Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

Recent transmission of large volumes of data through mobile ad hoc networks (MANETs) has resulted in degraded Quality of Service (QoS) due to factors such as packet loss, delay, and packet drop in multipath routing. To address this issue, a traffic-aware Enhanced QoS-Aware Multipath Routing Protocol (EQMRP) has been proposed for real-time IoT applications in MANETs. EQMRP efficiently switches between multiple paths and monitors traffic conditions to maintain an optimal data transmission rate. The proposed method considers different delay sensitivity levels and link expiration time (LTE) to maintain QoS in each path. Through IoT application data analysis, EQMRP maintains QoS in each path more efficiently than conventional methods. The proposed method has been simulated and validated using MATLAB, and the performance analysis shows that EQMRP achieves a higher packet delivery ratio, lower delay, and reduced packet drop compared to conventional methods. In conclusion, the traffic-aware EQMRP protocol offers a significant improvement in QoS for real-time IoT applications in MANETs.

The paper on scientometric analysis contains the study about artificial intelligence (AI)- improved mobile and data-driven business decision-making and its impact on the Sustainable Development Goals (SDG 8: Decent Work and Economic Growth, and SDG 9: Innovation and Infrastructure). It is based on 2,443 articles in Scopus (2010–2025) and combines the metrics of performance, co-citation networks, and co-occurrence mapping of keywords to understand the intellectual basis and the research hotspots. The results show that the number of publications has been increasing since 2016, a sign of increasing application of AI and mobile systems in industry decision support. Based on the co-citation analysis, it identifies four intellectual clusters, including big data and analytics, smart cities, knowledge systems, and IoT infrastructures. The keyword analysis targets the cross-sectoral application of AI in business, healthcare, and digital governance. Since AI-guided mobile systems will enable making realtime, predictive, and inclusive decisions, the problems of data bias, ethical leadership, and digital disparities persist. The research creates awareness of the socio-technical systems theory and the theory of affordance, with the need for future research that combines interdisciplinary insights and explores Global South contexts and the actual application of AI-based decisions.