Aggregated Traffic Models for Real-World Data in the Internet of Things

Traffic models play a key role in the analysis, design and simulation of communication networks. The availability of accurate models is essential to investigate the impact of traffic patterns created by the introduction of new services such as those forecasted for the Internet of Things (IoT). The Poisson model has historically been a popular aggregated traffic model and has been extensively used by the IoT research community. However, the Poisson model implicitly assumes an infinite number of traffic sources, which may not be a valid assumption in various plausible application scenarios. The practical conditions under which the Poisson model is valid in the context of IoT have not been fully investigated, in particular under a finite (and possibly reduced) number of traffic sources with random inter-arrival times. In this context, this letter derives exact mathematical models for the packet inter-arrival times of aggregated IoT data traffic based on the superposition of a finite number of traffic sources, each of which is modelled based on real-world experimental data from typical IoT sensors (temperature, light and motion). The obtained exact models are used to explore the validity of the Poisson model, showing that it can be extremely inaccurate when a reduced number of traffic sources is considered. Finally, an illustrative example is presented to show the importance of having accurate and realistic models such as those presented in this letter

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society