ABSTRACT Blockchain effectively addresses issues of trust and information asymmetry in distributed network architectures; however, its decentralization significantly constrains performance. Sharding is a viable solution to enhance blockchain scalability and improve network efficiency. In this context, this paper proposes a Sharding Algorithm Based on Coordinate Interval Distance(SACID), based on coordinate distance intervals, and develops a blockchain‐sharded network communication model. This model partitions the blockchain network into multiple shards, enabling parallel information processing and enhancing scalability. Additionally, a Coordinate‐based Network Path Selection Communication Strategy (CNPSC) is introduced to mitigate inter‐shard communication overhead and consistency challenges, optimizing communication efficiency while maintaining security and decentralization. Furthermore, this paper incorporates the blockchain P2P core data transmission protocol Gossip, Dijkstra's algorithm, and the newly designed CNPSC communication strategy into the blockchain‐shaded network communication model as data transmission protocols. Experimental analysis evaluates the robustness and communication efficiency of the proposed sharding model and CNPSC communication strategy. Results demonstrate that within the proposed blockchain‐sharded network model, Gossip, Dijkstra, and CNPSC all exhibit strong robustness. Among them, the proposed CNPSC achieves the highest communication efficiency and delivers the best network performance.

The penetration rate of electronic vehicles (EVs) has been increasing rapidly in recent years, and the deployment of EV infrastructure has become an increasingly important topic in some solutions of the Internet of Things (IoT). A reasonable balance needs to be struck between the user experience and the deployment cost of charging stations and the number of charging piles. The deployment of EV’s charging station is a challenging problem due to the uneven distribution and mobility of EV. Fortunately, EVs move with a certain regularity in the urban environment. It makes the deployment strategy design of EV charging stations feasible. Therefore, we proposed a deployment strategy of EV charging station based on particle swarm optimization algorithm to determine the charging station localization and number of charging piles. This strategy is designed based on the nonuniform distribution of EV in a city scene map, at the same time, the distribution of EV at different times, which makes the strategy more reasonable. Extensive simulation results further demonstrated that the proposed strategy can significantly outperform the K‐means algorithm in the urban environment.

This paper aims to explore the problem of energy economy of mobile devices in the Mobile Crowdsensing (MCS) scenario. The neighbor scanning mechanism of mobile devices usually consumes most of the energy in multi-hop message transmission. Traditional mechanisms such as chaotic neighbor detection and continuous neighbor discovery, can easily exhaust the limited energy. Since they are actually unnecessarily considering the strong correlation between the transmission opportunity and the social characteristics. Therefore, diminishing energy consumption is of key importance toward energy-efficient MCS. Sleeping strategy stands out as a promising approach to improve energy efficiency and hold the the network metrics in MCS applications, while challenges still lie in achieving effective scheduling given the changing environment. In this study, we proposed a novel self-adapt sleeping scheduling approach based on the correlation between the pedestrian's own historical trajectory and geographic grid information for energy saving (ESGeo). As the grid-based method can well record the encounter characteristics of nodes, ESGeo is able to pick flexible duty-cycling strategies for mobile devices in each distinguishable grid. This enables, mobile devices to avoid excessive scanning in low-probability encounter areas. Extensive simulation results further demonstrated that the proposed approach can significantly outperform the typical routing approaches in terms of energy-efficiency, without largely affecting the overall networking performance.

With the implementation arriving of the Internet of Things, multi-hop transmission by short-range communication becomes more important than ever before in DTN research areas. The energy efficiency has been received more attention by researchers due to constrained energy battery. From earlier research, the vast majority of energy consumption is used for neighbors detecting. Duty-cycle strategy is an efficient way to improve power efficiency, whereas it is too simple to take advantage of contacting opportunities. Therefore, we designed a sleeping strategy for the mobile devices, and named self-adaptive duty-cycle, which based on the social contacts of the devices in the urban scenario. This approach mainly focuses on the historical encounters of the devices, and then it uses the weighted moving average approach to predict the encounter situation of the mobile device, and adjust the size of the duty-cycle to achieve the energy-saving purpose. Our simulation results show that self-adaptive duty-cycle is superior to other fixed ratio of duty-cycles in energy efficiency, network lifetimes. Meanwhile, the main indicators of network performance are not affected by sleeping behavior.

Most existing DTN routing algorithms can't show efficient network performance. Prophet is a routing protocol widely used for DTNs. The delivery predictability of active nodes will obviously reduce with the same speed as less active nodes. In this paper, we propose an Adaptive Probability Prediction Routing (APPR) approach, which can dynamically adjust the aging factor based on the active degree of nodes. Leveraging the APPR approach, the delivery predictability of each node can be reduced in different speed if the node does not encounter the other node. Furthermore, when two nodes encounter, the forward decision depends on the distance between each node to the destination node, if the delivery predictabilities of encountered nodes are similar. Compared with the traditional Prophet, simulation results demonstrate the proposed APPR approach can significantly improve successful delivery ratio, reduce overhead ratio and the average latency.

Vehicular communications are widely developed through three types of network structures including vehicle-to-infrastructure (V2I), infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V). The information transmission requirements of V2I and I2V links needs high capacity and V2V links are vulnerable to the outage cases caused by high mobility of vehicles. Aim to this, we propose a Joint Uplink-Downlink Resource Allocation under Outage Case (RAOC) scheme based on both traceable slow-varying large scale fading information and the fast-varying small scale fading information substituted into outage probability. Importantly, the stability of large V2V links can be guaranteed with the tolerable reduction of the sum capacity of all V2I links and I2V links, which is considered as the optimization objective. The simulation results indicate that the proposed algorithm improves the stability performance of V2V links at the little sacrifice of the sum capacity compared with another algorithm.