Information-centric networks (ICNs), a foreseen future Internet architecture, are focusing on the content itself rather than its hosting location. With this, the existing Internet protocol (IP)-based host-centric communication model can be transformed into a content-centric communication model with the support of in-network caching, name-based routing, and location-independent content domain names. Recently, ICN is proposed to be a potential Internet architecture for Internet-of-Things (IoT) networks due to minimal retrieval delays and reduced load on the data producer. Content retrieval from IoT nodes plays a prominent role in enhancing the performance of the ICN-IoT networks. State-of-the-art cache retrieval mainly focuses on the noncooperative caching mechanism, which is not efficient for constrained IoT networks. As an improvement, this article proposes a cooperative caching scheme for information-centric-IoT networks to optimize the cache hit with the support of a caching network topology model, a content popularity model, and an exogenous request access model. Simulations results reveal that the proposed cooperative caching scheme improves the cache hit rate, resource utilization, and reduce the network delay in IoT networks.

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.

The strategic prioritization in policy synergies heterogeneous stakeholders and opportunities that facilitates developing nations to game for betterment of society in limited resources. The strategic prioritization methodology was presented to frame long and short-term actions with available resources. The theme is to develop inherently de-carbonize economies with minimum spending, efforts, adopting best practices, exploit regional potential, optimize asset efficiency, recycling/reuse, technology and innovation, etc. However, trickle down global climate change regulations require level of awareness for regional energy dynamics, politics, bureaucratic structure, training and education, infrastructural weaknesses, financial barriers, etc. Several conflicting, non-measurable and inconsistency in policies destroy efforts towards net carbon zero and hindering de-carbonizing objectives in the developing world. No doubt, societal factors and their interest’s influences political systems engaged in energy transition policymaking, implementation and enforcement. Therefore, it’s time to organize energy transition efforts/planning in a way that it has minimum financial impact and keep developing economies on momentum. The article highlights sustainable policy instruments, initiatives, best practices, opportunities, innovation areas and identify stating steps those will inherently lead climate change ambitious targets of de-carburization in developing economies with minimum financial investment.

End‐to‐end application performance and throughput of vehicular cognitive transport control protocol depend on how efficiently the segments (TCP header) are being transmitted from source to destination. One way to enhance the performance of vehicular cognitive TCP protocol is by reducing the packet drops in between the source and destination. In general, packet drops occur in between the source and destination of Cognitive Radio Vehicular Ad Hoc Network (CR‐VANET) because of spectrum handoff or cognitive node handoff, or network congestion. In this paper, we focus on enhancing the performance of cognitive TCP protocol through “cognitive AODV routing protocol with the spectrum handoff mechanism.” In the proposed work, channel‐route control messages of cognitive AODV routing protocol are updated with the support of spectrum handoff which helps to provide the backup opportunistic channel during PU active and helps to reduce the end‐to‐end spectrum handoff packet drops. Simulation results reveal that the overall performance of the vehicular cognitive TCP protocol with the proposed spectrum handoff aware cognitive AODV routing protocol is enhanced as compared to the existing cognitive TCP protocol.