Fuzzy Analysis for Nodes Deployment Strategies in Wireless Sensor Network

The objective of a best sensor deployment policy is to have a fully connected network while optimizing coverage area at the same time. By optimizing the different parameters like distance, energy level, transmission loss and density of sensor nodes. the deployment plan would guarantee the optimum connectivity of sensor nodes, as required by the essential applications. By making sure that the network is connected, it is also ensured that the sensed data is transmitted to other nodes and perhaps to a centralized base station that can make important decisions for the application. This paper investigates the fundamental parameters of a wireless sensor network: that is optimization of node density, transmission range, transmission loss, residual energy and connectivity

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

Simulation of an Optimized Data Packet Transmission in a Congested Network

Computer network and the Internet nowadays accommodate simultaneous transmission of audio, video, and data traffic among others. Efficient and reliable data transmission is essential for achieving high performance in a networked computing environment. Thus, there is need to optimized data packet transmission in the present day network. This paper simulates and demonstrates the process of optimizing data packet transmission in a congested network. It uses the modified FIFO Queue system to control data packet loss and uses the prototyping software methodology to develop software in Python Programming language for its implementation. From the simulation process, it was observed that causes of packet loss during transmission are largely dependent on protocol, congestion of traffic way, speed of the sender and speed of the receiver’s machine. Thus, the paper takes advantage of the observations from simulation and presents a system that simulates control of data loss during transmission in a congested network. Keywords: Simulation, Auxiliary Queue, Departing Packets, Arrival Packets, Packet Loss

A Theoretical Approach to Optimize the Pipeline Data Communication in Oil and Gas Remote Locations Using Sky X Technology

Oil, gas, and water distribution networks in remote locations require optimized data transmission from their sources to prevent or detect leakage or improve production flow in their manufacturing units. Remote oil and gas installations frequently encounter substantial obstacles in terms of data connectivity and transfer. Slow data transmission rates, data loss, and decision-making delays can all be caused by a lack of dependable network infrastructure, restricted bandwidth, and severe climatic conditions. The purpose of this research work is to identify critical concerns concerning data communication and data transfer in oil and gas distant areas and to investigate feasible approaches to these challenges. The survey was carried out to gather feedback from oil and gas experts on issues concerning data transmission in remote locations. This study provides a theoretical approach to optimizing data transmission and communication in remote areas using Sky X technology. This study presents a new theoretical method that improves the performance of IP over satellite using the critical aspects of data transmission issues from experts. This technology's contribution can improve the reliability of all users on a satellite network by delivering all features with a successful data transfer rate discreetly. This attempt may also aid oil and gas companies in optimizing data transmission/communication in remote regions

ShallowForest: Optimizing All-to-All Data Transmission in WANs

All-to-all data transmission is a typical data transmission pattern in both consensus protocols and blockchain systems. Developing an optimization scheme that provides high throughput and low latency data transmission can significantly benefit the performance of those systems. This thesis investigates the problem of optimizing all-to-all data transmission in a wide area network (WAN) using overlay multicast. I first prove that in a congestion-free core network model, using shallow tree overlays with height up to two is sufficient for all-to-all data transmission to achieve the optimal throughput allowed by the available network resources. Based on this finding, I build ShallowForest, a data plane optimization for consensus protocols and blockchain systems. The goal of ShallowForest is to improve consensus protocols' resilience to skewed client load distribution. Experiments with skewed client load across replicas in the Amazon cloud demonstrate that ShallowForest can improve the commit throughput of the EPaxos consensus protocol by up to 100% with up to 60% reduction in commit latenc

Green Hybrid Satellite Terrestrial Networks: Fundamental Trade-Off Analysis

With the worldwide evolution of 4G generation and revolution in the information and communications technology(ICT) field to meet the exponential increase of mobile data traffic in the 2020 era, the hybrid satellite and terrestrial network based on the soft defined features is proposed from a perspective of 5G. In this paper, an end-to-end architecture of hybrid satellite and terrestrial network under the control and user Plane (C/U) split concept is studied and the performances are analysed based on stochastic geometry. The relationship between spectral efficiency (SE) and energy efficiency (EE) is investigated, taking consideration of overhead costs, transmission and circuit power, backhaul of gateway (GW), and density of small cells. Numerical results show that, by optimizing the key parameters, the hybrid satellite and terrestrial network can achieve nearly 90% EE gain with only 3% SE loss in relative dense networks, and achieve both higher EE and SE gain (20% and 5% respectively) in sparse networks toward the future 5G green communication networks