Edge caching has emerged as an effective solution to the challenges posed by massive content delivery in the vehicular network. In vehicular networks, vehicles and roadside units (RSUs) can serve as intermediate relays with caching capabilities. However, due to the mobility of vehicles, the topology of the edge network changes frequently, which leads to frequent link interruptions and increases the transmission delay. This paper proposes an adaptive cooperative caching (ACC) strategy to adapt the frequent changes in the vehicular edge network topology and describes an optimization problem to minimize the average transmission delay. Then, the optimization problem is transformed into two sub-optimization problems: multiplechoice knapsack (MCK) problem and multiple minimum-weight dominating set (MMWDS) problem. Finally, two greedy algorithms with low complexity are designed to solve the above two optimization problems and obtain approximate solutions to the optimal caching decision. Simulation results show that ACC can effectively improve the cache hit rate and reduce the average transmission delay and the communication overhead compared with other caching strategies.

In recent years, semantic communication has garnered significant attention for its potential to address challenges in traditional communication systems. However, in complex communication environments, semantic communication still faces challenges, such as semantic information loss, low transmission efficiency, and poor adaptability. This article proposes a novel semantic communication with adaptive semantic reconstruction (SCASR) scheme to enhance transmission efficiency and adaptability in complex communication environments. First, a compression mechanism based on semantic importance is designed to achieve flexible and efficient semantic compression. Then, we develop an adaptive semantic reconstruction network to predict and reconstruct lost semantic information. Finally, we integrate an attention mechanism into the reconstruction network, dynamically adjusting parameter weights based on signal-to-noise ratio (SNR), semantic compression rate (SCR), and packet loss rate (PLR) to improve reconstruction quality and adaptability. To evaluate the efficiency of SCASR, we conduct extensive simulation experiments on semantic segmentation tasks using the Cityscapes dataset. Results demonstrate that SCASR outperforms existing semantic communication and traditional schemes, offering higher mean Intersection over Union (mIoU), and enhanced semantic transmission benefit (STB).

IP header compression technologies such as Robust Header Compression (ROHC) are well-known to improve bandwidth efficiency strategy using resource-constrained communication links. However, ROHC is a point-to-point approach so it must be maintained by the per-flow and per-hop, which is disadvantageous for bandwidth constrained networks (BCNs) featured by multi-hops transmission. In order to solve this problem, an efficient end-to-end IP header compressed packet forwarding framework for BCNs is proposed in this paper. Firstly, an end-to-end ROHC tunnel is established through the multi-hop transmission scheme. Then, based on the offer/answer model, a fast negotiation mechanism is established to ensure the context identifier (CID) is globally unique. Finally, the intermediate forwarder determines the next hop forwarding path according to CID. Therefore, besides reducing the transmission bandwidth and time consumption of compression and decompression per hop, it can also speed up forwarding the packets among BCNs. The simulation results show that the proposed framework improves forwarding efficiency and reduces end-to-end delivery latency and node power consumption in a BCN, which is independent of the channel conditions and the bit error rate.

In traditional wireless sensor networks (WSNs), packets are mainly transmitted in a multihop routing manner. The multihop transmission, however, leads to a hotspot problem in the sink connectivity area (SCA), and the overall network efficiency is reduced due to the quick battery power exhaustion of nodes in that area. This article proposes a novel balanced routing algorithm with transmission range adjustment (BRATRA) to address the network efficiency problem, including the energy efficiency and utilization issues. First, a balanced routing strategy is designed to deal with the SCA load imbalance problem. With the shortest balanced path, the amounts of forwarding packets for the nodes in the SCA and all the other intralayers become more even. From the perspective of power equilibrium in each routing path, each node then determines its accurate transmission radius according to the derived formula and performs power control to realize the even power utilization between interlayers, thereby prolonging the overall network lifetime. Performance evaluation validates that the proposed BRATRA strategy can achieve efficient power utilization in each intralayer and double the network lifetime as compared to the Dijkstra routing strategy. Additionally, it yields better power utilization fairness among nodes, and on average only 5% of battery power is unused for all network nodes, resulting in a network lifespan ten times larger than that using a conventional strategy.

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the $K$ -means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

High-performance multicast-enabled switches and routers are being constantly developed, which use a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether or not a packet is forwarded through a unique or multiple egress ports. Among these, Bloom filter (BF) and Scalar-pair Vectors Routing and Forwarding (SVRF) are being considered as two representations of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as space and time inefficiencies, especially for a carrier-grade PFE with high port-density feature. In order to solve these imperfections of SVRF, we propose an improved Fractional-N Scalar-matrix and Vectors Routing and Forwarding (Fractional- N SVRF) scheme based on re-examining the idea of the original SVRF. The Fractional- N SVRF preprocesses a scalar-matrix by dividing an n-element group into N sub-blocks, thus element' keys belonging to distinct sub-blocks and sub-scalars are allowed to reuse relatively smaller identical prime keys. Based on Fractional-N SVRF, membership queries can be partitioned to leverage task parallelism, therefore better performance is achieved in terms of memory consumption and computational complexity.