ad-hoc networks. In this paper, we propose a novel, Signal strength and Energy Aware routing protocol (SEA-DSR), which directly incorporates signal strength and residual battery capacity of nodes into route selection through cross layer approach. This model defines a metric called Reliability Factor for route selection among the feasible routes. It is simulated using ns2, under different mobility conditions. The simulation results shows better performance in terms of packet delivery ratio, control overhead and average end-end delay. The proposed model has extended the time to network partition and reduce the path breakages when compared with similar routing protocols DSR and SSA

Enhancing IoT performance via using Mobility Aware for dynamic RPL routing protocol technique (MA-RPL)

Nodes\u27 aware-mobility in the Internet of Things (IoTs) stills open defy for researchers, due to the dynamic changing of routing path and networks’ resource limitations. Therefore, in this study a new method is proposed called Mobility Aware - “Routing Protocol for Low power and Lossy Networks” (MA-RPL), that consists of two phases: in the first phase splitting the entire network into sub areas based on reference nodes with “Time Difference of Arrival” (TDoA) technique. While, the second phase, is about managing mobile nodes (MNs) in RPL according to the sub areas\u27 ID. The Cooja simulator software has been used to implement and assess MA-RPL method performance, according to the data packet metrics (lost packet, packet delivery ratio PDR), latency and nodes\u27 power usage in comparison with two methods: Corona (Co-RPL) and Mobility Enhanced (ME-RPL). The simulation results have been shown that the MA-RPL method consumes less nodes\u27 energy usage, gives less latency with minimum data packet loss in comparison with Co-RPL and ME-RP

Prediction Based Efficient Resource Provisioning and Its Impact on QoS Parameters in the Cloud Environment

The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users

Mapping of SLA-Based Workflows with Light Communication onto Grid Resources

Service Level Agreements (SLAs) are currently one of the major research topics in Grid Computing. Among those system compo- nents that support SLA-aware Grid jobs, the SLA mapping mechanism has an important position. It is responsible for assigning sub-jobs of the work ow to Grid resources in a way that meets the user's dead- line and minimizes costs. Assuming many dierent kinds of sub-jobs and resources, the process of mapping an SLA-based work ow with light communication denes an unfamiliar and dicult problem. This paper presents a solution to this problem. The quality and eciency of the algorithm is validated through performance measurements

An efficient algorithm for modelling and dynamic prediction of network traffic

Network node degradation is an important problem in internet of things given the ubiquitous high number of personal computers, tablets, phones and other equipments present nowadays. In order to verify the network traffic degradation as one or multiple nodes in a network fail, this paper proposes one algorithm based on product form results (PRF) for fractionally auto regressive integrated moving average (FARIMA) model, namely PFRF. In this algorithm, the prediction method is established by FARIMA model, through equations for queuing state and average queue length in steady state derived from queuing theory. Experimental simulations were conducted to investigate the relationships between average queue length and service rate. Results demonstrated that, not only it has good adaptability, but has also achieved promising magnitude of 9.87 as standard deviation which shows its high prediction accuracy, given the low-magnitude difference between original value and the algorithm

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time