Search Results (595)

Business sustainability emerges as a cornerstone for balancing economic growth, environmental preservation, and social well-being, establishing itself as a global priority. However, this approach still faces significant challenges among micro, small, and medium-sized enterprises (MSMEs), particularly in emerging regions such as Ciudad Victoria, Tamaulipas, Mexico. This study examines how business networks facilitate the adoption of sustainable practices through knowledge transfer and inter-organizational collaboration, emphasizing the influence of sociological factors and social interactions. Using a qualitative approach that combines semi-structured interviews and social network analysis with UCINET software version 6.776, an active network of 54 companies is analyzed. The results highlight central nodes with high betweenness centrality, underscoring their role as catalysts in disseminating sustainable practices. A significant correlation is also identified between types of business interactions and the implementation of these practices, emphasizing the strategic value of cooperation in fostering responsible behaviors. This study helps bridge the theoretical gap regarding sustainability in MSMEs and offers practical implications for designing policies that enhance business collaboration for sustainable development. Ultimately, it underscores the importance of understanding business interaction dynamics and their implications for driving sustainable transformation in regional contexts. Full article

Federated learning has attracted widespread attention due to its strong capabilities of privacy protection, making it a powerful supporting technology for addressing data silos in the future. However, federated learning still lags significantly behind traditional centralized learning in terms of learning efficiency and system security. In this paper, we first construct a hierarchical federated learning architecture integrated with blockchain based on the cooperation of the cloud, edge, and terminal, which has the ability to enhance the security of federated learning while reducing the introduction costs of blockchain. Under this architecture, we propose a semi-asynchronous aggregation scheme at the edge layer and introduce a hierarchical aggregation scheme that combines it with synchronous aggregation at the cloud end to improve system efficiency. Furthermore, we present a multi-objective node selection scheme that considers various influencing factors such as security and efficiency. We formulate the node selection problem as a Markov Decision Process (MDP) and propose a solution based on deep reinforcement learning to address it more efficiently. The experimental results show that the proposed scheme can effectively improve system efficiency and enhance system security. In addition, the proposed DQN-based node selection algorithm can efficiently realize the selection of the optimal policy. Full article

Deep Reinforcement Learning (DRL) has demonstrated promising capabilities for routing optimization in Software-Defined Networks (SDNs). However, existing DRL-based routing algorithms are struggling to extract graph-structured information and constrained to a fixed topology, suffering from the lack of robustness. In this paper, we strengthen the advantages of Graph Neural Networks (GNNs) for DRL-based routing optimization and propose a novel algorithm named Graph Transformer Star Routing (GTSR) to enhance robustness against topology changes. GTSR utilizes the multi-agent architecture to enable each node to make routing decisions independently, and introduces a Graph Transformer to equip agents with the capabilities of handling topology changes. Furthermore, we carefully design a global message-passing mechanism with a virtual star node and a path-based readout method, enhancing the long-range perception of traffic and the detection of potential congestion for routing decision-making. Moreover, we construct a multi-agent cooperation mechanism to facilitate the learning of universal perceptual strategies and reduce the amount of computation. Extensive experiments on multiple real-world network topologies demonstrate that GTSR is capable of adapting to unseen topology changes without retraining and decreases end-to-end latency by at least 47% and packet loss rate by at least 10% compared to all baselines, highlighting strong robustness. Full article

Precise cooperative node localization is essential for the application of multifunctional integrated radio frequency (RF) sensor networks in military and civilian domains. Most geometric localization methods commonly rely on observation data from multiple receiving nodes or anchor points with known positions and synchronized clocks, producing complex system architectures and high construction costs. To address this, our paper proposes an effective single-station cooperative node localization technique, where the observation station only requires two antennas for operation. Leveraging prior knowledge of the geometry of surrounding structures, multiple virtual stations (VSs) are constructed by mining the spatiotemporal information contained in the multipath components (MPCs) to realize target positioning. The proposed method consists of two steps. In the first step, an unambiguous dual-antenna direction-finding algorithm is designed to extract the spatial information of MPCs and construct VSs, allowing a preliminary estimate of the source position (SP). In the second step, the path delays are extracted via matched filtering, while the spatiotemporal information is correlated based on the energy distribution for a more precise SP estimation. Simulations and experimental results demonstrate that our algorithm achieves high-precision single-station localization for a collaborative node, with positioning accuracy typically within $0.1$ m. Full article

With the deepening of international trade and the increasing strain on water resources, the importance of the virtual water trade of grain products on an international level has become increasingly prominent. Based on FAOSTAT and water value research reports, this study constructed the virtual water trade networks of wheat, rice, maize, and soybeans for 29 major grain trading countries in 2012 and 2022 and measured their network indicators and virtual water flow patterns. In addition, a QAP regression analysis was used to study the influencing factors of the virtual water trade network for grain products from the perspective of four dimensions: economic scale, geographical characteristics, resource endowment, and policy agreements. The results were as follows: Firstly, from 2012 to 2022, the virtual water trade of wheat and rice shifted from a state of net virtual water outflow to net virtual water inflow, and the overall net virtual water flows of maize and soybeans both showed a net virtual water inflow. Secondly, wheat’s virtual water trade network participants had reduced obvious “small-world” features, and KOR, the USA, TUR, and IND have long been at the center of that network. When the core nodes of the virtual water trade network of rice were reduced, the network tended to be decentralized. In that network, IND, NPL, the USA, and ZAF always occupied dominant positions. The overall connectivity of the maize virtual water trade network increased, with both the USA and JPN as the trade core. The number of core countries in the soybean virtual water trade network increased; significantly, CHN, the USA, and THA were in dominant positions. Lastly, the GDP at the economic scale was the biggest core driving factor of all virtual water trade networks of various grain products, followed by per capita arable land area in terms of resource endowment. In addition, the geographic characteristics and trade agreements of the virtual water trade networks of grain products also had a more significant negative impact. This paper argues that countries should make trade adjustments for their own developing disadvantaged grain products, vigorously develop their national economies, optimize the structure of the grain trade, and promote benign cooperation in international virtual water trade for grain products. Full article

In this paper, we consider a three-node free space optical (FSO) decode-and-forward (DF) cooperative network. The relay is not connected to a permanent power supply and relies solely on the optical energy harvested (EH) from the source node. This energy is accumulated in an energy buffer in order to enable the relay–destination communications. Moreover, buffer-aided (BA) relaying is considered where the relay is equipped with a data buffer for storing the incoming packets. For such networks, we propose a relaying protocol that delineates the roles of the source and the EH BA relay in each time slot. We develop a Markov chain framework for capturing the dynamics of the data and energy buffers. We derive the transition probabilities between the states of the Markov chain after discretizing the continuous-value energy buffer allowing for the evaluation of the analytical performance of the considered system. A numerical analysis is also presented over a turbulence-induced gamma–gamma fading channel highlighting the impacts of the data rate threshold levels, relay position, relay transmit power and propagation conditions on the achievable performance levels. Results validate the accuracy of the theoretical analysis and demonstrate significant reductions in the network outage, especially when the relay’s transmit level is appropriately selected. Full article

IEEE 802.11 is one of the most common medium access control (MAC) protocols used in wireless networks. The carrier sense multiple access with collision avoidance (CSMA/CA) mechanisms in 802.11 have been designed under the assumption that all nodes in the network are cooperative and trustworthy. However, the potential for non-cooperative nodes exists, nodes that may purposefully misbehave in order to, for example, obtain extra bandwidth, conserve their resources, or disrupt network performance. This issue is further compounded when receivers such as Wi-Fi hotspots, normally trusted by other module nodes, also misbehave. Such issues, their detection, and mitigation have, we believe, not been sufficiently addressed in the literature. This research proposes a novel trust-incorporated MAC protocol (TMAC) which detects and mitigates complex node misbehavior for distributed network environments. TMAC introduces three main features into the original IEEE 802.11 protocol. First, each node assesses a trust level for their neighbors, establishing a verifiable backoff value generation mechanism with an incorporated trust model involving senders, receivers, and common neighbors. Second, TMAC uses a collaborative penalty scheme to penalize nodes that deviate from the IEEE 802.11 protocol. This feature removes the assumption of a trusted receiver. Third, a TMAC diagnosis mechanism is carried out for each distributed node periodically, to reassess neighbor status and to reclassify each based on their trust value. Simulation results in ns2 showed that TMAC is effective in diagnosing and starving selfish or misbehaving nodes in distributed wireless networks, improving the performance of trustworthy well-behaving nodes. The significant feature of TMAC is its ability to detect sender, receiver, and colluding node misbehavior at the MAC layer with a high level of accuracy, without the need to trust any of the communicating parties. Full article

Since MANET consists of only nodes and is in wireless communication with limited resources, cooperation between nodes is very important. Multicasting technology supports various applications that allow data to be immediately transmitted to target nodes. In environments such as MANET, where nodes are constantly moving, finding an efficient path from the source to the destination is a critical challenge. Providing integrity for the data transmitted from the source to the target node set is also an important part. Maintaining the state of neighbor nodes not only increases the communication and processing overhead but also requires more memory. In this paper, we propose a zone-based secure routing algorithm to improve the performance of routing protocols. We will also prove efficient management of node groups in an area and better scalability, performance, and security despite frequent topology changes by using group keys. To evaluate the proposed technique, detailed and extensive simulation performance with PAST-DM and MSZRP are evaluated. The simulation results show that the proposed technique, compared to other routing protocols, can achieve scalability by maintaining the routing load even if the speed of nodes, the number of sources, the number of group members, and the size of the network increase. Full article

Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and cloud computing to harness their collective strengths, facilitating optimised data processing across these tiers. Our approach optimises performance, reducing energy consumption, and lowers costs. We evaluate our architecture through a series of experiments conducted on a purpose-built testbed. The results demonstrate significant improvements, with speedups of up to 7.5 times and energy savings reaching 80%, underlining the effectiveness and practical benefits of our cooperative edge-fog-cloud model in supporting the dynamic computational needs of IoT ecosystems. We argue that a multi-tier (e.g., edge-fog-cloud) dynamic task offloading and management of heterogeneous devices will be key to flexible edge computing, and that the advantage of task relocation and offloading is not straightforward but depends on the configuration of devices and relative device capabilities. Full article

Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions. Full article

To enhance the operational efficiency of high-speed trains (HSTs), Train-to-Train (T2T) communication has received considerable attention. This paper introduces a T2T cooperative communication model that allows direct information exchange between HSTs, enhancing communication efficiency and system performance. The model incorporates a mix of dynamic and static nodes, and within this framework, we have developed a novel Dynamic Hierarchical Algorithm (DHA) to optimize communication paths. The DHA combines the stability of traditional algorithms with the flexibility of machine learning to adapt to changing network topologies. Furthermore, a communication link quality assessment function is proposed based on stochastic network calculus, which accounts for channel randomness, allowing for a more precise adaptation to the actual channel environment. Simulation results demonstrate that DHA has superior performance in terms of optimization time and effect, particularly in large-scale and highly dynamic network environments. The algorithm’s effectiveness is validated through comparative analysis with traditional and machine learning-based approaches, showing significant improvements in optimization efficiency as the network size and dynamics increase. Full article

The integration of unmanned aerial vehicles (UAVs) into vehicular networks offers numerous advantages in enhancing communication and coverage performance. With the ability to move flexibly in three-dimensional space, UAVs can effectively bridge the communication gap between intelligent connected vehicles (ICVs) and infrastructure. However, the rapid movement of UAVs and ICVs poses significant challenges to the stability and reliability of communication links. Motivated by these challenges, integrated UAV–ICV networks can be viewed as vehicular delay-tolerant networks (VDTNs), where data delivery is accomplished through the “store-carry-forward” transmission mechanism. Since VDTNs exhibit social attributes, this paper first investigates the opportunistic transmission problem in the presence of selfish nodes. Then, by enabling node cooperation, this paper proposes an opportunistic transmission scheme for integrated UAV–ICV networks. To address the issue of node selfishness in practical scenarios, the proposed scheme classifies the degree of cooperation and analyzes the encounter probability between nodes. Based on this, information is initially flooded, and the UAV is selected for data distribution by jointly considering the node centrality, energy consumption, and cache size. Finally, simulation results demonstrate that the proposed scheme can effectively improve the delivery ratio and reduce the average delivery delay compared to state-of-the-art schemes. Full article

This study explores the potential of heterogeneous hybrid Free Space Optical (FSO) and Radio Frequency (RF) cognitive networks, which feature both cooperative and economic systems. The cooperative system is defined as a heterogeneous network where the hybrid FSO/RF node possesses dedicated RF resources and shares these resources to create additional transmission opportunities. In contrast, the low-cost economic system consists of a heterogeneous network where only an RF node has RF resources, and the hybrid node shares these resources. We provide a comprehensive analysis for each system, employing stay-and-switch (SAS) and simultaneous multipacket transmission (SMT) methods to ensure a thorough understanding of its performance. As a performance measure, we investigate the stability region of the proposed cognitive and economic systems and devise a reference system without cognitive capability for comparison. Numerical evaluations indicate that the cooperative system using SMT typically outperforms the reference system, increasing stability throughput by up to 52%. However, this advantage diminishes when SAS is used or in rainy conditions. The economic model shows performance levels comparable to the reference model, particularly when incoming traffic is low and when SAS is implemented in clear or hazy environments. Full article

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal–interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low. Full article

Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, we propose a novel single-site localization method tailored for complex multipath NLOS environments, leveraging only angle-of-arrival (AOA) estimates in conjunction with a ray-tracing (RT) algorithm. The method transforms NLOS paths into equivalent line-of-sight (LOS) paths through the generation of generalized sources (GSs) via ray tracing. A novel weighting mechanism for GSs is introduced, which, when combined with an iteratively reweighted least squares (IRLS) estimator, significantly improves the localization accuracy of non-cooperative target sources. Furthermore, a multipath similarity displacement matrix (MSDM) is incorporated to enhance accuracy in regions with pronounced multipath fluctuations. Simulation results validate the efficacy of the proposed algorithm, achieving localization performance that approaches the Cramér–Rao lower bound (CRLB), even in challenging NLOS scenarios. Full article