Network Programming Research Articles

Survival trends in hilar and extrahepatic cholangiocarcinoma based on Facility type: A National Cancer Database analysis.

e13536 Background: The diagnosis and treatment of Cholangiocarcinoma continues to pose a challenge. Due to their rarity, few studies have examined the impact of facility type on the survival of patients with extrahepatic cholangiocarcinoma. The hypothesis of this study was that differences in the outcome of patients at different facility types exist. Methods: The National Cancer Database (NCDB) was used to identify patients diagnosed with hilar and extra-hepatic cholangiocarcinoma from 2004 to 2017 using ICD-O-3 histology codes. We used four different cohorts to assess survival amongst those at different facility types: academic/research programs, community cancer programs, comprehensive community center programs, and integrated network cancer programs. Data was analyzed and descriptive statistics and Kaplan Meier curves were produced using SPSS to measure survival in these patients with statistical significance set at α = 0.05. Results: Using the database, a total of 57,146 patients with hilar and extrahepatic cholangiocarcinoma were identified. Overall median survival in these patients was 11.2 months. Patients at Academic/research programs had a median survival of 14 months (95% CI 14.1-14.8 p < 0.001). In comparison, median survival of 9.7 months (95%CI 9.3-10.2 p < 0.001) and 8.7 months (95% CI 8.4-8.9 p < 0.001) was noted in Integrated network and Comprehensive community cancer programs, respectively. Community cancer programs had the lowest median survival rate in patients with all extrahepatic biliary tract cancers included in our study was 6.9 months (95% CI 6.4-7.7 p < 0.001). Specifically, patients with Klatskin tumors at Academic/Research programs had median survival of 10.3 months (95% CI 8.6-11.9 p < 0.0010) compared to median survival of 3.6 months in patients treated at Community Cancer program. We also studied specific histology types such as papillary and mucinous adenocarcinomas. In papillary adenocarcinomas, there are no statistically significant differences in median survival in different facility types. In mucinous adenocarcinomas, median survival of 26.5 months was found in academic/research program patients compared to 16.2 months in Comprehensive Community Cancer program patients (p < 0.001). Conclusions: Median overall survival in patients with hilar and extrahepatic cholangiocarcinoma was significantly higher in academic/research programs compared to other facility types. Further studies are needed to ascertain why academic/research programs have superior outcomes in these patients.

Sex Differences in Diet-induced Obesity: Identification of Key Genes in Association With Phenotypes

ObjectivesObesity is associated with many metabolic disorders requiring personalized management. In this study, we examined sex-dependent metabolic changes in diet-induced fat accumulation and tissue-specific transcriptomics to identify responsible genes. Estrogen-dependency was also evaluated.MethodsMale, female and ovariectomized female C57BL/6J mice fed with high fat diet were maintained for 10wks. At sacrifice, body weight, tissue weight, fasting blood glucose and insulin, leptin and adiponectin were measured. Adipose tissue histology and the quantification of major proteins involved in fat synthesis and oxidation were carried out. Key genes in major tissues were identified based on microarray analyses followed by network analyses using protein-protein network and STRING programs.ResultsFemale animals showed significantly lower body weight, adipocyte size, and the macrophage infiltration in white adipose tissue, which are increased by ovariectomy. Not like leptin, adiponectin concentration is significantly higher in female animals, which is maintained even after the ovariectomy. The expressions of proteins related to the regulation of lipid metabolism in adipose tissue show sex differences with or without estrogen depletion. Microarray analyses reveal that ovariectomized female group-specific genes up-regulated in fat, liver and muscle tissue are 251,148, and 49 in numbers, respectively. The number of down-regulated genes are 329, 86, and 37, respectively. PPI network analysis identified Cxcr3, Il2ra, Il2rg, Lck, and Ccl5 as top five up-regulated genes showing highest interaction score, while Cyc1, Uqcrc1, Atp5d, Ndufa9, and Ndufs8 are down-regulated with highest scores. Similarly, the top 5 genes up-regulated in liver tissue are Emr1, Itgb2, Igsf6, Clec4a3, and Aif1, while Eed, Myh11, and Tjp2 are down-regulated with highest scores. Cd4, Slco2b1, Fbxw11, Tub, Wnt5b, U2af2, and Cd38 are up-regulated and the Egfr is down-regulated with highest score in muscle tissue.ConclusionsThis study suggests that biochemical alterations in abdominal obesity in females are different from males, and a part of mechanisms are estrogen-independent. Several key genes associated with these changes are suggested.Funding SourcesThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT).

