These days, wireless sensor networks (WSNs) are expanding fast and are used in many fields such as healthcare, battlefields, etc. Depending upon the type of sensor, they are transmitting a considerable amount of data in a short duration, so security is a significant issue while transferring the data. So, it is essential to solve security concerns while transferring data by secure routing in wireless sensor networks. We address this challenge by proposing Trusted Energy-Aware Hierarchical Routing (TEAHR), a new framework for a multi-level trust assessment that raises the security level in WSNs. TEAHR introduces a variety of trust metrics ranging from energy trust to forwarding trust to consistency trust to behavioral trust to anomaly detection, unlike existing models, enabling it to effectively address the challenges of dynamic network topologies and evolving cyber threats. Trust-based routing mechanisms are usually associated with high computation and storage complexity and susceptibility to collusive attacks such as spoofing. The mechanism in TEAHR overcomes these challenges by placing an adaptive trust assessment mechanism that adapts to the background network conditions and real-time activities of the nodes. We show through empirical analysis in this paper that TEAHR not only uses computational and storage resources efficiently but also enhances network performance and security. Our experimental setup presents the simulation approach to prove our proposed protocol of TEAHR in comparison with typical trust models under different scenarios of node mobility, variable node density, and sophisticated security attacks such as Sybil, wormhole, and replay attacks. TEAHR keeps the network connected, even when the nodes are isolated due to trust misbehavior, and demonstrates that widely it reduces the chances of misjudgment in trust evaluation. Moreover, we explore the scalability of TEAHR across large networks as well as its performance in computationally constrained contexts. We have verified through our detailed investigation that the energy metrics used uniquely in TEAHR extend the life of the network while increasing data routing trust and trustworthiness. The comparisons of TEAHR with conventional techniques show that the proposed algorithm reduces total latency by 15%, enhances energy efficiency by around 20%, and maintains a stable packet forwarding rate, which is highly desirable for accurate operation in adversarial environments, as demonstrated through comparative analysis. Through in-depth theoretical and practical analysis, TEAHR is confirmed as a high-performance framework that outperforms currently existing studies for WSN security, making TEAHR a strong candidate for use in industrial IoT applications and urban sensor networks.

With increasing demand of agile methodology in software development, almost every software organization is trying to move far from previous methods to adopt agile methodology in software development. In place of being extrapolative, agile somewhat being adaptive and focuses on customer satisfaction. It supports self-organized groups that work for technical excellence to increase agility. But number of team members is an issue that is in turn controlled by team perspective. The technique used in this research focuses on three diverse sized agile groups making software's using the same technologies. Both objective and subjective actions are used and the outcomes are reinforced by a study. The result clearly demonstrates that for better results in agile software development, it is critical to select the right persons to be considered as a good team. This paper focuses on different programming advancement techniques, their benefits and demerits, and how to choose the appropriate strategies for a specific circumstance. In addition it examines agile methodologies with the help of a case study that focuses on the advantages of agile over the conventional methods used in the software industry. This paper introduces a concept namely “Two Way Assessment” an approach to boost the process to make it more effective by identifying and removing the defects.

Agile methodology promotes changing requirements and its journey has come a long way in the software industries giving edge as well as dimensions to software products. According to the statistics, agile methodology is more successful than traditional projects. The agile team manages the project more efficiently and delivers quality product. In agile methodology, customer satisfaction is a priority due to rapid development as well as continuous delivery which is the core of the Dynamic System Development (DSD). Thus, the industry keeps pace with the new expertise and changing market situation. This paper reveals the utilization of the Fuzzy Analytic Hierarchical Process (FAHP) which is a Multi-Criteria Decision Making (MCDM) process used in the software industry by correlating the selected alternatives using fuzzy triangular numbers, which decides on the best process of agile. Thus, the methodology of Fuzzy AHP for agile process selection is discussed in this research paper. Moreover, by expressing Fuzzy AHP comprehensively and numerally in this paper, the best likely process selection for agile methodology amongst various criteria as well as decision making issues is implemented.

With the advent of disruptive methodologies to ensure higher success rates in software engineering processes, the agile software development principles have gained the interest of large organizations. Agile methodology is characterized by iterative and incremental nature, customer-oriented development, adaptability and brisk development cycles. This study analyses the criteria of gathering data and evaluation is conducted by the expert’s judgement from which valuable information is extracted to deliver quality software. Critical Success Factors (CSF) is dynamic in implementing an autonomous methodology using entropy approach from the organisation perspective CSF must be known initially following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which is a multiple-criteria decision-making (MCDM) approach. The approach’s aim is to in parallel minimize and maximise the critical factors. CSF which needs more attention are emphasized from this research findings and is arranged from high to low. We conclude this study by selecting the best software methodology using entropy and TOPSIS.

Vehicular networks are a continuation of vehicle-to-everything (V2X) communication, which is becoming the foundation of intelligent transportation systems and allows vehicles, roadside units, and grid infrastructure to communicate reliably. Nevertheless, the decentralized aspect of V2X renders it very susceptible to adversarial cyberattacks, both on a large scale such as DDoS attacks, spoofing, and Sybil attacks. Conventional centralized intrusion detection systems (IDSs) have limitations in terms of latency, bandwidth overhead, and privacy risks. In this study, we introduce a Differentially Private Federated Adversarial Learning (DP-FAL) model that integrates federated learning to train decentralized models with adversarial defence schemes and differential privacy tools to address gradient leakage. The proposed DP-FAL framework achieves a detection accuracy of 94.2% and a communication overhead reduction of up to 23%. This shows that it can be used in bandwidth-limited and latency-sensitive V2X systems because of its ability to ensure high detection reliability with low communication costs. The suggested DP-FAL model is a privacy-conserving, scalable, and robust intrusion prevention system that can be used real-time V2X conditions. These results indicate that DP-FAL has the potential to be deployed safely, reliably, and sustainably in next-generation transportation systems.