Network Survivability Research Articles

Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation

The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation.

WSLC: Weighted semi-local centrality to identify influential nodes in complex networks

Identifying and ranking influential nodes in complex networks is a critical aspect to study the survival and robustness of networks. Many ongoing researches have proposed centrality metrics to address this problem, so that the performance of each is attributed to specific scenarios. For example, metrics based on local structure have low ranking accuracy due to the use of limited information, and metrics based on global structure suffer from high complexity. Meanwhile, metrics based on semi-local structure are amazingly well, but an efficient centrality for identifying influential nodes is still not available due to differences in the structure and scale of networks. In addition, most semi-local centrality metrics only consider one aspect of each node's information, and their development still faces serious challenges. This paper develops a Weighted Semi-Local Centrality (WSLC) to identify influential nodes in complex networks based on extended neighborhood concept. Here, several different weights are investigated to find the best performance on WSLC. We use the extended neighborhood concept to select the nearest neighbors, which considers the global information of the network in a limited and efficient way to calculate the ranks. Here, a distributed approach is presented that can cut a subgraph of the entire network for each node with low complexity. This subgraph contains neighbors with different hops, which are used to maintain high efficiency when facing large-scale networks. In addition to the importance of the node itself, WSLC also combines the importance of the node's nearest neighbors with different hops for ranking. Therefore, defining semi-local structure with a distributed approach as well as using an efficient edge weighting policy differentiates WSLC from other existing centrality metrics. The evaluation of WSLC has been done through several real-world networks using Kendall's correlation. The effectiveness of WSLC under the SIR infection spreading model has been verified by extensive simulations compared to state-of-the-art centrality metrics.

Identification of a ceRNA Network Driven by Copy Number Variations in Esophageal Cancer.

Copy number variation (CNV) is associated with progression of esophageal cancer (EC), a common gastrointestinal neoplasm. Using sequencing data, CNV data, and clinical data of EC transcriptome samples obtained from public databases, we performed differential expression analysis on sequencing data. Differentially expressed CNV-driven lncRNAs were screened using the chi-square test, and CNV-driven lncRNA-associated miRNAs and mRNAs were predicted. Cytoscape software was then used to construct ceRNA networks. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate biological functions of mRNAs in the ceRNA network. Survival curves were plotted to explore correlations between lncRNAs in the ceRNA network and overall survival of CNV patients. Multiple databases were used to predict lncRNAs-related drugs. A dysregulated lncRNA-associated ceRNA network driven by CNV in EC, including 11 lncRNAs, 11 miRNAs and 159 mRNAs, was constructed. Downstream enrichment of mRNAs was related to biological processes such as extracellular matrix organization, indicating that these mRNAs mainly participate in intercellular exchange between tumor cells. Additionally, expression of all lncRNAs in the ceRNA network, except LINC00950, LINC01270 and MIR181A1HG, was correlated with patients' CNV. In addition, none of the 11 lncRNAs was significantly correlated with overall survival of CNV patients. CH5424802 and PD-033299CNV mainly affected the RTK signaling pathway and the cell cycle of tumor cells via RP11-180N14.1 and RP11-273 G15.2 in the ceRNA network. This study identified 11 CNV-driven lncRNAs that might affect EC development, 2 of which have promising effects if applied to drug treatment. These findings might assist in identifying novel treatments for EC.

A neutral theory of plant carbon allocation.

How plants use the carbon they gain from photosynthesis remains a key area of study among plant ecologists. Although numerous theories have been presented throughout the years, the field lacks a clear null model. To fill this gap, I have developed the first null model, or neutral theory, of plant carbon allocation using probability theory, plant biochemistry and graph theory at the level of a leaf. Neutral theories have been used to establish a null hypothesis in molecular evolution and community assembly to describe how much of an ecological phenomenon can be described by chance alone. Here, the aim of a neutral theory of plant carbon allocation is to ask: how is carbon partitioned between sinks if one assumes plants do not prioritize certain sinks over others? Using the biochemical network of plant carbon metabolism, I show that, if allocation was strictly random, carbon is more likely to be allocated to storage, defense, respiration and finally growth. This 'neutral hierarchy' suggests that a sink's biochemical distance from photosynthesis plays an important role in carbon allocation patterns, highlighting the potentially adaptive role of this biochemical network for plant survival in variable environments. A brief simulation underscores that our ability to measure the carbon allocation from photosynthesis to a given sink is unreliable due to simple probabilistic rules. While neutral theory may not explain all patterns of carbon allocation, its utility is in the minimal assumptions and role as a null model against which future data should be tested.

Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia.

: Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network. : Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model. : The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864-0.892] for diabetes mellitus, 0.835 (95% CI = 0.826-0.845) for hypertension, and 0.826 (95% CI = 0.817-0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P ≤ 0.001; hypertension: NRI = 0.194, P ≤ 0.001; dyslipidemia: NRI = 0.397, P ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P ≤ 0.001; hypertension: IDI = 0.007, P ≤ 0.001; and dyslipidemia: IDI = 0.043, P ≤ 0.001]. : This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.

Abstract A129: Association of concordance with guidelines of the National Comprehensive Cancer Network and overall survival in Puerto Rican women with endometrial cancer

Abstract Background: Endometrial cancer represents a significant burden in Puerto Rico, as it is one of the most common cancers among women. Although it has a favorable prognosis when diagnosed early, it remains the fifth leading cause of cancer-related deaths among women living in Puerto Rico. This highlights the need to address healthcare disparities and improve the quality of the healthcare system. Treatment guidelines are designed to assist healthcare professionals in offering the best possible care to patients by providing evidence-based suggestions. Implementing standardized practices is crucial to enhancing healthcare quality outcomes. Our objective was to assess the concordance of National Comprehensive Cancer Network (NCCN) guidelines for endometrial cancer treatment and its association with overall survival. Methods: Data on endometrial cancer patients aged 18 years and older diagnosed between 2009 and 2015 was obtained from the Puerto Rico Central Cancer Registry, linked to the Puerto Rico Health Insurance Administration database (n=2,114). The association between clinical aspects, health system, and socioeconomic factors with receipt of guideline-concordant treatment was evaluated using logistic regression. Overall survival (OS) was calculated using the Kaplan-Meier method. Univariate and multivariate-adjusted Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between guideline-concordant treatment and OS. Results: In our cohort, 53.9% of patients received guideline-concordant treatment. Patients receiving treatment at a Commission on Cancer (CoC)-accredited facility are twice as likely to receive guideline-concordant treatment than non-accredited facilities (95% CI: 1.71-2.98). Individuals who received an assessment from a gynecologist oncologist are 90.0% more likely to receive treatment based on NCCN guidelines than those who did not (95% CI: 1.56-2.32). Private insured patients are 28.0% more likely to receive guideline-concordant treatment than Medicaid patients (95% CI: 1.02-1.61). In Cox regression models, guideline-concordant treatment was associated with a lower mortality risk compared to women who did not receive guideline-concordant treatment (95% CI: 0.59- 0.89). Conclusion: Receiving guideline-concordant treatment is a strong predictor of improved survival rates in endometrial cancer patients. Additionally, patients treated at CoC-accredited hospitals and by gynecologic oncologists tend to receive higher-quality care. Given that guidelines based on effective treatment standards have been demonstrated to enhance cancer patient outcomes, we must encourage and support factors contributing to their increased adoption. By doing so, we can strive to improve the prognosis and overall well-being of individuals affected by endometrial cancer in Puerto Rico. Citation Format: Yisel Pagan-Santana, Karen J. Ortiz-Ortiz, Maira Castañeda-Avila, Ruth Rios-Motta. Association of concordance with guidelines of the National Comprehensive Cancer Network and overall survival in Puerto Rican women with endometrial cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A129.

