FCERP: A Novel WSNs Fuzzy Clustering and Energy Efficient Routing Protocol

Wireless sensor networks (WSNs) are set of sensor nodes to monitor and detect transmitted data to the sink. WSNs face significant challenges in terms of node energy availability, which may impact network sustainability. As a result, developing protocols and algorithms that make the best use of limited resources, particularly energy resources, is critical issues for designing WSNs. Routing algorithms, for example, are unique algorithms as they have a direct and effective relationship with lifetime of network and energy. The available routing protocols employ single-hop data transmission to the sink and clustering per round. In this paper, a Fuzzy Clustering and Energy Efficient Routing Protocol (FCERP) that lower the WSNs energy consuming and increase the lifetime of network is proposed. FCERP introduces a new cluster-based fuzzy routing protocol capable of utilizing clustering and multiple hop routing features concurrently using a threshold limit. A novel aspect of this research is that it avoids clustering per round while considering using fixed threshold and adapts multi-hop routing by predicting the best intermediary node for clustering and the sink. Some Fuzzy factors such as residual energy, neighbors amount, and distance to sink considered when deciding which intermediary node to use.

Geotechnical Management of Isolated Sustainable Alpine Communities

The Albanian Alps are situated in a mountainous block in the Northern Albania region, in the counties of Shkodër (also known as Shkodra or Gegëria) and Kukës (Kukësi). The nature of the mountainous terrain formation has led to the creation of isolated communities. The need for integrating these scattered communities into a cohesive co-operating community for area sustainability is now possible by using the Internet to link them all onto an online system. To deal with natural catastrophes, disaster management cells will be created which will serve as hubs. These hubs will be located at geographically strategic positions that will enable a predetermined geofenced region for evaluation of different disasters viz. forest fires, landslide, flooding, avalanches, the burial of villages under heavy snowfalls, etc. These cells will connect the particular case with the most appropriate disaster relief, rescue service and EMR (Emergency Medical Responder), first aid services (e.g. Green Crescent/Red Cross) and EMT (Emergency Medical Technician) personnel. The cells shall be managed by locally trained human resources with the necessary equipment to provide the monitoring/analyses and first aid assistance in case of need. The technology needed for the monitoring and geotechnical management of the isolated Alpine communities will be described. The socio-economic impact of the deployment of these technologies aiding in the sustainability of these vulnerable communities will conclude the research.

A Systematic Literature Review of Blockchain Technology Adoption in Bangladesh

The spirit of “blockchain technology” is a distributed database in which saved data is transparent, accountable, public, immutable, and traceable. This base-level disruptive technology can boost the security and privacy-related efficiency of various domains. As Bangladesh is currently aiming for sustainable development, blockchain technology adoption by the local researchers is growing robustly. However, in Bangladesh, the blockchain Technology Acceptance Model (TAM) is not yet well structured which is also limiting the perspective of local developers and researchers. Therefore, sectors like governance, healthcare, security, privacy, farming, information authentication, cryptocurrencies, internet architecture, data, and so on are unable to utilize the full potential of this technology. In this research, the authors conduct an in-depth review of such types of blockchain technology-related research articles that have been published recently and are also solely focused on Bangladesh. From 5 publishers (IEEE Xplore, ACM, ScienceDirect, Taylor & Francis, and SpringerLink) this study analyses 70 articles published during the year 2016-2020. The study results find the top 13 sectors where Bangladeshi researchers are currently focusing on. Those studies identify that the rigid policy by the government, scarcity of expert researchers, and lack of resources are the main reasons why Bangladesh is still struggling to accommodate blockchain extensively. In addition, published papers are mostly based on theoretical concepts without an appropriate implementation. Finally, this study will be a great resource to the developers, entrepreneurs, and technology enthusiasts to determine the strategic plan for adopting blockchain technology in Bangladesh or even to any other developing country.

