Transactions on Networks and Communications <p>Transactions on Networks and Communications is an international peer-reviewed, open access, bi-monthly, on-line journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all aspects of networking and data communications ranging from architectures, services, virtualization, privacy, security and management.</p> Services for Science and Education, United Kingdom en-US Transactions on Networks and Communications 2054-7420 IoT Serverless Computing at the Edge: Open Issues and Research Direction <p>Novel computing paradigms aim to enable better hardware utilization, allowing a greater number of applications to be executed on the same physical resources. Serverless computing is one example of such an emerging paradigm, enabling faster development, more efficient resource usage, as well as no requirements for infrastructure management by end users. Recently, efforts have been made to utilize serverless computing at the network edge, primarily focusing on supporting Internet of Things (IoT) workloads. This study explores open issues, outlines current progress, and summarizes existing research findings about serverless edge computing for IoT by analyzing 67 relevant papers published between 01.01.2015 and 01.09.2021. We discuss the state-of-the-art research in 8 subject areas relevant to the use of serverless at the network edge, derived through the analysis of the selected articles. Results show that even though there is a noticeable interest for this topic, further work is needed to adapt serverless to the resource constrained environment of the edge.</p> Vojdan Kjorveziroski Cristina Bernad Canto Pedro Juan Roig Katja Gilly Anastas Mishev Vladimir Trajkovik Sonja Filiposka Copyright (c) 2021 Vojdan Kjorveziroski, Cristina Bernad Canto, Pedro Juan Roig, Katja Gilly, Anastas Mishev, Vladimir Trajkovik, Sonja Filiposka 2021-12-12 2021-12-12 9 4 1 33 10.14738/tnc.94.11231 Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network <p>Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for &nbsp;&nbsp;training &nbsp;&nbsp;deep &nbsp;&nbsp;neural networks &nbsp;&nbsp;for classification &nbsp;&nbsp;of &nbsp;&nbsp;breast &nbsp;&nbsp;cancer in histopathological images was developed in this study. However, this images are affected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A threshold &nbsp;&nbsp;that &nbsp;&nbsp;decays over iteration number is used &nbsp;&nbsp;to &nbsp;&nbsp;decide which high confidence samples should be concatenated with manually labeled samples and then used infine-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .</p> <p>&nbsp;</p> oluwashola David Adeniji Copyright (c) 2021 oluwashola David Adeniji 2021-08-22 2021-08-22 9 4 29 38 10.14738/tnc.94.10663 Assessment of RCS-specific SNR and Loglikelihood Function in Detecting Low-observable Targets and Drones Illuminated by a Low Probability of Intercept Radar Operating in Littoral Regions <p>The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones).&nbsp; Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.</p> Perambur Neelakanta Dolores De Groff Copyright (c) 2021 Perambur Neelakanta, Dolores De Groff 2021-07-30 2021-07-30 9 4 1 22 10.14738/tnc.94.10512 Surge of the Innovative Quest in the first lockdown period due to the Pandemic Effect <p>When the entire world is reeling under the COVID 19 pandemic effect and the tensed human race is struggling to return back to the normalcy of life, the one thing which has become very active is the grey cells of our brain. The pandemic effect has cut down our physical limits due to the movement constraints. But it is thankfully unable to restrict the ticking of the grey cells of the human brain. As is said, “Necessity is the mother of the invention”. Sure enough!! We can be extremely pleased to know that the innovative surge in science and technology continues unabated in this lockdown period. The prime requirement of the pandemic effect is social distancing, less physical contact and keeping ourselves away from infection by corona virus. Keeping this necessity in mind, the doctors, the engineers, the researchers as well as the students’ community are keeping themselves busy in pumping out the solutions to the currently faced problems. The outputs include automatic masks machines, low cost PPE’s, automatic wash basins, suitable ventilators, sanitizer tunnels etc. This review paper looks into the innovative surge already made and what more can be churned out for the effective social safety in this tensed pandemic effect. The most awaited news as of now is the successful implementation of an effective vaccine and cost effective drugs which can help the human beings breathe easy. The pandemic effect has also showed us the way for a cleaner and greener nature. It is now a challenge to the intellectual world to come up with inexpensive, innovative and smart solutions which will make our beautiful planet safer, greener, cleaner and worthier to live in.</p> Ishita Ghosh Copyright (c) 2021 Ishita Ghosh 2021-12-29 2021-12-29 9 4 34 43 10.14738/tnc.94.11428 Fault-Tolerant Placement of Additional Mesh Nodes in Rural Wireless Mesh Networks: A Minimum Steiner Tree Based Centre of Mass With Bounded Edge Length <p>Wireless mesh networks are presented as an attractive solution to reduce the digital divide between rural and developed areas. In a multi-hop fashion, they can cover larger spaces. However, their planning is subject to many constraints including robustness. In fact, the failure of a node may result in the partitioning of the network. The robustness of the network is therefore achieved by carefully placing additional nodes. This work tackles the problem of additional nodes minimization when planning bi and tri-connectivity from a given network. We propose a vertex augmentation approach inspired by the placement of Steiner points. The idea is to incrementally determine cut vertices and bridges in the network and to carefully place additional nodes to ensure connectivity, bi and tri-connectivity. The approach relies on an algorithm using the centre of mass of the blocks derived after the partitioning of the network. The proposed approach has been compared to a modified version of a former approach based on the Minimum Steiner Tree. The different experiments carried out show the competitiveness of the proposed approach to connect, bi-connect, and tri-connect the wireless mesh networks.</p> Jean Louis Kedieng Ebongue Fendji Patience Leopold Bagona Copyright (c) 2021 Jean Louis Kedieng Ebongue Fendji, Patience Leopold Bagona 2021-08-29 2021-08-29 9 4 39 50 10.14738/tnc.94.10754 Sensors and wrong values <p>In the world of IoT and BigData, sensor based data collection is a really important domain. Using these tools it is possible to stow large amounts of data collection sensors in a factory or in nature in harsh environments. However, in order to obtain valuable information from these tools, it is important that potentially wrong data is discovered and handled. Automated exploration of wrong data is not a trivial task, even if similar measurements are performed in parallel with spatial differences. We present the difficulties of revealing defected data and suggest easy-to-implement procedures for detecting and handing them. We also draw attention to the potential disadvantages of these methods based on the given results.</p> Zoltán Pödör Copyright (c) 2021 Zoltán Pödör 2021-07-30 2021-07-30 9 4 23 28 10.14738/tnc.94.10539