Facility type and late-stage Thyroid Hürthle Cell Adenocarcinoma: A NCDB Review.

e18074 Background: Hürthle cell adenocarcinoma, or oxyphilic adenocarcinoma, is a rare and invasive cancer accounting for 3-4% of all thyroid malignancies. Surgical removal is the most common treatment. Survival differences based on treatment facility have been identified in other cancers, but this has not yet been studied in Hürthle cell adenocarcinoma. This study utilizes the National Cancer Database (NCDB) to investigate facility survival differences. Methods: Using the NCDB, 2,900 patients with stage III or IV oxyphilic adenocarcinoma of the thyroid were identified with ICD-O-3 histology code 8290/3 and primary site C739 between years 2004 and 2016. Facility types included community cancer, comprehensive community cancer, academic/research, and integrated network cancer programs. Between-facility type survival differences were estimated by the Kaplan-Meier method and associated log-rank tests. Descriptive statistics of race, sex, stage, facility location, urban/rural 2013, primary payer, median income quartile, and Charlson-Deyo score were used. All analysis was conducted using SPSS statistical software version 27. Results: Survival data was available for 2,017 stage III patients and 644 stage IV patients. In stage III, survival was lower in community programs when compared to academic/research (p = .011) and integrated network programs (p = .017). In stage IV, survival was also lower in community programs when compared to academic/research (p = .007) and integrated network programs (p = .011). No significant difference was seen within other comparisons. In both stages, the highest percentage of patients were white, had a Charlson-Deyo score of 0, and were treated in an urban metro area. Sex and facility location were variable among all stages and facilities. In stage III, private insurance was most common overall and for academic/research programs, but Medicare was the majority for other facility types. In stage IV, Medicare was most common overall and for each facility. The median income quartile was > $63,000 across all facility types for stage III and overall for stage IV, although stage IV treated in community and integrated network programs were most commonly between $48,000-$62,999. Conclusions: Patients with late-stage Hürthle cell adenocarcinoma have worse survival when treated in a community program than other facility types. Most demographics were consistent across stage and facility type, except for sex, primary insurance, median income, and facility location. This data contributes to a developing understanding of differences in cancer survival.[Table: see text]

Odds of stage IV bone cancer diagnosis based on socioeconomic and geographical factors.

e23506 Background: There are significant differences in prognosis for osteosarcoma, Ewing Sarcoma, chondrosarcoma, & chordomas based on stage at diagnosis. 5-year survival at early stage vs late stage is as follows; osteosarcoma 75% vs 27%, Ewing sarcoma 82% vs 39%, chondrosarcoma 78% vs 22%, and chordomas 87% vs 55%. This study seeks to evaluate the socioeconomic and geographical factors that affect the odds of late-stage bone cancer diagnosis. Methods: This study retrospectively evaluated the risk of stage I vs stage IV cancer at diagnosis in patients with primary malignant bone tumors (osteosarcoma, chondrosarcoma, Ewing sarcoma & chordoma) diagnosed and recorded in the National Cancer Database (NCDB) between 2004 and 2018. Patients were identified by ICD-O-3 codes and patients with other malignancies were excluded. Cross Tabulations with Chi-square analysis was performed to evaluate frequencies of different socioeconomic and geographical characteristics between groups. Multivariable binary logistic regression was performed to evaluate relationships between socioeconomic and geographical factors and the odds of stage IV cancer. Statistical significance was set at α = 0.05. Results: 11,945 patients with stage I or stage IV primary malignant bone tumors were identified. Odds of stage IV bone cancer at diagnosis increased in patients of greater age (odds ratio [OR] = 1.011, 95% confidence interval [CI]: 1.003-1.018). Odds of stage IV bone cancer at diagnosis were decreased with female sex (OR = 0.747, 95% CI: 0.647-0.862), private insurance (OR = 0.519, 95% CI: 0.367-0.732), Medicare insurance (OR = 0.664, 95% CI: 0.456-0.965), or with diagnosis at comprehensive cancer center programs (OR = 0.549, 95% CI: 0.371-0.814), academic/research programs (OR = 0.339, 95% CI: 0.232-0.495), or integrated cancer network programs (OR = 0.392, 95% CI: 0.261-0.587). No significant relationship was identified between stage at diagnosis and race, ethnicity, Charlson-Deyo score, income, education, region, travel distance, or urban/rural status. Conclusions: Odds of stage IV bone cancer at diagnosis are greater with increasing age, male sex, non-private or non-Medicare insurance status, or treatment at community cancer programs.

