An Optimized Bandwidth Estimation for Adaptive Video Streaming Systems Using WLBWO Algorithm

The increasing popularity of streaming video is a cause of concern for the stability of the internet because most streaming video content is currently delivered via UDP without any end-to-end congestion control. Since the internet relies on end systems implementing transmit rate regulation, there has recently been significant interest in congestion control mechanisms that are both fair to TCP and effective in delivering real-time streams. Streaming video over the internet requires dealing with bandwidth and delay that vary over time. Many video streaming applications address this problem by adapting the quality of the scalable video. But it produces poor quality service, and sending data on this channel results in buffering time. To trounce these issues, this paper proposed optimized bandwidth estimation for adaptive video streaming systems using the WLBWO algorithm. Originally, the input video is compressed by using the UHE algorithm. Next, the system proposes a KEECC to securely transfer the data. Then, the encrypted data is sent to the receiver via a multipath channel. Before sending the data to the receiver, the bandwidth is estimated by using the WLBWO. Finally, the inverse process is performed. Extensive experimental results showed the effectiveness of the proposed system than conventional methods.

SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique.

Improving Streaming Capacity In Tree & Meshed Based P2P Live Streaming Systems

The streaming capacity for a channel is defined as the maximum streaming rate that can be achieved by every user in the channel. In the thesis, we investigated the streaming capacity problem in both tree-based and mesh-based Peer-to-Peer (P2P) live streaming systems, respectively. In tree-based multi-channel P2P live streaming systems, we propose a crosschannel resource sharing approach to improve the streaming capacity. We use cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we propose a resource sharing approach to improve the streaming capacity. In mesh-based P2P streaming systems, each peer exchanges video chunks with a set of its neighbors. We formulate the streaming capacity problem into an optimal resource allocation problem. By solving the optimization problem, we can optimally allocate the link rates for each peer, thus improve the streaming capacity.

Improving Streaming Capacity In Tree & Meshed Based P2P Live Streaming Systems

The streaming capacity for a channel is defined as the maximum streaming rate that can be achieved by every user in the channel. In the thesis, we investigated the streaming capacity problem in both tree-based and mesh-based Peer-to-Peer (P2P) live streaming systems, respectively. In tree-based multi-channel P2P live streaming systems, we propose a crosschannel resource sharing approach to improve the streaming capacity. We use cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we propose a resource sharing approach to improve the streaming capacity. In mesh-based P2P streaming systems, each peer exchanges video chunks with a set of its neighbors. We formulate the streaming capacity problem into an optimal resource allocation problem. By solving the optimization problem, we can optimally allocate the link rates for each peer, thus improve the streaming capacity.

Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence

Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

As the demand for video streaming has been rapidly increasing recently, new technologies for improving the efficiency of video streaming have attracted much attention. In this paper, we thus investigate how to improve the efficiency of video streaming by using clients’ cache storage considering exclusive OR (XOR) coding-based video streaming where multiple different video contents can be simultaneously transmitted in one transmission as long as prerequisite conditions are satisfied, and the efficiency of video streaming can be thus significantly enhanced. We also propose a new cache update scheme using reinforcement learning. The proposed scheme uses a K-actor-critic (K-AC) network that can mitigate the disadvantage of actor-critic networks by yielding K candidate outputs and by selecting the final output with the highest value out of the K candidates. The K-AC exists in each client, and each client can train it by using only locally available information without any feedback or signaling so that the proposed cache update scheme is a completely decentralized scheme. The performance of the proposed cache update scheme was analyzed in terms of the average number of transmissions for XOR coding-based video streaming and was compared to that of conventional cache update schemes. Our numerical results show that the proposed cache update scheme can reduce the number of transmissions up to 24% when the number of videos is 100, the number of clients is 50, and the cache size is 5.