A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Narrow-band imaging bronchoscopy in the detection of premalignant airway lesions: a meta-analysis of diagnostic test accuracy

Objectives: Both autofluorescence imaging bronchoscopy and narrow-band imaging have shown promise in the detection of premalignant airway lesions, each by utilizing different bandwidths of lights for better characterization of the mucosal and submucosal vascular grid. Since previously published meta-analyses have shown poor specificity of autofluorescence imaging bronchoscopy, we specifically studied the diagnostic accuracy of narrow-band imaging alone and in combination with autofluorescence imaging bronchoscopy in the detection of premalignant airway lesions. Methods: After an extensive search of eligible studies from PubMed and Medline, extracted data were pooled with weighted averages. Symmetrical summary–receiver operating characteristic curves were constructed to summarize the results quantitatively. Study heterogeneity was assessed by the I 2 index. Results: Analysis of data from eight studies on narrow-band imaging showed a pooled sensitivity of 0.80 [95% confidence interval (CI): 0.77–0.83] and a pooled specificity of 0.84 (95% CI: 0.81–0.86). Summary–receiver operating characteristic curves from the data on narrow-band imaging calculated an area-under-the-curve of 0.908 (standard error 0.01). The diagnostic odds ratio of narrow-band imaging was 31.49 (95% CI: 12.17–81.45). Data from studies where narrow-band imaging and autofluorescence imaging bronchoscopy were used together showed a pooled sensitivity, specificity, area-under-the-curve and diagnostic odds ratios of 0.86 (95% CI: 0.82–0.89), 0.75 (95% CI: 0.71–0.79), 0.964 (standard error 0.05) and 27.96 (95% CI: 3.04–257.21), respectively. Conclusions: Our findings indicate that in the evaluation of premalignant airway lesions, narrow-band imaging has a higher sensitivity, specificity and diagnostic odds ratios compared with autofluorescence imaging bronchoscopy. However, combining autofluorescence imaging bronchoscopy and narrow-band imaging does not significantly improve test performance characteristics

A novel framework for G/M/1 queuing system based on scheduling-cum-polling mechanism to analyze multiple classes of self-similar and LRD traffic

Provisioning guaranteed Quality of Service (QoS) in multiservice wireless internet is challenging due to diverse nature of end-user traffic (e.g., voice, streaming video, interactive gaming) passing through heterogeneous interconnected domains with their own policies and procedures. Numerous studies have shown that multimedia traffic carried in wireless internet possesses self-similar and long-range dependent characteristics. Nonetheless, published work on wireless traffic modeling is merely based on traditional Poisson traffic distribution which fails to capture these characteristics and hence yield misleading results. Moreover, existing work related to self-similar traffic modeling is primarily based on conventional queuing and scheduling combinations which are simple approximations.This paper presents a novel analytical framework for G/M/1 queuing system based on realistic internet traffic distribution to provide guaranteed QoS. We analyze the behavior of multiple classes of self-similar traffic based on newly proposed scheduling-cum-polling mechanism (i.e., combination of priority scheduling and limited service polling model). We formulate the Markov chain for G/M/1 queuing system and present closed form expressions for different QoS parameters i.e., packet delay, packet loss rate, bandwidth, jitter and queue length. We develop a customized discrete event simulator to validate the performance of the proposed analytical framework. The proposed framework can help in building comprehensive service level agreements for heterogeneous wireless domains

Diagnostic yield of electromagnetic navigational bronchoscopy

Objectives: Peripheral lung nodules (PLNs) are a common and diagnostically challenging finding. Electronavigational bronchoscopy (ENB) is used to increase the diagnostic yield and is considered safe. Multiple factors have been correlated with a better diagnostic yield. We sought to assess the effect of nodule characteristics and prior workup on the diagnostic yield in ENB. Methods: This was a retrospective chart review of 98 ENB procedures in a community referral center. Two investigators reviewed patients’ charts and images independently. Multiple logistic regression analyses was used to determine if factors such as bronchus sign, ground glass opacification (GGO), distance from pleura, prior use of endobronchial ultrasound (EBUS) and positron emission tomography (PET) had an impact on the diagnostic yield. Results: We evaluated 98 ENBs performed in 92 patients. Most of the lesions were in the upper lobes. The diagnostic yield was 60%. A PET scan was performed prior to ENB in 47% of cases. EBUS was performed in 24% of cases. Bronchus sign was present in 60% of cases and GGO in only 6% of nodules. The odds ratio for diagnostic yield with a bronchus sign was 1.89 [95% confidence interval (CI): 0.83–4.33] and with nodules showing GGO characteristics it was 4.51 (95% CI: 0.51–39.68). Pneumothorax occurred in 6% of cases. Conclusion: In our cohort, diagnostic yield was 60% with a 6% pneumothorax rate. A suggestive trend for the presence of bronchus sign on computed tomography scan, albeit statistically nonsignificant, as a predictor for improved diagnostic yield needs to be validated in a larger cohort

UAV-assisted 5G networks for optimised coverage under dynamic traffic load

No abstract available