Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.Comment: Best Student Paper Awar

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resource-efficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%

Changes in the gene expression profile during spontaneous migraine attacks

Migraine attacks are delimited, allowing investigation of changes during and outside attack. Gene expression fluctuates according to environmental and endogenous events and therefore, we hypothesized that changes in RNA expression during and outside a spontaneous migraine attack exist which are specific to migraine. Twenty-seven migraine patients were assessed during a spontaneous migraine attack, including headache characteristics and treatment effect. Blood samples were taken during attack, two hours after treatment, on a headache-free day and after a cold pressor test. RNA-Sequencing, genotyping, and steroid profiling were performed. RNA-Sequences were analyzed at gene level (differential expression analysis) and at network level, and genomic and transcriptomic data were integrated. We found 29 differentially expressed genes between ‘attack’ and ‘after treatment’, after subtracting non-migraine specific genes, that were functioning in fatty acid oxidation, signaling pathways and immune-related pathways. Network analysis revealed mechanisms affected by changes in gene interactions, e.g. ‘ion transmembrane transport’. Integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment, i.e. ‘5HT1 type receptor mediated signaling pathway’. In conclusion, we uniquely investigated intra-individual changes in gene expression during a migraine attack. We revealed both genes and pathways potentially involved in the pathophysiology of migraine and/or migraine treatment.publishedVersio

A human mitochondrial poly(A) polymerase mutation reveals the complexities of post-transcriptional mitochondrial gene expression

The p.N478D missense mutation in human mitochondrial poly(A) polymerase (mtPAP) has previously been implicated in a form of spastic ataxia with optic atrophy. In this study, we have investigated fibroblast cell lines established from family members. The homozygous mutation resulted in the loss of polyadenylation of all mitochondrial transcripts assessed; however, oligoadenylation was retained. Interestingly, this had differential effects on transcript stability that were dependent on the particular species of transcript. These changes were accompanied by a severe loss of oxidative phosphorylation complexes I and IV, and perturbation of de novo mitochondrial protein synthesis. Decreases in transcript polyadenylation and in respiratory chain complexes were effectively rescued by overexpression of wild-type mtPAP. Both mutated and wild-type mtPAP localized to the mitochondrial RNA-processing granules thereby eliminating mislocalization as a cause of defective polyadenylation. In vitro polyadenylation assays revealed severely compromised activity by the mutated protein, which generated only short oligo(A) extensions on RNA substrates, irrespective of RNA secondary structure. The addition of LRPPRC/SLIRP, a mitochondrial RNA-binding complex, enhanced activity of the wild-type mtPAP resulting in increased overall tail length. The LRPPRC/SLIRP effect although present was less marked with mutated mtPAP, independent of RNA secondary structure. We conclude that (i) the polymerase activity of mtPAP can be modulated by the presence of LRPPRC/SLIRP, (ii) N478D mtPAP mutation decreases polymerase activity and (iii) the alteration in poly(A) length is sufficient to cause dysregulation of post-transcriptional expression and the pathogenic lack of respiratory chain complexe