Wi-Fi has become a fundamental wireless communication technology, supporting a wide range of applications while continuing to evolve to meet increasingly demanding use cases. FutureWi-Fi networks are expected to support industrial and time-sensitive applications that require low-latency and highly reliable communication, thereby driving the need for further advancements in wireless communication technologies. Whereas earlier Wi-Fi standards primarily focused on increasing data rates, recent generations emphasize efficient resource allocation and performance under stringent latency requirements. Multi-Connectivity (MC) has emerged as a promising approach to bridge the gap between the capabilities of current Wi-Fi standards and the communication requirements of industrial and real-time applications. This survey provides a comprehensive, mechanism-centric review of MC in the IEEE 802.11 family, with particular emphasis on time-sensitive communication. We systematically classify and analyze the literature according to the three canonical MC scheduling strategies: Load Balancing (LB), Packet Duplication (PD), and Packet Splitting (PS), and discuss their evolution toward more adaptive and intelligent scheduling frameworks for emerging wireless networks. Furthermore, we provide a comparative analysis of existing scheduling approaches, highlighting the fundamental trade-offs among reliability, latency, spectral efficiency, resource utilization, and implementation complexity. Finally, we discuss the key research challenges and future directions for enabling deterministic and scalable MC in next-generation Wi-Fi systems, with emphasis on bounded-latency communication, adaptive scheduling, and practical deployment considerations. These insights provide a foundation for the design of robust, intelligent, and time-sensitive wireless communication systems.

Emergency vehicles must reach accident locations as quickly as possible, yet the shortest route can be suboptimal in urban rush-hour traffic. While V2X-based pre-emption of traffic at intersections can reduce delays, heavy congestion may still prevent an effective passage. We propose a concept that leverages roadside units (RSUs) equipped with the Collective Perception Service to continuously monitor traffic conditions at intersections to support rerouting decisions for emergency vehicles. Our concept defines and compares three scenarios: (i) no V2X, where the emergency vehicle follows the shortest route; (ii) V2X-enabled pre-emption using Signal Request Messages to obtain a green wave at signalized intersections; and (iii) preemption combined with RSU-assisted collective perception, where RSUs broadcast Collective Perception Messages that enable the emergency vehicle to periodically re-evaluate its route based on observed traffic states. Our preliminary results already prove that the shortest route is not always the fastest one.

Industrial Internet of Things (IIoT) applications demand predictable low latency and high reliability, yet wireless performance often degrades in dense and interference-prone factory environments. While Wi-Fi has traditionally struggled to meet the requirements of time-sensitive industrial traffic, recent Wi-Fi 6 improvements and the introduction of fundamentally new capabilities in Wi-Fi 7, such as multi-link communication and enhanced spectrum management, promise to improve determinism in harsh industrial settings. However, it remains unclear whether these innovations can deliver the latency predictability and reliability required by time-sensitive IIoT applications under realistic industrial interference and coexistence conditions. This work experimentally compares Wi-Fi 6 and Wi-Fi 7 on a real testbed across three representative scenarios: baseline traffic, cross-traffic interference, and coexistence with legacy Wi-Fi 5 devices. One-way latency, reliability, and Consecutive Latency Exceedance (CLE) are evaluated as indicators of industrial communication quality. Across all scenarios, Wi-Fi 7 consistently delivers lower latency, fewer delay bursts, and higher reliability than Wi-Fi 6, with these benefits becoming increasingly evident under heavy load and interference conditions. These improvements stem from Multi-Link Operation (MLO) and fine-grained Resource Unit (RU) allocations, which enhance channel utilization and improve resilience to congestion. Even under legacy coexistence conditions, Wi-Fi 7 maintains stable performance and effectively mitigates contention-induced degradation. These findings demonstrate that Wi-Fi 7 provides clear and consistent benefits for time-critical IIoT applications, positioning it as a strong candidate for future low-latency and scalable wireless deployments.

Reliable and low-latency wireless communication is a key requirement for industrial Internet of Things (IIoT) applications. Packet duplication (PD) is a well-known technique to improve reliability and latency by transmitting redundant packets, but it is often considered inefficient due to its overhead. In this paper, we investigate a selective packet duplication strategy tailored to periodic IIoT communication over Wi-Fi networks. Based on a detailed analysis of packet transmission behavior in industrial Wi-Fi scenarios, we observe that most packets are successfully delivered within the required deadlines on the first transmission attempt, while only a small fraction contributes to latency violations. Leveraging this insight, we propose a MAC-layer selective PD mechanism that activates duplication only after failed transmission attempts, using standard Wi-Fi metrics without requiring hardware-specific channel information. We evaluate the proposed approach through system-level simulations in ns-3. The results show that selective PD significantly improves latency–reliability performance, achieving up to 99.999 % reliability within a 5 ms deadline, while introducing only negligible additional network load compared to single-connectivity operation. These findings demonstrate that selective packet duplication is an effective and practical solution for enhancing industrial Wi-Fi communication.

Resilient network design is a mandatory feature for future wireless network infrastructure. When links in mesh networks fail, multi-connectivity and multi-path transmissions can establish the required end-to-end availability. In this work, we investigate a general wireless network graph with dependent link failures. We consider both packet duplication and load balancing strategies, as well as resource allocation across various network topologies with different levels of multi-connectivity. Our approach is based on analytical lower and upper bounds on network connectivity to compute the overall reliability, complemented by numerical simulations to illustrate the results. By modeling the entire resilience cycle from the event, through survival, to the recovery phase, different tradeoffs are characterized: redundancy versus efficiency, dependencies, data rates, and network topologies. Finally, we present a method for identifying the optimal link configuration to achieve the desired network performance.

Increasing the reliability of multihop industrial wireless sensor networks (IWSNs) is an important challenge to enable continuous data collection in industrial processes. One approach for increasing reliability is multiconnectivity (MC), which enables simultaneous data transmission through two independent network links. In practice, MC can be implemented on all OSI layers, resulting in different parts of the network stack being duplicated. In this article, we compare the implementation of MC above the MAC layer and above the network layer, corresponding to an implementation on a per-hop basis or an end-to-end basis in multihop networks. For a comprehensive consideration, the approaches are discussed analytically, simulated and deployed in a real-world scenario to provide foundational knowledge about the effects of different layer MC on reliability. We show that the per-hop approach offers higher reliability than the end-to-end approach in homogeneous networks, while both approaches outperform the single-connectivity baseline. In comparison, the end-to-end approach outperforms the per-hop approach and the baseline depending on the network topology in heterogeneous networks. Apart from the topology and the link quality, we reveal the influence of fragmentation on both approaches. All influencing factors, in combination with external factors introduced by a real-world deployment, led to a decreased data loss rate of up to 56% compared to the single-connectivity baseline.

Traditional Linux network stacks struggle to handle the high packet rates of modern Network Interface Cards (NICs), pushing high-performance users towards kernel bypass solutions. But kernel bypass solutions introduce compatibility issues requiring exclusive access to network interfaces, lacking access to privileged instructions, and breaking existing application compatibility. To partially address these issues, Linux introduced eXpress Data Path (XDP), enabling programmable, efficient, user-space packet processing but still failing to provide compatibility with traditional socket-based applications.We propose the XDP Packet Mill, a framework combining the performance benefits of XDP socket with seamless compatibility for existing applications. The Mill intercepts and processes packets via XDP, and it offers efficient, zero-copy primitives to drop, modify, forward, duplicate, slice, and merge layer-2 packets. This enables practical experimentation and deployment of advanced networking protocols, such as multi-path communication, without requiring non-standard application interfaces. Our evaluation shows the throughput characteristics of Mill in different XDP modes (native, SKB, zero-copy) in a realistic high-performance Ethernet setup. Mill aims to simplify the real-world deployment and experimentation of flexible networking protocols while preserving application compatibility.

Vehicle-to-X (V2X) communication has become crucial for enhancing road safety, especially for Vulnerable Road Users (VRUs) such as pedestrians and cyclists. However, the increasing number of devices communicating on the same channels will lead to significant channel load. To address this issue this study evaluates the effectiveness of Redundancy Mitigation (RM) for VRU Awareness Messages (VAMs), focusing specifically on cyclists. The objective of RM is to minimize the transmission of redundant information. We conducted a simulation study using a urban scenario with a high bicycle density based on traffic data from Hannover, Germany. This study assessed the impact of RM on channel load, measured by Channel Busy Ratio (CBR), and safety, measured by VRU Perception Rate (VPR) in simulation. To evaluate the accuracy and reliability of the RM mechanisms, we analyzed the actual differences in position, speed, and heading between the ego VRU and the VRU, which was assumed to be redundant. Our findings indicate that while RM can reduce channel congestion, it also leads to a decrease in the perception rate of VRUs. The analysis of actual differences revealed that the RM mechanism standardized by ETSI often uses outdated information, leading to significant discrepancies in position, speed, and heading, which could result in dangerous situations. To address these limitations, we propose an adapted RM mechanism that improves the balance between reducing channel load and maintaining VRU awareness. The adapted approach shows a significant reduction in maximum CBR and a less significant decrease in VPR compared to the standardized RM. Moreover, it demonstrates better performance in the actual differences in position, speed, and heading, thereby enhancing overall safety. Our results highlight the need for further research to optimize RM techniques and ensure they effectively enhance V2X communication without compromising the safety of VRUs.

Road traffic simulations are crucial for establishing safe and efficient traffic environments. They are used to test various road applications before real-world implementation. SUMO (Simulation of Urban MObility) is a well-known simulator for road networks and intermodal traffic, often used in conjunction with other tools to test various types of applications. Realistic simulations require accurate movement models for different road users, such as cars, bicycles, and buses. While realistic models are already implemented for most vehicle types, bicycles, which are essential for achieving safe and efficient traffic, can only be modeled as slow vehicles or fast pedestrians at present. This paper introduces the Realistic Bicycle Dynamics Model (RBDM), the first dedicated bicycle model for SUMO, addressing this significant gap. Leveraging real-world bicycle data from the SimRa dataset, the RBDM implements realistic speed, acceleration, and deceleration behaviors of bicycles in urban scenarios. The evaluation is conducted using the Monaco SUMO traffic scenario (MoST) and a newly generated Berlin scenario in SUMO. The RBDM significantly outperforms the existing slow-vehicle approximation in SUMO, aligning more closely with real-world data across distribution, median, and interquartile range metrics. These results underscore the necessity of a realistic bicycle movement model for accurate simulations, given the significant differences in the movement profiles of bicycles, cars, and pedestrians. Furthermore, the model is tested for its ability to generalize to disparate scenarios and urban topologies, which is dependent on the manner and geographical region in which the SimRa data were gathered. In addition, recommendations are provided for how it could be adapted for use in different city topologies. The enhanced realism of the RBDM is essential for accurately simulating and evaluating applications such as Vehile-to-X communication and urban planning systems that depend on precise bicycle movement representation.

Node and link failures in harsh industrial environments diminish the reliability of multi-hop wireless networks. Multi-Connectivity offers a solution by leveraging multiple communication paths and exploiting path diversity. However, existing MC approaches often lack efficient multi-path and multi-radio path selection in multi-hop scenarios. Therefore, this work proposes an innovative network layer MC routing protocol that combines multi-path and multi-radio selection using a dynamic source routing-inspired mechanism and link-transparent routing based on Node IDs. This decoupling of routing from link-layer addresses enables diverse end-to-end paths while delegating perhop radio interface selection to individual nodes for local adaptation. Numerical simulations demonstrate improved reliability in the handling of node and link failures compared to single-connectivity routing, with minimal routing overhead in arbitrary network topologies.

The growing demand for highly reliable, low-latency communication in industrial and real-time applications poses significant challenges for traditional Wi-Fi networks, especially under dynamic traffic conditions. Multi-Connectivity (MC) presents a viable solution to enhance network performance by utilizing multiple transmission paths. This paper extends our previous work where we proposed Channel Capacity Index-Aware Adaptive Scheduler (CIAS), and evaluates static and adaptive scheduling, i.e., selecting which paths are used, on latency and reliability in non-periodic, sporadic, and attenuated scenarios, which are commonly observed in autonomous systems and industrial applications. We analyze the performance of static schedulers and compare them with CIAS, an adaptive cross-layer scheduling approach that dynamically selects the optimal MC scheme based on channel capacity. Our results demonstrate that CIAS consistently reduces latency and mitigates packet delays, particularly under high traffic loads, sporadic transmissions, and signal attenuation. Furthermore, CIAS enhances reliability by minimizing tail-end latencies and adapting to channel fluctuations more effectively than static schedulers. This study underscores the critical role of adaptive MC scheduling in improving wireless communication efficiency for time-sensitive applications that demand both low latency and high reliability.

