Indirect Estimation of Link Delays by Directly Observing a Triplet of Network Metrics

AbstractThis paper presents an improved indirect estimation link delays from a triplet of network metrics; path delays, packet loss rate (PLR), and jitter by using indirect inverse modeling techniques. conventionally a network metric is estimated by directly observing another network parameter. Based on the evidence in the literature that path delays, PLR, and jitter are interdependent, this work exploits this mutual interdependent of this triplet of metrics based on the notion that a better observation leads to better estimation. We applied NTF1 model, a variation of non negative tensor factorization (NTF) for this purpose and estimated link delay from a triplet of metrics. Evaluation process used data from an experimental test bed that consists of standard networking devices. The estimated link delays were correlated to actual link delays to benchmark the accuracy of estimation. Results showed a better correlation between the estimated and measured link delays when a triplet of metrics is used

Deployment of drone-based small cells for public safety communication system

In the event of a natural disaster, communications infrastructure plays an important role in organizing effective rescue services. However, the infrastructure-based communications are often affected during severe disaster events such as earthquakes, landslides, floods, and storm surges. Addressing this issue, the article proposes a novel drone-based cellular infrastructure to revive necessary communications for out-of-coverage user equipment (UE) which is in the disaster area. In particular, a matching game algorithm is proposed using one-to-many approach wherein several drone small cells (DSCs) are deployed to match different UEs to reach a stable connection with optimal throughput. In addition, a medium access control framework is then developed to optimize emergency and high priority communications initiated from the rescue workers and vulnerable individuals. The simulation results show that the throughput for the out-of-coverage UEs are significantly improved when the DSCs are deployed in public safety network while the channel access delay is also notably reduced for emergency communications within the affected areas

Inkjet-printed UHF RFID tag based system for salinity and sugar detection

This article presents an RFID system to detect the salinity and sugar contents of water. The proposed system is based on low‐cost ink‐jet printed passive ultrahigh frequency (UHF) RFID tag. The tag is designed using slot match technique, which poses a good imaginary impedance match with RFID chip both in free space and after mounting on the water bottle. Moreover, the tag antenna is exploited as a sensor to detect salt and sugar contents of water by measuring the backscatter power from the tag in term of received signal strength indicator (RSSI). A Tagformance Pro setup form Voyantic is used for measuring RSSI. Furthermore, an approximate relationship is derived between backscatter power and no. of grams of salt and sugar dissolved in water. This study paves a way to check the contents of drinks using portable devices, which is pivotal for healthcare applications in smart cities and the future Internet of things (IoT)

Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.PJN and SR were funded by the Medical Research Council. PJN also acknowledges partial support from the NIHR Cambridge Biomedical Research Centre.This is the accepted manuscript. The final version is available from SAGE at http://dx.doi.org/10.1177/096228021454874

An Efficient Channel Access Scheme for Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are getting more popularity due to the potential Intelligent Transport Systems (ITS) technology. It provides many efficient network services such as safety warnings (collision warning), entertainment (video and voice), maps based guidance, emergency information, etc. VANETs most commonly use Road Side Units (RSUs) and Vehicle-to-Vehicle (V2V) referred as Vehicle-to-Infrastructure (V2I) mode for data accessing. IEEE 802.11p standard which was originally designed for Wireless Local Area Networks (WLANs) is modified to address such type of communication. However, IEEE 802.11p uses Distributed Coordination Function (DCF) for communication between wireless nodes. Therefore, it does not perform well for high mobility networks such as VANETs. Moreover, in RSU mode timely provision of data/services under high density of vehicles is challenging. In this paper, we propose a RSU-based efficient channel access scheme for VANETs under high traffic and mobility. In the proposed scheme, the contention window is dynamically varied according to the times (deadlines) the vehicles are going to leave the RSU range. The vehicles with shorter time deadlines are served first and vice versa. Simulation are performed by using the Network Simulator (NS-3) v. 3.6. The simulation results show that the proposed scheme performs better in terms of throughput, backoff rate, RSU response time, and fairness

Heuristic edge server placement in Industrial Internet of Things and cellular networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current Information and Communications Technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay is a challenging problem. In this paper, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom’s base station the dataset that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multi-objective constraint optimization problem that places edge servers strategically to the balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base-stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hillclimbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost functio

Development of an Intelligent Real-time Multi-Person Respiratory Illnesses Sensing System using SDR Technology

Respiration monitoring plays a vital role in human health monitoring, as it is an essential indicator of vital signs. Respiration monitoring can help determine the physiological state of the human body and provide insight into certain illnesses. Recently, non-contact respiratory illness sensing methods have drawn much attention due to user acceptance and great potential for real-world deployment. Such methods can reduce stress on healthcare facilities by providing modern digital health technologies. This digital revolution in the healthcare sector will provide inexpensive and unobstructed solutions. Non-contact respiratory illness sensing is effective as it does not require users to carry devices and avoids privacy concerns. The primary objective of this research work is to develop a system for continuous real-time sensing of respiratory illnesses. In this research work, the non-contact software-defined radio (SDR) based RF technique is exploited for respiratory illness sensing. The developed system measures respiratory activity imprints on channel state information (CSI). For this purpose, an orthogonal frequency division multiplexing (OFDM) transceiver is designed, and the developed system is tested for single-person and multi-person cases. Nine respiratory illnesses are detected and classified using machine learning algorithms (ML) with maximum accuracy of 99.7% for a single-person case. Three respiratory illnesses are detected and classified with a maximum accuracy of 93.5% and 88.4% for two- and three-person cases, respectively. The research provides an intelligent, accurate, continuous, and real-time solution for respiratory illness sensing. Furthermore, the developed system can also be deployed in office and home environments

A comparison of software defined network (SDN) implementation strategies

Publisher's Version/PDF2nd International Workshop on Survivable and Robust Optical Networks (IWSRON)Software defined networking (SDN) is an emerging approach to handle data forwarding and control separately. The notion of programmability has central importance in SDN. Two implementation strategies; proprietary and open source, are shaping the trends of the adoptability of SDN by major hardware manufacturers. A group of leading vendors believes that loose coupling between the logical and physical layers of a network hinders the proper provision of physical resources and suggests a proprietary fix to this problem. The other group regards the notion of openness as s key feature of SDN. This paper compares and contrasts these two implementation strategies of SDN by identifying their respective operating principles, features of the product lines, and weakness and strengths

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic