ABSTRACT Named data networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management is critical to NDN performance, especially under dynamic and high‐traffic conditions. A novel hybrid strategy, DRR‐MDPF, is introduced, integrating the Markov decision process forwarding (MDPF) model with the deficit round robin (DRR) algorithm. Optimal forwarding decisions are intelligently predicted based on key metrics such as bandwidth, delay, and the number of unsatisfied Interests, whereas fair and adaptive bandwidth allocation among competing data flows is ensured by DRR. Each router is modeled as a learning agent capable of adjusting its strategies through continuous feedback and probabilistic updates. According to simulation results obtained using ndnSIM, DRR‐MDPF was found to significantly outperform state‐of‐the‐art strategies including SAF, RFA, SMDPF, and LA‐MDPF across various metrics such as throughput, interest satisfaction rate (ISR), packet drop rate, content retrieval time, and load balancing. Notably, DRR‐MDPF maintains robustness under limited cache sizes and heavy traffic, offering enhanced adaptability and lower computational complexity due to its single‐path routing design. More accurate interface selection is achieved due to the multi‐metric decision‐making capability of DRR‐MDPF, leading to optimized network performance. Overall, DRR‐MDPF serves as an intelligent, adaptive, and scalable queue management solution for NDN, effectively addressing core challenges such as resource allocation, congestion control, and route optimization in dynamic networking environments.

ABSTRACT In wireless sensor networks (WSNs), nodes typically operate with limited energy supplies, making efficient data gathering essential for prolonging network lifespan. One effective approach to reduce energy consumption is clustering. However, using a fixed sink to collect data can lead to energy depletion in specific nodes, causing bottlenecks. A mobile sink, on the other hand, can address this issue by enhancing network performance and reducing energy load on individual nodes. This paper introduces a hierarchical cluster‐based data aggregation method that employs fuzzy logic alongside a mobile sink to improve energy efficiency. The strategy has two main stages: clustering and data aggregation. In the clustering stage, the process is split into two steps: identifying cluster heads and organizing clusters. A fuzzy inference system assesses each node's potential as a cluster head based on factors such as remaining energy, node connectivity, and centrality. The nodes with the highest scores are selected as primary cluster heads, while those with slightly lower scores serve as backup cluster heads. Clusters are then formed around these chosen heads. In the data aggregation phase, cluster heads gather data from cluster members and forward it either to a mobile sink or directly to the base station (BS). Cluster heads located within a specified range (distance ≤ r ) of the BS send data directly, while others route data via the mobile sink. This technique enhances data transmission efficiency and optimizes energy consumption, contributing to overall network improvement. The HDAMM approach demonstrated considerable advancements over earlier methods in terms of energy efficiency, delay reduction, packet delivery rate, and network longevity.

Summary The internet of things (IoT) refers to the interconnection of various devices through the internet. With the help of IoT, different applications and devices can interact with each other and even with humans through the internet. However, the widespread use of IoT in homes, offices, and private companies has raised concerns about privacy and security. The theft of user information by hackers is a major challenge that has hindered the adoption of IoT. This paper proposes a method for improving security on two levels: intra‐cluster and inter‐cluster. In the intra‐cluster phase, the cluster head uses fuzzy logic to measure the trust level of nodes. If the node is trusted, the information is received and sent to the destination. In the inter‐cluster phase, data between cluster heads is encrypted using column transposition encryption. The encryption is hop by hop, and the encryption key is changed at each hop. The proposed method is simulated using NS‐2 software. A comparison of the proposed method's performance with conventional methods shows that the proposed method results in better packet delivery rate, throughput, and prevention of link spoofing attacks.

The vehicular ad hoc network (VANET) is a promising technology to improve the comfort and safety of passengers, roads and urban traffic. Applications applied to VANETs require efficient routing protocols. Urban environments include tunnels, subways, overpasses, and multilevel highways, which indicate the multilevel information environment and specific conditions of radio channel dissemination. In this paper proposed a three‐dimensional (3D) evidence theory based, opportunistic routing protocol, called 3DEOR, which address the above issues using a new hybrid criterion called PAL. The PAL criterion includes three metrics of packet delivery probability to compensate for unreliable dissemination environments, packet advancement appropriated to 3D environments to reduce the number of hop counts and level to improve link connectivity. Prioritise the relay set members based on the new criteria to select the best relay node. In cases where there is no certainty, the evidence theory can be a good choice for combining the three metrics of the PAL criterion. The simulation results show the superiority of the proposed protocol over the 3D link state aware geographic opportunistic (3DLSGO) and 3D greedy perimeter stateless routing (3DGPSR) protocols in network performance indices such as packet delivery rate, end‐to‐end delay and average hop count.

Energy consumption, network lifetime and coverage are the major challenges in wireless sensor networks. This study proposes a method that grids network area into square cells on a geographical basis. The node possessing the highest energy is selected as an aggregator in each cell. Given the cover area and remaining energy, the firefly algorithm functions to select and actively maintain the most suitable node in each cell while deactivating others. The uniqueness of the proposed evolutionary firefly algorithm is to ensure the desirable and effective network coverage. Omnet++ was applied for simulation study, the results of which were compared with those from both TCO and ACO‐Greedy models. A significantly greater coverage was provided by the proposed method with fewer nodes and 30% decrement in energy demand. The added advantage was the elongation of network lifetime.