Application of Artificial Intelligence (AI) in Dredging Efficiency in Bangladesh

The integration of Artificial Intelligence (AI) into the dredging systems and dredging machinery used in "capital" and "maintenance" dredging in Bangladesh can enhance the efficiency of the machines and dredging process, enabling the operators to perform regular and repetitive dredging tasks safely in the rivers, ports, and estuaries all over the country. AI, including Big Data, Machine Learning, Internet of Thing, Blockchain and Sensors and Simulators with their catalytic potentials, can systematically compile and evaluate specific data collected from different sources, develop applications or simulators, connect the stakeholders on a virtual platform, store lakes of information without compromising their intellectual rights, predicting models to harness the challenges, minimise the cost of dredging, identify possible threats and help protect the already dredged areas by giving timely signals for further maintenance. Furthermore, the application of AI modulated dredging devices and machinery can play a significant role when monitoring aspects becomes crucial, keeping environmental impacts mitigated without affecting the quality of the human environment. This study includes the evaluation of the application of AI – its prospect and challenges in the existing dredging systems in Bangladesh against the backdrop of the challenges faced in capital and maintenance dredging in the major rivers – and assess whether such inclusion of AI is likely to minimise the cost of dredging in the rivers of Bangladesh and facilitate the materialisation of the objectives of Bangladesh Delta Plan 2100.This paper studies the organisation's infrastructural requirement for the integration of AI into dredging systems, using benchmarking such as 1- "Understanding AI Ready Approach", 2-"Strategies for Implementing AI", 3-"Data Management", 4-"Creating AI Literate Workforce and Upskilling", and 5-"Identifying Threats" concerning the management and dredging operations of Bangladesh Inland Water Transport Authority (BIWTA), under Bangladesh Ministry of Shipping and Bangladesh Water Development Board (BWDB). The paper also uses several case studies such as channel dredging to show that the use of AI can bring a significant change in the dredging operations both in reducing the cost of dredging and in terms of harnessing the barriers in adaptive management and environmental impacts.

A Comparative and Analytical Study for Choosing the Best Suited SDN Network Operating System for Cloud Data Center

The growing deployment of Software Defined Network (SDN) paradigm in the academic and commercial sectors resulted in many different Network Operating Systems (NOS). As a result, adopting the right NOS requires an analytical study of the available alternatives according to the target use case. This study aims to determine the best NOS according to the requirements of Cloud Data Center (CDC). This paper evaluates the specifications of the most common open-source NOSs. The studied features have been classified into two groups, i.e., non-functional features such as availability, scalability, ease of use, maturity, security and interoperability, and functional features, such as virtualization, fault verification and troubleshooting, packet forwarding techniques and traffic protection solutions. A Decision support system, Analytical Hierarchy Process (AHP) has been applied for assessing specifications of the inspected NOSs, namely, ONOS, Opendaylight (ODL), Floodlight, Ryu, POX and Tungsten. Our investigation revealed that ODL is the most suitable NOS for CDC compared to the rest studied NOSs. However, ODL and ONOS have almost similar scores compared to the rest NOSs.

Towards 76-81 GHz Scalable Phase Shifting by Folded Dual-strip Shielded Coplanar Waveguide with Liquid Crystals

Unconventional folded shielded coplanar waveguide (FS-CPW) has yet to be fully investigated for tunable dielectrics-based applications. This work formulates designs of FS-CPW based on liquid crystals (LC) for electrically controlled 0-360˚ phase shifters, featuring a minimally redundant approach for reducing the LC volume and hence the costs for mass production. The design exhibits a few conceptual features that make it stand apart from others, noteworthy, the dual-strip structure with a simplified enclosure engraved that enables LC volume sharing between adjacent core lines. Insertion loss reduction by 0.77 dB and LC volume reduction by 1.62% per device are reported at 77 GHz, as compared with those of the conventional single-strip configuration. Based on the proof-of-concept results obtained for the novel dual-strip FS-CPW proposed, this work provides a springboard for follow-up investible propositions that will underpin the development of a phased array demonstrator.

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

Process Discovery Enhancement with Trace Clustering and Profiling

The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

A Novel Hybrid Signal Decomposition Technique for Transfer Learning Based Industrial Fault Diagnosis

In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial purposes serves a crucial role. In contemporary times, although deep learning is a popular approach for fault diagnosis, it requires massive amounts of labelled samples for training, which is arduous to come by in the real world. Our contribution to introduce a novel comprehensive intelligent fault detection model using the Case Western Reserve University dataset is divided into two steps. Firstly, a new hybrid signal decomposition methodology is developed comprising Empirical Mode Decomposition and Variational Mode Decomposition to leverage signal information from both processes for effective feature extraction. Secondly, transfer learning with DenseNet121 is employed to alleviate the constraints of deep learning models. Finally, our proposed novel technique surpassed not only previous outcomes but also generated state-of-the-art outcomes represented via the F1 score.

Evaluation of Green Alternatives for Blockchain Proof-of-Work (PoW) Approach

Following the footprints of Bitcoins, many other cryptocurrencies were developed mostly adopting the same or similar Proof-of-Work (PoW) approach. Since completing the PoW puzzle requires extremely high computing power, consuming a vast amount of electricity, PoW has been strongly criticised for its antithetic stand against the notion of green computing. Use of application-specific hardware, particularly application-specific integrated circuits (ASICs) has further fuelled the debate, as these devices are of no use once they become “legacy” and hence obsolete to compete in the mining race, thus contributing to electronics waste. Therefore, this paper surveys the currently available alternative approaches to PoW and evaluates their applicability - especially their appropriateness in terms of greenness.