Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Studying on the strictly self-compatibility mechanism of 'Liuyefeitao' peach (Prunus persica L.).

Peach (Prunus persica L.) generally exhibits self-pollination, however, they can also be pollinated by other varieties of pollen. Here we found two varieties that are different from other peaches: 'Daifei' and 'Liuyefeitao'. 'Daifei' produces less pollen, which needs artificial pollination, honeybee pollination, and the fruit setting depends on other varieties of peach pollen. 'Liuyefeitao' exhibits strictly self-pollination, hence pollen from other species is rejected. To explore the mechanism of this phenomenon, we performed a high-throughput sequencing of the stigma (including style) of 'Daifei' and 'Liuyefeitao' to explain the rejection mechanism of other varieties of pollen of 'Liuyefeitao' peach. In our study, we found one S gene, and lots of non-S-locus factors such as: F-box proteins, Ub/26S, MAPKs, RLK, and transcription factor were differential expressed between 'Daifei' and 'Liuyefeitao'. We supposed that the strictly self-compatible of 'Liuyefeitao' may result from the synthesis of these factors

Correlation analysis between samples.

<p>A, ‘Daifei’; C, ‘Liuyefeitao’. The bottom left of the graph is the correlation coefficient, the higher the correlation coefficient is, the darker and larger the circle at the upper right of the graph is.</p

Fruit setting rate in self- and cross-fertilization of ‘Daifei’ and ‘Liuyefeitao’.

<p>Fruit setting rate in self- and cross-fertilization of ‘Daifei’ and ‘Liuyefeitao’.</p

GO analysis of DEGs.

<p>Directed Acyclic Graph (DAG) is the graphical display of GO enrichment results with candidate targeted genes (A-C). The number of genes in GO term was showed in histography (D).</p

The expression of ten selected DEGs.

<p>The X-axis represents the gene name, and the Y-axis represents the relative expression of gene. ‘*’indicate significant differences (P < 0.01).</p

KEGG analysis of DEGs.

<p>The area of the circle represents the number of genes, the larger the area of the circle, the greater the number of genes. The color of the circle indicates the degree of enrichment, the higher the degree of enrichment, the more red.</p

Summary of the sequence analyses.

<p>Summary of the sequence analyses.</p