Wearable Communications in 5G: Challenges and Enabling Technologies

As wearable devices become more ingrained in our daily lives, traditional communication networks primarily designed for human being-oriented applications are facing tremendous challenges. The upcoming 5G wireless system aims to support unprecedented high capacity, low latency, and massive connectivity. In this article, we evaluate key challenges in wearable communications. A cloud/edge communication architecture that integrates the cloud radio access network, software defined network, device to device communications, and cloud/edge technologies is presented. Computation offloading enabled by this multi-layer communications architecture can offload computation-excessive and latency-stringent applications to nearby devices through device to device communications or to nearby edge nodes through cellular or other wireless technologies. Critical issues faced by wearable communications such as short battery life, limited computing capability, and stringent latency can be greatly alleviated by this cloud/edge architecture. Together with the presented architecture, current transmission and networking technologies, including non-orthogonal multiple access, mobile edge computing, and energy harvesting, can greatly enhance the performance of wearable communication in terms of spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin

Cluster analysis for 94 strains of <i>Brucella abortus</i> based on the MLVA-16 dataset.

<p>In the columns, the following data for strains are indicated: Key, serial number for the strain in the MLVA bank; GT, genotype MLVA16 in this study; MLVA-8 and MLVA-11, genotype numbers associated with the genotypes corresponding to each strain in the database; region, geographic region (NKR, North Kazakhstan Region; EKR, East Kazakhstan Region; WKR, West Kazakhstan Region); host, animal host; year, year of isolation.</p

Epidemiology of Brucellosis and Genetic Diversity of <i>Brucella abortus</i> in Kazakhstan

<div><p>Brucellosis is a major zoonotic infection in Kazakhstan. However, there is limited data on its incidence in humans and animals, and the genetic diversity of prevalent strains is virtually unstudied. Additionally, there is no detailed overview of Kazakhstan brucellosis control and eradication programs. Here, we analyzed brucellosis epidemiological data, and assessed the effectiveness of eradication strategies employed over the past 70 years to counteract this infection. We also conducted multiple loci variable-number tandem repeat analysis (MLVA) of <i>Brucella abortus</i> strains found in Kazakhstan. We analyzed official data on the incidence of animal brucellosis in Kazakhstan. The records span more than 70 years of anti-brucellosis campaigns, and contain a brief description of the applied control strategies, their effectiveness, and their impact on the incidence in humans. The MLVA-16 method was used to type 94 strains of <i>B</i>. <i>abortus</i> and serial passages of <i>B</i>. <i>abortus</i> 82, a strain used in vaccines. MLVA-8 and MLVA-11 analyses clustered strains into a total of four and seven genotypes, respectively; it is the first time that four of these genotypes have been described. MLVA-16 analysis divided strains into 28 distinct genotypes having genetic similarity coefficient that varies from 60 to100% and a Hunter & Gaston diversity index of 0.871. MST analysis reconstruction revealed clustering into "Kazakhstani-Chinese (Central Asian)", "European" and "American" lines. Detection of multiple genotypes in a single outbreak confirms that poorly controlled trade of livestock plays a crucial role in the spread of infection. Notably, the MLVA-16 profile of the <i>B</i>. <i>abortus</i> 82 strain was unique and did not change during 33 serial passages. MLVA genotyping may thus be useful for epidemiological monitoring of brucellosis, and for tracking the source(s) of infection. We suggest that countrywide application of MLVA genotyping would improve the control of brucellosis in Kazakhstan.</p></div

Incidence of animal brucellosis.

<p>Time in years is on the x-axis, seroprevalence (%) is on the y-axis.</p

Allelic types and HGDI of <i>B</i>. <i>abortus</i> strains for 16 loci in this study.

<p>Allelic types and HGDI of <i>B</i>. <i>abortus</i> strains for 16 loci in this study.</p

Incidence of human brucellosis.

<p>Time in years is on the x-axis, incidence per 100,000 population is on the y-axis.</p

Geographical representation of <i>Brucella abortus</i> sample collection sites.

<p>Geographical representation of <i>Brucella abortus</i> sample collection sites.</p

Information on whole mitochondrial genome sequences which used for comparison.

Information on whole mitochondrial genome sequences which used for comparison.</p

Mitochondrial genomes (N = 2074) from the Central Eurasia for comparative analyses.

(XLSX)</p

Schematic phylogenetic tree for the haplotype D5a2a1.

Note: Bur: Buryat; Kaz: Kazak; Kir: Kirghiz; Tib: Tibetan; Uyg: Uyghur; The following sequences were obtained from the Phylotree Build 17.0 [46]: AP013256, AP008854, AP013197, AP010743, AP011023, AP008536, AP013256, and AP009424 are Japanese sequences; JF824956 is from China, but unknown ethnic origin; FJ383195 is from India; AY255162 is a Han Chinese sequence.</p