AMPLE: An Adaptive Multiple Path Loss Exponent Radio Propagation Model Considering Environmental Factors

We present AMPLE -- a novel multiple path loss exponent (PLE) radio propagation model that can adapt to different environmental factors. The proposed model aims at accurately predicting path loss with low computational complexity considering environmental factors. In the proposed model, the scenario under consideration is classified into regions from a raster map, and each type of region is assigned with a PLE. The path loss is then computed based on a direct path between the transmitter (Tx) and receiver (Rx), which records the intersected regions and the weighted region path loss. To regress the model, the parameters, including PLEs, are extracted via measurement and the region map. We also verify the model in a suburban area. To the best of our knowledge, this is the first time that a multi-slope model precisely maps PLEs and region types. Besides, this model can be integrated into map systems by creating a new path loss attribute for digital maps.Comment: This paper has been submitted to IEEE Transactions for possible publication

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Distributed sensing with low-cost mobile sensors toward a sustainable IoT

Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT

Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT

This zip file contains the dataset used to produce Figure 4 of &quot;Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT&quot;. It contains two folders, the first for the stationary sensors and the second for the mobile sensors.</span