Treatment facility: Effects on stage IV osteosarcoma survival.

e23501 Background: Osteosarcoma is a malignant, bone-forming tumor that makes up approximately 20% of bone cancers. Osteosarcoma has an overall 5-year survival of 68%, so to see significant changes in survival, this study looks only at Stage IV osteosarcoma. Osteosarcoma is most commonly treated with a combination of chemotherapy and surgery, but no studies have been conducted to investigate the facility type at which treatment is received and its effect on survival outcomes. The goal of this study is to investigate the survival of patients with stage IV osteosarcoma based on treatment facility type. Methods: There were 1,634 patients with stage IV osteosarcoma identified in the National Cancer Database (NCDB), with 657 including data on facility type. SPSS version 27 was used to generate both descriptive statistics regarding demographics and Kaplan-Meier curves to determine the impact treatment facility type has on overall survival. Results: Stage IV osteosarcoma was more common in males (59.5%) and white race (77%) with 55.4% receiving treatment at academic centers, 22.5% at comprehensive community programs, 16.9% at integrated cancer programs, and 5.2% at community cancer programs. Patients on Medicare also demonstrated a decreased overall survival when compared to all other insurance types (p < 0.001). In stage IV osteosarcoma patients, there were statistically significant differences in survival outcomes when comparing each facility type to each other (with p values all < 0.05), except for the comparison between comprehensive community cancer programs and integrated network cancer programs. Patients who received treatment at academic/research programs demonstrated the best overall survival with a median of 7.29 months when compared to community center programs, comprehensive community cancer programs, and integrated network programs (with p values of < 0.001, 0.046, and 0.03, respectively). Patients treated at academic research programs were more likely to be white males with private insurance or Medicare. Conclusions: Patients treated at academic centers for stage IV osteosarcoma had better survival outcomes compared to those treated at nonacademic facilities. These patients were more likely to be white, insured individuals. African Americans and Medicare patients were associated with increased risk of mortality from stage IV osteosarcoma. Future studies can be done with multivariate analyses to determine other variables that may affect outcomes in this patient population.[Table: see text]

The use of sentinel lymph node biopsy in patients undergoing mastectomy for DCIS.

e12572 Background: Current NCCN guidelines do not recommend sentinel node biopsy (SLNB) for the treatment of DCIS, except in the setting of a mastectomy. The extent of utilization of SLNB in patients undergoing mastectomy for DCIS and which factors are associated with a higher likelihood of SLNB use is unknown. The objective of this study is to evaluate the use of SLNB in patients undergoing mastectomy for DCIS and determine predictors of SLNB utilization. Methods: A retrospective cohort analysis was performed using the NCDB. Women diagnosed with DCIS who underwent a unilateral or bilateral mastectomy from 2012-2017 were included. Patient, tumor, and treatment variables were compared between patients who received a SLNB and those who did not. A multivariate logistic regression model was then used to identify factors predictive of SLNB utilization. Results: A total of 29,962 patients met inclusion criteria. Of these, 25,734 (85.9%) underwent SLNB and 4,228 (14.1%) had no surgical axillary staging. Most patients were age 50-69 (52.0%), non-Hispanic white (71.7%), and had private insurance (65.9%). In those who underwent SLNB, only 10 (<0.01%) were found to have nodal involvement. On multivariate analysis, older patients were less likely to receive SLNB ( p<0.05), while those with grade 2 and 3 DCIS were more likely to undergo SLNB (OR 1.4 and 2.2, respectively, p<0.001) (Table). Patients who were treated from 2014-2017 were also more likely to undergo SLNB (OR 1.2, p<0.01) as were patients treated in the Atlantic and West Central United States ( p<0.05). Treatment at a comprehensive community program, academic/research program, or integrated network program were also more likely to undergo SLNB (OR 1.5, 2.1, 2.3 respectively, p<0.001) compared to community programs. Conclusions: Patterns of SLNB utilization in DCIS varied in clinical practice. Given the low rate of nodal involvement in DCIS patients (<0.01%), and with a 5-10% rate of upper extremity lymphedema after SLNB alone, it may be reasonable to omit SLNB in the setting of a mastectomy for DCIS.[Table: see text]

Neural network programming: Integrating first principles into machine learning models

This work introduces Neural Network Programming (NNP) as an integrated hybrid modelling approach. NNP consists in formulating a set of first principles equations that is later decomposed and transcribed into an Algorithmically Structured artificial Neural Network (ASNN). NNP leverages the advantages of the universal approximation theorem and neural network optimization algorithms in order to generate physically coherent machine learning models. Since ASNNs are not mere approximations of physics equations, it is not necessary to modify either the gradient or performance function in order to account for errors with respect to the first principles equations. ASNNs are trained faster and more accurately than typical hybrid models because the gradient is computed through automatic differentiation instead of numeric differentiation. It is shown that the same ASNN architecture is transferable between processes with similar characteristics. In particular, a flash separator, distillation column, and a biogas upgrading process, were modelled using an identical architecture.

POLIMORFISME GEN MITOKONDRIA 16S IKAN BAUNG (Hemibagrus nemurus Valenciennes, 1840) DARI SUNGAI PROGO, MAGELANG, JAWA-TENGAH

Abstract: Asian redtail catfish (Hemibagrus nemurus Valenciennes, 1840) is one of Indonesian native fish species with high economic potency. However research on genetic variation of Asian redtail catfish in Indonesia is very limited. Therefore, this study purposed to identify and analyze the mitochondrial gene polymorphism 16S of Baung fish (H. nemurus) from the Progo River, Magelang, Central Java. The method used in this study was the PCR method with universal primers 16Sar and 16Sbr. Intraspecific genetic variation was conducted by combining mitochondrial gene 16S sequence data of five Asian redtail fish (H. nemurus) samples from Progo River (BGM 02-06) and five Asian redtail fish samples from GenBank. BLAST, DnaSP, DNASTAR, GeneStudio,, MEGA, Mesquite, and NETWORK programs were used to analyze the data obtained. The results showed there were no genetic differences among the Asian redtail fish from Progo River indicating no intrapopulation genetic variation of the fish species. Conversely, the intraspecific genetic divergence was between 0.20% and 2.21% with an average of 0.92%. Analysis of intraspecific genetic variations detected 5 different haplotypes and 15 variable sites with 5 parsimony informative sites. Haplotype diversity and nucleotide diversity values were 0.756 ± 0.130 and 0.00812 ± 0.00294 respectively. Analysis of phylogenetic and haplotype network revealed BGM 02-06 formed a specific haplotype that was separated from other haplotypes. Molecular markers for Asian redtail fish (H.nemurus) from the Progo River, Magelang, Central Java based on the 16S mitochondrial gene can use this specific haplotype.

Resource optimization and image processing for vegetation management programs in power distribution networks

Vegetation management programs are some of the largest budget items for utility companies in their efforts to prevent power outages. Our work focuses on developing techniques to streamline vegetation management operations through detection of at-risk locations in a power distribution network. We have trained deep convolutional neural networks to segment vegetation and estimate the risk of outage using high resolution aerial imagery. The output of these networks helps in the optimization of resource allocation and vegetation management planning. Experimental results on the Bay Area Synthetic Network demonstrate high accuracy in segmentation and risk prediction, thus minimizing costs for vegetation management programs.

