Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application

The self-sustainable Wireless Sensor Networks (WSNs) face a major challenge in terms of energy efficiency as they have to operate without replacement of batteries. The benefits of renewable and green energy are taken into consideration for sensing and charging the battery in recent literatures using Energy Harvesting (EH) techniques. The sensors are provided with a reliable energy source through Wireless Charging (WC) techniques. Several challenges in WSN are addressed by combining these technologies. However, it is essential to consider the deployment cost in these systems. This paper presents a self-sustainable energy efficient WSN based model for Mobile Charger (MC) and Energy Harvesting Base Station (EHBS) while considering the cost of deployment. This system can also be used for low-cost microelectronic devices and low-cost Micro-Energy Harvesting (MEH) system-based applications. While considering the deployment cost, the network lifetime is maximized and an extensive comparison of simulation with various existing models is presented to emphasize the validity of the proposed model.

Literature Review of Socioeconomic and Environmental Impacts of High-Speed Rail in the World

Countries considering high-speed rail (HSR) developments face enormous challenges because of their high deployment cost, environmental obstacles, political opposition, and their potentially adverse effects on society. Nevertheless, HSR services are importantly sustainable that can have positive and transformative effects on the economic growth of a nation. This paper systematically reviews and classifies impact areas of HSR deployments around the world as well as the analytical methods used to evaluate those impacts. We have utilized the scholarly scientific database to find articles in HSR systems. By defining some rules, we select 116 articles between 1997 and March 2020. The approach revealed interesting patterns and trends in space, time, and sentiment of the analyzed impacts on society, the economy, and the environment. The findings can inform decision-making about HSR developments and deployments, and the gaps identified in the literature can propose new research opportunities for future studies.

Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied. Then, to optimize the deployment of the whole circular area, a model based on minimum deployment cost is proposed, and the proposed model is divided into two sub-models to solve the optimization issue. In the inner model, it is assumed that the width of a sub-circle is given. Based on the optimization conditions of the deployment pattern, integer linear programming (ILP) and exhaustive method (EM) are jointly adopted to determine the types and numbers of deployment patterns. Moreover, a modified formula is introduced to calculate the maximum valid number of receivers in a pattern, thus narrowing the search scope of the EM. In the outer model, the width of a sub-circle is assumed to be a variable, and the EM is adopted to determine the minimum total deployment cost and the optimal deployment patterns on each sub-circle. Moreover, the improved formula is exploited to determine the range of width for a sub-circle barrier and reduce the search scope of the EM. Finally, simulations are conducted in different conditions to verify the effectiveness of the proposed method. The simulation results indicate that the proposed method can spend less deployment cost and deploy fewer transmitters than the state-of-the-artwork.

Research on Multicore Key-Value Storage System for Domain Name Storage

This article proposes a domain name caching method for the multicore network-traffic capture system, which significantly improves insert latency, throughput and hit rate. The caching method is composed of caching replacement algorithm, cache set method. The method is easy to implement, low in deployment cost, and suitable for various multicore caching systems. Moreover, it can reduce the use of locks by changing data structures and algorithms. Experimental results show that compared with other caching system, our proposed method reaches the highest throughput under multiple cores, which indicates that the cache method we proposed is best suited for domain name caching.

Development of a CubeSat Single Channel LoRa Receiver Module for Space-based IoT Application

CubeSat attracts many researchers due to its low production and deployment cost. One of the application is implemented in low data rate communication or machine to machine (M2M) with IoT devices in remote areas such as islands, forests, and mountains. In this study, a CubeSat receiver for IoT communication in remote areas has been developed and realized. A LoRa SX1276 chip is used for processing passband signals captured by the antenna. The device has a amplifier gain of 20.92 dB, 390 mW power consumption, and operating frequency of 923 MHz. The developed CubeSat is expected to provide a low bit rate of 5468.750 bps for SF 7 and 292.969 for SF 12 , the receiver serves as a concentrator for monitoring devices in rural areas.

Sensor placement optimization for critical-grid coverage problem of indoor positioning

It is more practical and efficient to deploy sensors in critical areas rather than common areas to ensure indoor positioning accuracy and reduce deployment cost. This study focused on the sensor placement optimization for critical-grid coverage problem with two objectives: accuracy and cost. After reviewing some related works, this article proposed a multi-objective optimization model for critical-grid coverage problem of indoor positioning considering k-coverage problem as well as the topological rationality of sensor distribution. Then, NSGA-II algorithm was used to solve the optimizing model of sensor placement. At last, the simulation experiment and real environment validation were conducted for proposed method. The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). As a conclusion, the proposed method could reduce the cost of sensor deployment while ensuring the accuracy of indoor positioning for critical areas. It also provides a new direction for improving the accuracy of indoor positioning.

Service Function Chain Placement for Joint Cost and Latency Optimization

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).