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Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

Designing an efficient routing protocol that opportunistically forwards data to the destination node through nearby sensor nodes or devices is significantly important for an effective incidence response and disaster recovery framework. Existing sensor routing protocols are mostly not effective in such disaster recovery applications as the networks are affected (destroyed or overused) in disasters such as earthquake, flood, Tsunami and wildfire. These protocols require a large number of message transmissions to reestablish the clusters and communications that is not energy efficient and result in packet loss. This paper introduces ODCR - an energy efficient and reliable opportunistic density clustered-based routing protocol for such emergency sensor applications. We perform simulation to measure the performance of ODCR protocol in terms of network energy consumptions, throughput and packet loss ratio. Simulation results demonstrate that the ODCR protocol is much better than the existing TEEN, LEACH and LORA protocols in term of these performance metrics.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

The purpose of this paper is to explore the feasibility of mud brick houses as an alternative to the more widely adopted emergency shelters used in war-torn Syria. It focuses on the mud houses’ sustainability, cost effectiveness, cultural preference, income generation and security.

This study uses a qualitative research approach to a single case study. Interviews were conducted with field experts from the Qatar Red Crescent Society and members of the internally displaced persons (IDPs) community living in mud brick houses.

The findings reveal that the utilisation of mud houses is a good alternative in relief efforts. The houses are sustainable and cost-effective, take into consideration cultural dynamics and provide economic empowerment to IDPs. However, the maintenance of mud brick houses turns out to be the largest flaw, and this problem requires further research.

The paper highlights the inefficiency of current emergency sheltering practices in Syria and reveals that tents are not a preferred mode of shelter. It further sheds light on a unique case study in which mud houses were used by an aid and development organisation, highlighting its strengths and weaknesses. This paper contributes to practical discussion and ideation on more appropriate housing for IDPs.

This paper aims to investigate the thermal performance of equivalent square and circular thermal systems and compare the heat transport and irreversibility of magnetohydrodynamic (MHD) nanofluid flow within these systems.

The research uses a constraint-based approach to analyze the impact of geometric shapes on heat transfer and irreversibility. Two equivalent systems, a square cavity and a circular cavity, are examined, considering identical heating/cooling lengths and fluid flow volume. The analysis includes parameters such as magnetic field strength, nanoparticle concentration and accompanying irreversibility.

This study reveals that circular geometry outperforms square geometry in terms of heat flow, fluid flow and heat transfer. The equivalent circular thermal system is more efficient, with heat transfer enhancements of approximately 17.7%. The corresponding irreversibility production rate is also higher, which is up to 17.6%. The total irreversibility production increases with Ra and decreases with a rise in Ha. However, the effect of magnetic field orientation (γ) on total EG is minor.

Further research can explore additional geometric shapes, orientations and boundary conditions to expand the understanding of thermal performance in different configurations. Experimental validation can also complement the numerical analysis presented in this study.

This research introduces a constraint-based approach for evaluating heat transport and irreversibility in MHD nanofluid flow within square and circular thermal systems. The comparison of equivalent geometries and the consideration of constraint-based analysis contribute to the originality and value of this work. The findings provide insights for designing optimal thermal systems and advancing MHD nanofluid flow control mechanisms, offering potential for improved efficiency in various applications.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

The purpose of this paper is to develop a comprehensive overview of green innovation (GI) in China, which is carried out by reviewing the evolution of GI from 2000 to 2019, and the main type of technology, actors and localizations. When appropriate, GI is compared to non-GI.

The study uses patent data from the European Patent Office database (PATSTAT); these data are processed to map trends and identify the main contributors to GI and the location of such innovation. The findings are then discussed and complemented with academic literature.

Key findings reveal an increasing divergence between GI and nongreen innovation after the 2008 crisis. It is also observed that solar energy appears to be the main component of GI in China, with a shift from photovoltaic thermal energy to solar photovoltaic energy after 2008. Other areas, such as waste management, greenhouse gases capture and climate change adaptation, are less innovative. Companies play an essential role in the development of all types of innovation. In terms of location, green patents are mainly filed in China’s three main megacities. The study also highlights the significant role of the Chinese state, which led policies shaping the trajectories and forms of GI.

This study expands knowledge on GI in China, highlighting its main specificities and the role of key actors. It provides to the reader a comprehensive picture of China’s green policies and innovation realities. The results can therefore be used to improve the understanding of GI evolution in China and facilitate the formulation of new research questions.

This study aims to evaluate blockchain as an e-government governance model. It assesses its alignment with legal frameworks, emphasizing robustness against disruptions and adherence to existing laws.

The paper explores blockchain’s potential in e-government, focusing on legal, ethical and governance aspects. It conducts an in-depth analysis of blockchain’s integration into data governance, emphasizing legal compliance and resilient security protocols.

The study comprehensively evaluates blockchain’s implementation, covering privacy, interoperability, consensus mechanisms, scalability and regulatory alignment. It highlights governance’s critical role in ensuring legal compliance within blockchain paradigms.

Ethical and legal concerns arising from blockchain adoption remain unresolved. The study underscores how blockchain challenges its core principles of anonymity and decentralization in e-government settings.

The framework outlined offers potential for diverse technological environments, albeit raising ethical and legal queries. It emphasizes governance’s pivotal role in achieving legal compliance in blockchain adoption.

Blockchain’s impact on legal and ethical facets necessitates further exploration to align with its core principles while addressing governance in e-government settings.

This study presents a robust framework for assessing blockchain’s viability in e-government, emphasizing legal compliance, despite ethical and legal intricacies that challenge its fundamental principles.

The motivation to participate in crowdsourcing (CS) platforms is an emerging challenge. Although researchers and practitioners have focused on crowd motivation in the past, the results obtained through such practices have not been satisfactory. Researchers have left unexplored research areas related to CS pillars, such as the evolution of the crowd’s primary motivations, seekers applying effective policies and incentives, platform design challenges and addressing task complexity using the synchronicity of the crowd. Researchers are now more inclined to address these issues by focusing on sustaining the crowd’s motivation; however, sustaining the crowd’s motivation has many challenges.

To fill this gap, this study conducted a systematic literature review (SLR) to investigate and map the challenges and factors affecting sustained motivation during CS with the overcoming implications. Studies that satisfied the inclusion criteria were published between 2010 and 2021.

Important sustainable factors are extracted using the grounded theory that has sustained participation and the factors' cohesion leads to the identification of challenges that the pillars of CS face. Crowds being the most vital part of CS contests face the challenge of engagement. The results reported the factors that affect the crowd’s primary and post-intentions, perceived value of incentives and social and communal interaction. Seekers face the challenge of knowledge and understanding; the results identify the reason behind the crowd’s demotivation and the impact of theories and factors on the crowd's psychological needs which helped in sustaining participation. Similarly, the platforms face the challenge of being successful and demanding, the results identify the latest technologies, designs and features that seekers proclaim and need the platforms designer's attention. The identified task challenges are completion and achievement; the authors have identified the impact of trait of task and solving mechanisms that have sustained participation.

The study identifies, explores and summarizes the challenges on CS pillars researchers are facing now to sustain contributions by keeping participants motivated during online campaigns. Similarly, the study highlights the implication to overcome the challenges by identifying and prioritizing the areas concerning sustainability through the adoption of innovative methods or policies that can guarantee sustained participation.

Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.