On the Construction of Polar Codes for Achieving the Capacity of Marginal Channels

Achieving security against adversaries with unlimited computational power is of great interest in a communication scenario. Since polar codes are capacity achieving codes with low encoding-decoding complexity and they can approach perfect secrecy rates for binary-input degraded wiretap channels in symmetric settings, they are investigated extensively in the literature recently. In this paper, a polar coding scheme to achieve secrecy capacity in non-symmetric binary input channels is proposed. The proposed scheme satisfies security and reliability conditions. The wiretap channel is assumed to be stochastically degraded with respect to the legitimate channel and message distribution is uniform. The information set is sent over channels that are good for Bob and bad for Eve. Random bits are sent over channels that are good for both Bob and Eve. A frozen vector is chosen randomly and is sent over channels bad for both. We prove that there exists a frozen vector for which the coding scheme satisfies reliability and security conditions and approaches the secrecy capacity. We further empirically show that in the proposed scheme for non-symmetric binary-input discrete memoryless channels, the equivocation rate achieves its upper bound in the whole capacity-equivocation region

Conserving energy through neural prediction of sensed data

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points mak- ing a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energ

A neural data-driven algorithm for smart sampling in wireless sensor networks

Wireless sensor networks (WSN) take on an invaluable technology in many applications. Their prevalence, however, is threatened by a number of technical difficulties, especially the shortage of energy in sensors. To mitigate this problem, we propose a smart reduction in data communication by sensors. Indeed, in case we have a solution to this end, the components of a sensor, including its radio, can be turned off most of the time without noticeable influence on network operation. Thus, reducing the acquired data, the sensors can be idle for longer and power can be saved. The main idea in devising such a solution is to minimize the correlation between the data communicated. In order to reduce the measurements, we present a data prediction method based on neural networks which performs an adaptive, data-driven, and non-uniform sampling. Evidently, the amount of possible reduction in required samples is bounded by the extent to which the sensed data is stationary. The proposed method is validated on simulated and experimental data. The results show that it leads to a considerable reduction of the number of samples required (and hence also a power saving) while still providing a good approximation of the data

Implantable Medical Devices; Networking Security Survey

Abstract The industry of implantable medical devices (IMDs) is constantly evolving, which is dictated by the pressing need to comprehensively address new challenges in the healthcare field. Accordingly, IMDs are becoming more and more sophisticated. Not long ago, the range of IMDs' technical capacities was expanded, making it possible to establish Internet connection in case of necessity and/or emergency situation for the patient. At the same time, while the web connectivity of today's implantable devices is rather advanced, the issue of equipping the IMDs with sufficiently strong security system remains unresolved. In fact, IMDs have relatively weak security mechanisms which render them vulnerable to cyber-attacks that compromise the quality of IMDs' functionalities. This study revolves around the security deficiencies inherent to three types of sensor-based medical devices; biosensors, insulin pump systems and implantable cardioverter defibrillators. Manufacturers of these devices should take into consideration that security and effectiveness of the functionality of implants is highly dependent on the design. In this paper, we present a comprehensive study of IMDs' architecture and specifically investigate their vulnerabilities at networking interface

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Low Power Wireless Sensor Network

Wireless sensor networks (WSN) take on an invaluable technology in many applications. Their prevalence, however, is threatened by a number of technical difficulties. In particular, the shortage of energy in sensors is a serious problem to which many solutions have been proposed in recent years. This thesis takes this area of research one step further and proposes solutions to better conserve energy in sensors. The research conducted can be divided into two parts. The first part is on the design and development of low-power sensors and communication devices capable of monitoring the environment. In this part of research, we first show how smartphones can be employed as a device to acquire data from low-power sensors. Then, by using the idea of duty cycling, we achieve a significant reduction in power consumption in environmental sensing. The second part of this research is on the use of data-driven approaches where scholars suggest reducing the amount of required communication so that more energy can be saved in sensors. The main idea is that the components of a sensor, including its radio, can be turned off most of the time without noticeable influence on the judgments made using the sensed data. In fact, the data not sensed when the sensor is powered down can be predicted using the computational intelligence methods. To do so, we employ a multi-layer perceptron to predict missing environmental data on the basis of what is sensed. We also show that the effectiveness of this technique highly relies on the correlation between the points making the time series of sensed data. Our experimental results evidence the usefulness of the technique we propose in the second part of this research. Indeed, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. It is then observed that sensors can be powered on intermittently without any significant influence on the desired behavior of the sensor network. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy. It is also established that, among the solutions already proposed, the data- driven approach is best suited to Wireless Sensor Networks especially environmental sensing

Low Power Wireless Sensor Network

Wireless sensor networks (WSN) take on an invaluable technology in many applications. Their prevalence, however, is threatened by a number of technical difficulties. In particular, the shortage of energy in sensors is a serious problem to which many solutions have been proposed in recent years. This thesis takes this area of research one step further and proposes solutions to better conserve energy in sensors. The research conducted can be divided into two parts. The first part is on the design and development of low-power sensors and communication devices capable of monitoring the environment. In this part of research, we first show how smartphones can be employed as a device to acquire data from low-power sensors. Then, by using the idea of duty cycling, we achieve a significant reduction in power consumption in environmental sensing. The second part of this research is on the use of data-driven approaches where scholars suggest reducing the amount of required communication so that more energy can be saved in sensors. The main idea is that the components of a sensor, including its radio, can be turned off most of the time without noticeable influence on the judgments made using the sensed data. In fact, the data not sensed when the sensor is powered down can be predicted using the computational intelligence methods. To do so, we employ a multi-layer perceptron to predict missing environmental data on the basis of what is sensed. We also show that the effectiveness of this technique highly relies on the correlation between the points making the time series of sensed data. Our experimental results evidence the usefulness of the technique we propose in the second part of this research. Indeed, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. It is then observed that sensors can be powered on intermittently without any significant influence on the desired behavior of the sensor network. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy. It is also established that, among the solutions already proposed, the data- driven approach is best suited to Wireless Sensor Networks especially environmental sensing

Conserving Energy Through Neural Prediction of Sensed Data

The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy

Conserving Energy Through Neural Prediction of Sensed Data

The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy