Smart infrastructures: Artificial Intelligence-Enabled lifecycle automation

The deployment and maintenance of large smart infrastructures used for powering data-driven decision making, regardless of retrofitted or newly deployed infrastructures, still lack automation and mostly rely on extensive manual effort. In this article, we focus on the two main challenges in the lifecycle of smart infrastructures: deployment and operation, each of which is rather generic and applies to all infrastructures. We discuss the existing technologies designed to help improve and automate deployment and operation for smart infrastructures in general and use the smart grid as a guiding example to ground some examples across the article. Next, we identify and discuss opportunities where the broad field of artificial intelligence (AI) can help further improve and automate the lifecycle of smart infrastructures to eventually improve their reliability and drive down their deployment and operation costs. Finally, based on the usage of AI for web and social networks as well as our previous experience in AI for networks and cyber-physical systems, we provide decision guidelines for the adoption of AI

Whitelisting in RFDMA networks

Uplink transmissions, within coexisting distinct sub-GHz technologies operating in the same unlicensed band, can be exposed to detrimental impact of the interference. In such scenarios, transmission scheduling becomes important for mitigating interference or minimizing the impact of the interference. For this purpose, we aim to whitelist relatively better channels in terms of their yielded packet reception ratio using our proposed channel quality metric that is based on the received signal-to-interference-plus-noise ratio. In this paper, we investigate the trade-offs of the channel whitelisting in random frequency division multiple access (RFDMA) networks in the presence of the cumulative intra- and inter-technology interferences. Our main findings indicate that, although channel whitelisting reduces the degree of freedom, and thus the overall capacity, it empowers a certain amount of devices to be served at a much lower received signal power, whereas this is infeasible for non-whitelisting scenarios at larger received signal power, which signifies the energy conservation ability of our proposed whitelisting method. It is experimentally demonstrated, on Sigfox, a particular type of RFDMA network, that non-whitelisting scenarios are not capable of supporting any devices at a received signal power below -118 dBm. Even for lower received signal power, we are able to reduce the required number of retransmissions at the same reception probability, which indeed indicates that the overall reliability of the network is improved

Machine learning for wireless link quality estimation: A survey

Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated. A number of recent estimators leverage Machine Learning (ML) techniques that require a sophisticated design and development process, each of which has a great potential to significantly affect the overall model performance. In this article, we provide a comprehensive survey on link quality estimators developed from empirical data and then focus on the subset that use ML algorithms. We analyze ML-based Link Quality Estimation (LQE) models from two perspectives using performance data. Firstly, we focus on how they address quality requirements that are important from the perspective of the applications they serve. Secondly, we analyze how they approach the standard design steps commonly used in the ML community. Having analyzed the scientific body of the survey, we review existing open source datasets suitable for LQE research. Finally, we round up our survey with the lessons learned and design guidelines for ML-based LQE development and dataset collection

Time-to-provision evaluation of IoT devices using automated zero-touch provisioning

The Internet of Things (IoT) is being widely adopted in today's society, interconnecting smart embedded devices that are being deployed for indoor and outdoor environments, such as homes, factories and hospitals. Along with the growth in the development and implementation of these IoT devices, their simple and rapid deployment, initial configuration and out-of-the-box operational provisioning are becoming prominent challenges to be circumvented. Considering a large number of heterogeneous devices to be deployed within next generation IoT networks, the amount of time needed for manual provisioning of these IoT devices can significantly delay the deployment and manual provisioning may introduce human-induced failures and errors. By incorporating zero-touch provisioning (ZTP), multiple heterogeneous devices can be provisioned with less effort and without human intervention. In this paper, we propose software-enabled access point (Soft-AP)- and Bluetooth-based ZTP solutions relying only on a single mediator device and evaluate their performances usi ng LOG-A-TEC testbed against manual provisioning in terms of the time required for provisioning (time-to-provision, TTP). We demonstrate that on average, Soft-AP- and Bluetooth-based ZTP solutions outperform manual provisioning with about 154% and 313% when compared to the expert provisioning, and with about 434% and 880% when compared to the non-expert provisioning in terms of TTP performances, respectively

Mobocertinib in patients with EGFR exon 20 insertion-positive non-small cell lung cancer (MOON)

EGFR exon 20 (EGFR Ex20) insertion mutations in non-small cell lung cancer (NSCLC) are insensitive to traditional EGFR tyrosine kinase inhibitors (TKIs). Mobocertinib is the only approved TKI specifically designed to target EGFR Ex20. We performed an international, real-world safety and efficacy analysis on patients with EGFR Ex20-positive NSCLC enrolled in a mobocertinib early access program. We explored the mechanisms of resistance by analyzing postprogression biopsies, as well as cross-resistance to amivantamab. Data from 86 patients with a median age of 67 years and a median of two prior lines of treatment were analyzed. Treatment-related adverse events (TRAEs) occurred in 95% of patients. Grade ≥3 TRAEs were reported in 38% of patients and included diarrhea (22%) and rash (8%). In 17% of patients, therapy was permanently discontinued, and two patients died due to TRAEs. Women were seven times more likely to discontinue treatment than men. In the overall cohort, the objective response rate to mobocertinib was 34% (95% CI, 24–45). The response rate in treatment-naïve patients was 27% (95% CI, 8–58). The median progression-free and overall survival was 5 months (95% CI, 3.5–6.5) and 12 months (95% CI, 6.8–17.2), respectively. The intracranial response rate was limited (13%), and one-third of disease progression cases involved the brain. Mobocertinib also showed antitumor activity following EGFR Ex20-specific therapy and vice versa. Potential mechanisms of resistance to mobocertinib included amplifications in MET, PIK3CA, and NRAS. Mobocertinib demonstrated meaningful efficacy in a real-world setting but was associated with considerable gastrointestinal and cutaneous toxicity

On designing a machine learning based wireless link quality classifier

Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environment for radio signals significantly varies. In such environments, intelligent algorithms that can provide robust, resilient and adaptive links are being investigated to complement traditional algorithms in maintaining a reliable communication. In this respect, the data-driven link quality estimation (LQE) using machine learning (ML) algorithms is one of the most promising approaches. In this paper, we provide a quantitative evaluation of design decisions taken at each step involved in developing a ML based wireless LQE on a selected, publicly available dataset. Our study shows that, re-sampling to achieve training class balance and feature engineering have a larger impact on the final performance of the LQE than the selection of the ML method on the selected data

Selpercatinib in RET fusion-positive non-small-cell lung cancer (SIREN) : a retrospective analysis of patients treated through an access program

Introduction: Rearranged during transfection (RET) gene fusions are rare genetic drivers in non-small cell lung cancer (NSCLC). Selective RET-inhibitors such as selpercatinib have shown therapeutic activity in early clinical trialshowever, their efficacy in the real-world setting is unknown. Methods: A retrospective efficacy and safety analysis was performed on data from RET fusio-%positive NSCLC patients who participated in a selpercatinib access program (named patient protocol) between August 2019 and January 2021. Results: Data from 50 patients with RET fusion-positive advanced NSCLC treated with selpercatinib at 27 centers in 12 countries was analyzed. Most patients were Non-Asian (90%), female (60%), never-smokers (74%), with a median age of 65 years (range, 38-89). 32% of the patients had known brain metastasis at the time of selpercatinib treatment. Overall, 13 patients were treatment-naïve, while 37 were pretreated with a median of three lines of therapy (range, 1-8). The objective response rate (ORR) was 68% [95% confidence interval (CI), 53-81] in the overall population. The disease control rate was 92%. The median progression-free survival was 15.6 months (95% CI, 8.8-22.4) after a median follow-up of 9 months. In patients with measurable brain metastases (n=8) intracranial ORR reached 100%. In total, 88% of patients experienced treatment-related adverse events (TRAEs), a large majority of them being grade 1 or 2. The most common grade >/=3 TRAEs were increased liver enzyme levels (in 10% of patients), prolonged QTc time (4%), abdominal pain (4%), hypertension (4%), and fatigue/asthenia (4%). None of patients discontinued selpercatinib treatment for safety reasons. No new safety concerns were observed, nor where there any treatment-related death. Conclusions: In this real-world setting, the selective RET-inhibitor selpercatinib demonstrated durable systemic and intracranial antitumor activity in RET fusion-positive NSCLC and was well tolerated