Fosgonimeton, a Small‐Molecule Positive Modulator of the HGF/MET System, Attenuates Amyloid‐β – Mediated Toxicity in Primary Neuron Culture

AbstractBackgroundPositive modulation of the hepatocyte growth factor (HGF)/MET system may represent a promising therapeutic strategy for Alzheimer’s disease (AD). Downstream effectors of HGF/MET, such as AKT or ERK, have been shown to activate pro‐survival signaling cascades that counteract pathological components of AD including excitotoxicity, mitochondrial dysfunction, oxidative stress, and protein aggregation. We have previously that fosgonimeton, a small molecule positive modulator of the HGF/MET system, elicits broad neuroprotective effects (Johnston et. al, 2022). Herein, we investigate the mechanism of action by which fosgonimeton induces neuroprotective effects in beta‐amyloid and glutamate‐challenged primary cortical neurons.MethodPrimary rat cortical neurons were treated with the active metabolite of fosgonimeton (fosgo‐AM), challenged with beta‐amyloid (Aβ1‐42; 15 µM) for 24 hours, and co‐immunostained for microtubule‐associated protein‐2 (MAP‐2) and phospho‐tau (AT100). Immunofluorescence analyses were used to determine neuronal survival, neurite network integrity (total neurite length), and phospho‐tau levels. In a separate model, rat cortical neurons were treated with fosgo‐AM and challenged with glutamate (20 µM) for 24 hours, and immunostained for MAP‐2 to determine neuronal survival and neurite network. To elucidate the mechanism of neuroprotection, protein analyses via western blot and homogenous time‐resolved fluorescence were conducted to assay HGF/MET downstream signaling pathways. In addition, the effect of fosgo‐AM on mitochondrial membrane potential (MMP) was assessed in primary cortical neuron culture via fluorescent MMP dye.ResultFosgo‐AM treatment significantly improved survival of cortical neurons, protected neurite networks, and reduced tau hyperphosphorylation after injury with Aβ1‐42. Additionally, fosgo‐AM reduced glutamate‐mediated cortical neuron death and neurite degeneration. Analyses of downstream signaling effects indicate that the neuroprotective effects of fosgo‐AM treatment may, in part, be the result of activation of AKT signaling and subsequent rescue of mitochondrial membrane potential, thereby reducing protein pathology and oxidative stress.ConclusionOur in vitro data demonstrate the address pathological of AD reduce tau . neuroprotective effects of fosgonimeton, in addition to its neurotrophic and pro‐cognitive effects in preclinical models, highlight its therapeutic potential to interrupt disease progression and restore neuronal health. Fosgonimeton is currently in clinical for mild‐to‐moderate AD (NCT04488419; NCT04886063).

Improved design of load balancing for multipath routing protocol

In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time.

Network reliability analysis through survival signature and machine learning techniques

As complex networks become ubiquitous in modern society, ensuring their reliability is crucial due to the potential consequences of network failures. However, the analysis and assessment of network reliability become computationally challenging as networks grow in size and complexity. This research proposes a novel graph-based neural network framework for accurately and efficiently estimating the survival signature and network reliability. The method incorporates a novel strategy to aggregate feature information from neighboring nodes, effectively capturing the response flow characteristics of networks. Additionally, the framework utilizes the higher-order graph neural networks to further aggregate feature information from neighboring nodes and the node itself, enhancing the understanding of network topology structure. An adaptive framework along with several efficient algorithms is further proposed to improve prediction accuracy. Compared to traditional machine learning-based approaches, the proposed graph-based neural network framework integrates response flow characteristics and network topology structure information, resulting in highly accurate network reliability estimates. Moreover, once the graph-based neural network is properly constructed based on the original network, it can be directly used to estimate network reliability of different network variants, i.e., sub-networks, which is not feasible with traditional non-machine learning methods. Several applications demonstrate the effectiveness of the proposed method in addressing network reliability analysis problems.

The effect of donor graft type on survival after liver transplantation for hepatoblastoma in children.