B cell-derived cfDNA after primary BNT162b2 mRNA vaccination anticipates memory B cells and SARS-CoV-2 neutralizing antibodies

BackgroundMuch remains unknown regarding the response of the immune system to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination. MethodsWe employed circulating cell-free DNA (cfDNA) to assess the turnover of specific immune cell types following administration of the Pfizer/BioNTech vaccine. FindingsThe levels of B cell cfDNA after the primary dose correlated with development of neutralizing antibodies and memory B cells after the booster, revealing a link between early B cell turnover—potentially reflecting affinity maturation—and later development of effective humoral response. We also observed co-elevation of B cell, T cell, and monocyte cfDNA after the booster, underscoring the involvement of innate immune cell turnover in the development of humoral and cellular adaptive immunity. Actual cell counts remained largely stable following vaccination, other than a previously demonstrated temporary reduction in neutrophil and lymphocyte counts. ConclusionsImmune cfDNA dynamics reveal the crucial role of the primary SARS-CoV-2 vaccine in shaping responses of the immune system following the booster vaccine. FundingThis work was supported by a generous gift from Shlomo Kramer. Supported by grants from Human Islet Research Network (HIRN UC4DK116274 and UC4DK104216 to R.S. and Y.D.), Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine, The Alex U Soyka Pancreatic Cancer Fund, The Israel Science Foundation, the Waldholtz/Pakula family, the Robert M. and Marilyn Sternberg Family Charitable Foundation, the Helmsley Charitable Trust, Grail, and the DON Foundation (to Y.D.). Y.D. holds the Walter and Greta Stiel Chair and Research Grant in Heart Studies. I.F.-F. received a fellowship from the Glassman Hebrew University Diabetes Center.

Special Pathogens Readiness in the United States: From Ebola to COVID-19 to Disease X and Beyond.

Special Pathogens Readiness in the United States: From Ebola to COVID-19 to Disease X and Beyond.

High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network

Harvesting solar energy through photovoltaic (PV) power systems plays an important role in achieving the goal of carbon neutrality. However, the early microdefects in PV cells considerably affect the efficiencies of PV power systems. In addition, the growing number of PV power systems require more efficient and economic detection methods to ensure the long-term efficiency of PV power systems. To address this problem, recent state-of-the-art methods have attempted to use a convolutional neural network (CNN) model. However, these methods involve large amounts of parameters and require too many calculations, considerably affecting the efficiency, economy, and flexibility in real applications. In this work, we propose a lightweight dual-flow defect detection network (DDDN) and accelerate it with a field programmable gate array (FPGA) based on the developed dual-flow parallel computing architecture (DPCA). The DDDN can accurately detect early microdefects in PV cells with low calculations and storage costs, while the DPCA optimizes the data access and computation process to improve detection efficiency and reduce power consumption. Benefiting from the designed DDDN and DPCA, the proposed system achieves a high detection accuracy (88.26%), low power consumption (22 W), and competitive detection efficiency, making it more suitable than previous methods for ensuring the long-term efficiency of PV power systems.

100 Gbps Dynamic Extensible Protocol Parser Based on an FPGA

In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.

A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms

In the present study, the application of six engine oil-based Nano fluids (NFs) in a solar concentrating photovoltaic thermal (CPVT) collector is investigated. The calculations were performed for different values of nanoparticle volume concentration, receiver tube diameter, concentrator surface area, receiver length, receiver actual to the maximum number of channels ratio, beam radiation, and a constant volumetric flow rate. Besides, two novel soft computing paradigms namely, the cascaded forward neural network (CFNN) and Multi-gene genetic programming (MGGP) were adopted to predict the first law efficiency (ηI) and second law efficiency (ηII) of the system based on the influential parameters, as the input features. It was found that the increase of nanoparticle concentration leads to an increase in ηI and a decrease in ηII. Moreover, the rise of both the concentrator surface area (from 5 m2 to 20 m2) and beam irradiance (from 150 W/m2 to 1000 W/m2) entails an increase in both the ηI (by 39% and 261%) and ηII (by 55% and 438%). Furthermore, it was reported that the pattern of changes in both ηI and ηII with serpentine tube diameter, receiver plate length, and absorber tube length is increasing-decreasing. The results of modeling demonstrated that the CFNN had superior performance than the MGGP model.

