SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based access.This model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and scalability.However,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and delays.These issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous monitoring.To address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based IoHT.The SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop selection.This approach reduces broadcast storms,optimizes overall energy consumption,and extends network lifetime.The system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed strategy.The SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare scenarios.Results demonstrated that SEF significantly enhanced NDN-based IoHT performance.Specifically,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated areas.This forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

Research on the Method of Implementing Named Data Network Interconnection Based on IP Network

In order to extend the application scope of NDN and realize the transmission of different NDNs across IP networks,a method for interconnecting NDN networks distributed in different areas with IP networks is proposed.Firstly,the NDN data resource is located by means of the DNS mechanism,and the gateway IP address of the NDN network where the data resource is located is found.Then,the transmission between different NDNs across the IP network is implemented based on the tunnel technology.In addition,in order to achieve efficient and fast NDN data forwarding,we have added a small number of NDN service nodes in the IP network,and proposed an adaptive probabilistic forwarding strategy and a link cost function-based forwarding strategy to make NDN data obtaining the cache service provided by the NDN service node as much as possible.The results of analysis and simulation experiments show that,the interconnectionmethod of NDN across IP network proposed is generally effective and feasible,and the link cost function forwarding strategy is better than the adaptive probability forwarding strategy.

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.

Broadband Communications for High-Speed Trains via NDN Wireless Mesh Network

With the increasing utilization of High-Speed Trains （HSTs）, the need for a reliable and high-bandwidth Internet access under high-speed mobility scenarios has become more demanding. In static, walking, and low mobility environments, TCP/IP （transmission control protocol/Internet protocol） can work well. However, TCP/IP cannot work well in high-speed scenarios because of reliability and handoff delay problems. This is mainly because the mobile node is required to maintain the connection to the corresponding node when it handovers to another access point node. In this paper, we propose a named data networking wireless mesh network architecture for HST wireless communication （NDN-Mesh-T）, which combines the advantages of Wireless Mesh Networks （WMNs） and NDN architectures. We attempt to solve the reliability and handoff delay problems to enable high bandwidth and low latency in Internet access in HST scenarios. To further improve reliability and bandwidth utilization, we propose a Direction-Aware Forwarding （DAF） strategy to forward Interest packet along the direction of the running train. The simulation results show that the proposed scheme can significantly reduce the packet loss rate by up to 51% compared to TCP/IP network architecture. Moreover, the proposed mechanism can reduce the network load, handoff delay, and data redundancy.