Liver transplantation (LT) is the only potentially curative option for children with unresectable hepatoblastoma (HBL). Although post-transplant outcomes have improved in the contemporary era, the impact of donor graft type on survival remains unclear. Using the United Network for Organ Sharing database (02/2002-06/2021), demographics, clinical characteristics, and patient and graft survival were analyzed in children (<18 years) who underwent LT for HBL according to donor graft type. The Kaplan-Meier method, log-rank tests, and Cox regression modeling were used to evaluate the effect of whole, partial, and split deceased donor liver transplantation (DDLT) and living donor liver transplantation (LDLT) on patient and graft survival. A total of 590 pediatric HBL LT recipients (344 whole graft DDLT; 62 partial graft DDLT; 139 split graft DDLT; 45 LDLT) were included. During 2012-2021 the proportion of LDLTs for HBL decreased to about 5% compared with about 11% during 2002-2011. No significant differences were identified by donor graft type in either patient survival (log-rank test, p = .45) or graft survival (log-rank test, p = .69). The results remained similar during the 2002-2011 era, while during the 2012-2021 era, split graft DDLT was associated with decreased graft loss risk versus whole graft DDLT (hazard ratio: 0.48, 95% confidence interval: 0.23-0.99, p = .046) without any other significant between-group differences. Utilizing non-whole liver grafts can increase access to LT in children with unresectable HBL while ensuring favorable outcomes. LDLT is underutilized in children with HBL in the United States, and efforts to explore LDLT options should be undertaken.

Gradual Loss of Social Group Support during Competition Activates Anterior TPJ and Insula but Deactivates Default Mode Network.

Group forming behaviors are common in many species to overcome environmental challenges. In humans, bonding, trust, group norms, and a shared past increase consolidation of social groups. Being a part of a social group increases resilience to mental stress; conversely, its loss increases vulnerability to depression. However, our knowledge on how social group support affects brain functions is limited. This study observed that default mode network (DMN) activity reduced with the loss of social group support from real-life friends in a challenging social competition. The loss of support induced anterior temporoparietal activity followed by anterior insula and the dorsal attentional network activity. Being a part of a social group and having support provides an environment for high cognitive functioning of the DMN, while the loss of group support acts as a threat signal and activates the anterior temporoparietal junction (TPJ) and insula regions of salience and attentional networks for individual survival.

Survivability with Adaptive Routing and Reactive Defragmentation in IP-over-EON after A Router Outage

The occurrence of a router outage in the IP layer can lead to network survivability issues in IP-over-elastic-optical networks with consequent effects on the existing connections used in transiting the router. This usually leads to the application of a path to recover any affected traffic by utilizing the spare capacity of the unaffected lightpath on each link. However, the spare capacity in some links is sometimes insufficient and thus needs to be spectrally expanded. A new lightpath is also sometimes required when it is impossible to implement the first process. It is important to note that both processes normally lead to a large number of lightpath reconfigurations when applied to different unaffected lightpaths. Therefore, this study proposes an adaptive routing strategy to generate the best path with the ability to optimize the use of unaffected lightpaths to perform reconfiguration and minimize the addition of free spectrum during the expansion process. The reactive defragmentation strategy is also applied when it is impossible to apply spectrum expansion because of the obstruction of the neighboring spectrum. This proposed strategy is called lightpath reconfiguration and spectrum expansion with reactive defragmentation (LRSE+RD), and its performance was compared to the first Shortest Path (1SP) as the benchmark without a reactive defragmentation strategy. The simulation conducted for the two topologies with two traffic conditions showed that LRSE+RD succeeded in reducing the lightpath reconfigurations, new lightpath number, and additional power consumption, including the additional operational expense compared to 1SP.

A Web-based Prediction Model for Early Death in Patients With Metastatic Triple-negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by the absence of expression of estrogen receptor, progesterone receptor, or human epidermal growth factor receptor 2. This subtype of breast cancer is known for its high aggressiveness, high metastatic potential, tendency for recurrence, and poor prognosis. Patients with metastatic TNBC (mTNBC) have a poorer prognosis and a higher likelihood of early death (survival time ≤3 months). Therefore, the development of effective individualized survival prediction tools, such as prediction nomograms and web-based survival calculators, is of great importance for predicting the probability of early death in patients with metastatic TNBC. Patients diagnosed with mTNBC in the Surveillance, Epidemiology, and End Results database between 2010 and 2015 were included in the model construction. Univariate and multivariate logistic regression analysis was performed to identify risk factors associated with early death in patients with mTNBC and predictive prognostic nomograms were constructed. The accuracy of the nomograms was verified using receiver operating characteristic curves, and GiViTi Calibration belt plots were used to evaluate the model consistency. The clinical applicability of the nomograms was evaluated using decision curve analysis. On the basis of the predictive prognostic nomograms, a network survival rate calculator was developed for individualized survival prediction in patients with mTNBC. A total of 2230 patients diagnosed with mTNBC were included in the Surveillance, Epidemiology, and End Results database for this study. After strict exclusion criteria, 1428 patients were found to be eligible for the study. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. Independent risk factors for mTNBC, including age, tumor size, brain metastasis, liver metastasis, surgery, and chemotherapy, were identified and integrated to construct the prediction nomogram and survival calculator. Results of receiver operating characteristic curves, calibration curves, and decision curve analysis curves from the training and validation cohort confirmed that the developed nomogram and web-based survival calculator in this study could accurately predict the probability of early death in patients with mTNBC. In this study, we developed a reliable prediction nomogram and web-based survival calculator for predicting the probability of early death in patients with mTNBC. These tools can assist clinical physicians in identifying high-risk patients and developing personalized treatment plans as early as possible.

“Reel”ing it in: The Indian film industry's survival and growth in the post-pandemic era

Business-to-business (B2B) firms are expected to work collectively to overcome the sector's challenges. The COVID-19 pandemic was a ‘rare’ event that triggered failures across many business sectors. With limited financial resources, governments in emerging economies could not assist businesses and the B2B firms relied on their networks for survival. In this study, we explored the B2B firms' response to the pandemic and whether their long-established networks and relationships with each other aided or hindered their ability to operate through the disruption and rebound in the post-pandemic recovery period. We studied the operations of the Indian film industry through the pandemic and collected interview data from key informants from the sector. Using stakeholder and institutional theory lenses, we highlighted the changes in the sector, including the weakening of B2B firm networks and firms moving away from organizational collaboration to prioritizing individual interests. The nature of the business model has changed, with increased use of digital platforms for movie releases. These findings have implications for theory and practice as international government agencies were providing incentives for Indian filmmakers to produce movies in their country, which needed to be countered by the Indian government.

Wheel‐based 5G fronthaul/backhaul hybrid MDM‐NGPON/FSO system under Gamma‐Gamma turbulence and weather effects

SummaryThis work presents the bidirectional and symmetric 40 Gbps wheel architecture comprising 16 channels‐based bidirectional hybrid next generation passive optical network with free space optics (NGPON/FSO) system using mode division multiplexing (MDM) scheme. The system incorporates a low‐cost high bandwidth and integrated time and wavelength division multiplexing with orthogonal frequency division multiplexing (TWDM‐OFDM) for data transmission over mixed fiber‐FSO links to various end users at donut modes 0 and 1. This system enhances the fifth generation (5G)‐based networks reliability, survivability, and flexibility, especially in rural places where the core fiber link may be damaged/destroyed under diverse turbulent and weather conditions. The obtained results depict the faithful multimode fiber range of 100 km with fixed FSO range of 100 m at −17 dBm receiver sensitivity and low power penalty of 0.2 dB at 10−9 bit error rate. Maximum FSO range of 2800 m under weak‐to‐strong Gamma‐Gamma turbulence and the effect of fog, rain, snow, and snow with rain can be obtained successfully with fixed 50 km fiber length. Also, the proposed wheel architecture provides superior performance over hybrid ring‐mesh architecture with high split ratio of 1:64. Moreover, the system can sustain up to 70 remote nodes at symmetric 10 Gbps throughput providing high gain, high optical signal‐to‐noise ratio, and low noise figure. The comparative literature work also reveals that the proposed architecture offers high bandwidth, security, long‐reach, and cost‐effectiveness in 5G fronthaul/backhaul networks.

Identification of novel potential genes in testicular germ cell tumors: A transcriptome analysis.

Testicular germ cell tumors (TGCTs), containing pure seminoma and non-seminoma, occupy the most majority of testicular cancers in adolescents and young men, which has increased dramatically in recent decades. Therefore, it is important to find crucial genes for improving diagnosis and prognosis in TGCTs. However, the diagnostic and prognostic markers of TGCTs are limited. In this study, our main objective is to explore novel potential genes that can be used as diagnostic and prognostic biomarkers in TGCTs. Our study detected 732 differentially expressed genes (DEGs) using three microarray expression profiling datasets from Gene Expression Omnibus (GEO). Multiple analysis was performed to identify the roles of DEGs, including pathway and functional enrichment analysis, protein-protein interaction (PPI) network analysis, module analysis, and survival analysis. In total, 322 upregulated genes and 406 downregulated genes were identified as DEGs The functional and pathway enrichment analysis shows that DEGs were highly enriched in multiple biological attributes such as T cell activation, reproduction in multicellular organism, sperm flagellum, antigen processing and presentation Then, seven potential crucial genes were identified via PPI network analysis, module analysis, and survival analysis. Furthermore, 7 potential crucial genes had shown to play a key role in regulating immune cell infiltration level in patients with TGCTs. We identified seven potential crucial genes (LAPTM5, NCF2, PECAM1, CD14, COL4A2, ANPEP and RGS1), which may be molecular markers in improving the way of diagnosis and prognosis in TGCTs.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

ENRN: A System for Evaluating Network Resilience against Natural Disasters

The frequency and severity of natural disasters is surging, posing an urgent need for robust communication network infrastructure that is capable of withstanding these events. In this paper, we present a groundbreaking graph-theoretic system designed to evaluate and enhance network resilience in the face of natural disasters. Our solution harnesses the power of topological robustness metrics, integrating real-time weather data, geographic information, detailed network topology data, advanced resilience algorithms, and continuous network monitoring. The proposed scheme considers four major real-world U.S.-based network providers and evaluates their physical topologies against two major hurricanes. Our novel framework quantifies the important characteristics of network infrastructure; for instance, AT&amp;T is identified to have fared better against Hurricane Ivan (57.98 points) than Hurricane Katrina (39.17 points). We not only provide current insights into network infrastructure resilience, but also uncover valuable findings that shed light on the performance of backbone U.S. networks during hurricanes. Furthermore, our findings provide actionable insights to enrich the overall survivability and functionality of communication networks, mitigating the adverse impacts of natural disasters on communication systems and critical services in terms of improving network resiliency via adding additional nodes and link or rewiring.

Marginal gentrifiers, networks of mobilization and new contentious collective identities. The struggle for housing in post-austerity Lisbon

If literature has stressed the role of marginal gentrifiers in bringing resources to the areas where they move, apart for relevant exceptions, the potentialities and limits of their contribution to urban struggles has not been systematically addressed. This article assesses the role of these newcomers in the defence of the right to housing in post-austerity Lisbon focusing on their interaction with established activist networks. Resorting to a multi-method approach and an interactionist social movement framework, our study is supported by event analysis, 22 interviews, ethnographic observation and a questionnaire-survey. Our findings show that interactions between marginal gentrifiers and previous housing players helped to consolidate the local activist arena and contributed to the emergence of new urban collective contentious identities. Nevertheless, this contribution seems partially affected by further displacement waves in the context of growing gentrification and touristification, which have threatened the survival of resistance networks. The case of Lisbon can help illuminate similar processes in contexts highly impacted by gentrification and touristification.

Blocking aware offline survivable path provisioning of connection requests in elastic optical networks

Survivability of Elastic Optical Networks (EONs) from a link failure is an important problem. Shared Backup Path Protection (SBPP) is widely used for protection against a link failure. Backup Spectrum Reservation with Multi Path Protection (BSR-MPP) was introduced to increase backup spectrum sharing by reserving backup spectrum and employing protection with multiple backup paths. We propose a new polynomial-time strategy, Blocking Aware Survivable Path Provisioning (BASPP) that shares backup resources like SBPP and BSR-MPP but can reduce request blocking when opportunity of sharing resources does not exist. Since we address the offline problem of Survivable Routing, Modulation and Spectrum Allocation in EONs, we also propose an Integer Linear Program formulation. Results show appreciable reduction in bandwidth blocking ratio by using BASPP over existing strategies.