Computer Deep Learning Network Security Vulnerability Detection Based on Virtual Reality Technology

In order to detect the computer network security technology vulnerabilities due to various factors, the normal operation of the computer network must be ensured, the user’s confidential information must be protected, and it is proposed that the analysis and research on security vulnerability detection must be strengthened. This study introduces the working principle of the network security monitoring system, analyzes the key technologies involved in the system development process and network programming technology, gives the overall architecture of the system, and designs the processing flow of the monitoring function. The test and analysis of the system show that the design of the system has achieved the expected design goal. The design of the system has achieved the expected design goal. The five test points can meet the standard time specified in the demand analysis process. The time difference of all module test points in the test is less than 3 s. The system can realize the remote acquisition and real-time monitoring of the network access, file system operation, system operation status, and other information of the controlled computer. Through the test and analysis of the system, it is shown that the system has achieved the expected design goal, the working state problem, and can meet the functional requirements of internal network security monitoring. It can be applied to enterprises, institutions, and departments that have higher requirements for intranet information security.

The Persistent Problem of Software Insecurity

Software is increasingly playing a key role in all infrastructure and application domains we may think of. One notable example is represented by the increased “softwarization” of computer networks, including wireless communications networks—see, for example, the various initiatives toward open radio access networks (RANs) to disaggregate the various components of a RAN and standardize the interface of these components to run them on the cloud. Or, there is the notion of “network programs”1 as an approach to better control and manage networks and possibly even enhance their security.

Demand Response Application as a Service: An SDN-Based Management Framework

With an increase in the utilization of appliances, meeting the energy demand of consumers by traditional power grids is an important issue. The success of Demand Response (DR) depends conclusively on real-time data communication between the consumers and the suppliers. Hence, a scalable and programmable communication network is required to handle the data generated. We prove that the problem of DR global load balancing includes energy and data constraints is NP-hard. So, a dynamic and self-configurable network technology known as Software-defined Networking (SDN) can be an efficient solution. In order to handle DR communication challenges, an SDN-enabled framework for DR flow management is designed in this paper. This framework is based on two-tier cloud computing and manages energy and data traffic seamlessly. We also equip this framework with Network Functions Virtualization (NFV) technology. The proposed framework is implemented on a practical testbed, which includes Open vSwitch, Floodlight controller, and OpenStack. Its performance is appraised by comprehensive experiments and scenarios. Based on the results, it achieves low delay, a high throughput, and improves Peak to Average Ratio (PAR) by balancing the energy and data on the entire DR network.

AESS Professional Networking and Mentoring Program

AESS Professional Networking and Mentoring Program

SoftDrone: Softwarized 5G assisted drone networks for dynamic resource sharing using machine learning techniques

The concept of a programmable network instantiates dedicated network slices for Unmanned Aerial Vehicle (UAV)-based on-demand communication layers which improve the efficiency of overall network performance. With the increasing random service demands, network resource allocation, retention, and release have become serious networking challenges. Often existing methods consider dedicated resource allocations which result in poor resource utilization as well as service quality. Though Machine Learning (ML) techniques are being used for better performance, limited energy constraints and complexity in resource cycle management become a critical matter of fact again. To resolve these issues, we propose service-specific learning models on VNF (Virtual Network Function) data that are running on shared network slices. The results show an average reduction of 35% error from state-of-the-art techniques. This improved performance can further reduce chances of over or under allocation of resources which could lead to severe service denials to time-critical applications in the areas of disaster management, e-healthcare applications, etc.

A blockchain-based secure data-sharing framework for Software Defined Wireless Body Area Networks

In the past few years, blockchain has proven its necessity and unprecedented prospects in providing a secure environment for information exchange between two parties. The integration of the Internet of Things (IoT) with blockchain has enabled a digital transformation in many areas such as healthcare, supply chain, and financial services. Like blockchain, the programmable Software-Defined Network (SDN) is also achieving popularity due to its ability to reduce network management complexity. It is evident that incorporating SDNs with IoT-based healthcare systems can significantly improve healthcare management services. However, there are a number of challenges such as data confidentiality, user-centric design, integrity and privacy that makes it difficult to share sensitive information among different parties in a healthcare system. To overcome these challenges, we propose an architectural framework that incorporates blockchain with Software-Defined Wireless Body Area Networks (SDWBANs) to facilitate secure data sharing. We have also designed and integrated a smart contract based fine-grained access control policy to ensure that only data owners will have full control over their health data. The experimental outcomes show that the proposed model achieves good throughput and incurs a very low overhead in terms of latency compared to traditional cloud-based systems.