5 research outputs found
A context-aware improved POR protocol for Delay Tolerant networks
ABSTRACTThe intermittent network connectivity and unawareness of global network knowledge are remarkable challenges when designing efficient Position-based Opportunistic Routing (POR) for Delay Tolerant Networks (DTNs). The best progress set selection of POR effectively handles the intermittent connectivity issue and improves the reliable performance of DTNs. Hence, the optimal progress set selection and catching capacity decide the routing reliability of DTN-POR. This paper proposes a context-aware DTN-POR protocol STAP to ensure reliable DTN routing with minimum overhead STAP design is divided into three folds: application diversity and network failures, context-aware best progress set selection, and enhanced caching management. The application diversity and network failure switch the nodes in harsh environments to DTN mode for transmitting the data packets successfully to the destination. Selecting context-aware relay nodes ensures highly successful data transmissions in the absence of end-to-end connectivity. The probability estimation maximizes the data reachability by considering unique node and application-level context attributes in the best progressive set. Finally, the enhanced catching management strategy limits the number of data copies with effective catch invalidation, resulting in minimum overhead. The proposed STAP accomplishes better results in different scenarios and improves the packet delivery ratio with a minimum duplication rate
Artificial intelligence for media ecological integration and knowledge management
Information Technology’s development increases day by day, making life easier in terms
of work and progress. In these developments, knowledge management is becoming mandatory in
all the developing sectors. However, the conventional model for growth analysis in organizations is
tedious as data are maintained in ledgers, making the process time consuming. Media Ecology, a new
trending technology, overcomes this drawback by being integrated with artificial intelligence. Various
sectors implement this integrated technology. The marketing strategy of Huawei Technologies
Co. Ltd. is analyzed in this research to examine the advantages of Media Ecology Technology
in integration with artificial intelligence and a Knowledge Management Model. This combined
model supports sensor technology by considering each medium, the data processing zone, and
user location as nodes. A Q-R hybrid simulation methodology is implemented to analyze the data
collected through Media Ecology. The proposed method is compared with the inventory model, and
the results show that the proposed system provides increased profit to the organization. Paying
complete attention to Artificial intelligence without the help of lightweight deep learning models
is impossible. Thus, lightweight deep models have been introduced in most situations, such as
healthcare management, maintenance systems, and controlling a few IoT devices. With the support
of high-power consumption as computational energy, it adapts to lightweight devices such as mobile
phones. One common expectation from the deep learning concept is to develop an optimal structure
in case time management.Web of Science115art. no. 22
Evaluation of the Quality of Practical Teaching of Agricultural Higher Vocational Courses Based on BP Neural Network
Agriculture is the backbone of any developing or developed country that makes any living to survive. To make food available throughout the year, it is necessary to know about agriculture and the work and strategies involved. Hence, agricultural courses have to be introduced to higher education students. Additionally, agriculture-related methods are available in many higher education institutions for longer. However, students and teachers will face difficulties in real-time practical classes during certain challenging circumstances. These situations require the teacher to utilize trending technologies to improve the teaching and learning process and to make it more manageable. In this study, for this process, a novel neural network-based recognition algorithm (NN-RA) is implemented that works similarly to a backpropagation neural network (BP-NN) to provide a practical agriculture course. The proposed BP-NN is compared with the existing NN-RA, I-SC, and I-VDT algorithms based on the data transfer and signal-to-noise ratio. From the results, it can be observed that the proposed BP-NN attains a higher accuracy in data transfer of 99%
Enhanced dual-selection krill herd strategy for optimizing network lifetime and stability in wireless sensor networks
Wireless sensor networks (WSNs) enable communication among sensor nodes and require
efficient energy management for optimal operation under various conditions. Key challenges include
maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer
network lifetime contributes to improved data transfer durability, sensor conservation, and scalability.
In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy
utilization and reduces inter-node communication, addressing energy conservation challenges in
node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism
is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual
selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation
and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis
with benchmark approaches demonstrates that the proposed model enhances network lifetime by
23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more
efficient and reliable solution for WSN energy management.Web of Science2317art. no. 748