As manufacturing digitalizes, reliable industrial communication systems are essential for managing increased data volumes and ensuring secure, efficient production. These systems must maintain high productivity and economic performance despite fluctuating latency, bandwidth, and stability. Resilient design supports system functionality during information exchange failures. Resilience can be intrinsic, achieved through flexible network architectures, or extrinsic, achieved by the production system's adaptability to the communication environment. Data-flexible production systems may influence production schedules and performance indicators while adapting to the communication environment, altering data output and hotspot distribution. Quantitative evaluation of this impact is necessary for better decision-making. As a result of the collaborative project on resilient communication systems for secure and flexible production systems (RePro) extrinsic resilience is defined. Resilience mechanisms and their impact on energy and resource efficiency, quality, safety, productivity, and cost in production systems are discussed and exemplarily two extrinsic resilience mechanisms are selected to demonstrate the framework.

Modern Wi-Fi systems are facing increasing demands due to the rise of advanced industrial real-time applications. These applications require Wi-Fi networks to deliver low latency and high reliability. Current Wi-Fi solutions often struggle to meet these stringent performance requirements. Multi-Connectivity (MC) presents a promising approach to address these challenges by using multiple communication paths to enhance the overall network performance. In this paper, we propose the Channel Capacity Index-Aware Adaptive Scheduler (CIAS), a novel application-defined MC scheduler for wireless communication systems, and examine the performance of static, dynamic, and adaptive MC scheduling schemes under various network conditions. In the baseline scenario, CIAS significantly reduces one-way latency in medium to high load by dynamically selecting the best scheduling strategy based on the Channel Capacity Index (CCI). Overall, the study underscores the importance of adaptive MC scheduling for time-sensitive applications and highlights the potential of CIAS to improve scheduling efficiency under varying load conditions.

In recent years, Multi-Connectivity (MC) has emerged as a promising approach to enhancing the reliability and resilience of communication networks. The effectiveness of MC depends on the statistical dependencies between the links and heavily on the selection of an appropriate MC strategy. We introduce Packet Duplication (PD) and Load Balancing (LB) in this paper. Each of these strategies offers distinct advantages, but a comprehensive comparison under varying network conditions and dependencies is missing. To address this gap, we investigate an idealized diamond network element, applying two strategies. Analytical results are provided, highlighting the mathematical relationship between effective rate and reliability, considering worst-, best-, and independent and identically distributed (i.i.d.)-dependencies between links. Numerical simulations demonstrate the dichotomy of the optimal strategy under different network scenarios. Interestingly, LB outperforms PD for the worst-case. Additionally, we extend our analysis by deriving reliability bounds as functions of the effective rate for arbitrary network structures in both PD and LB.

The adoption of wearable sensor platforms is expanding beyond personal health tracking towards workplace environments. While such systems are commonly used in hazardous work scenarios, continuous monitoring of environmental factors in everyday workplaces remains limited. This paper introduces a human-centered wearable sensor node platform to monitor workplace conditions such as air quality and particulate matter concentration. We present the architecture and real-world evaluation of a platform tailored for non-life-threatening working conditions, with a strong focus on the human worker. We investigate the platform's impact, user perceptions, and potential concerns through a week-long field study and qualitative user interviews. Our findings discuss the platform's technical aspects and highlight challenges related to data management, privacy, and user well-being. This ensures that technological advancements align with worker needs and expectations.

In recent years, Multi-Connectivity (MC) has been identified as a promising candidate for enabling reliable lowlatency communication. While multiple theoretical and numerical works discuss the benefits of these techniques, only a few publications describe experimental systems, especially for network layer MC. To close this gap, this paper presents Networking in userspace to enable Multi-Connectivity (NuMuC), a program that implements network layer MC by handling packets inside the userspace. This program allows the implementation of various MC approaches while providing higher flexibility compared to kernel modifications. We give an overview of the architecture of NuMuC and demonstrate its functional capability by using packet duplication on wireless links to increase the resilience of a transmission.

The invasive insect brown marmorated stink bug (BMSB) is an emerging pest of global importance, as it is destroying fruits and seeds, having caused estimated damages of € 588 million to crops in 2019 in Northern Italy alone. An open challenge is to improve monitoring of BMSB in order to be able to deploy countermeasures more efficiently and to increase consumer confidence in the end product. The Horizon 2020 Haly.ID project seeks to reduce or eliminate dependence on conventional monitoring tools and practices, such as traps, baits, visual inspections, sweep netting, and tree beating. In their place, the project proposes the use of unmanned aerial vehicle (UAV) and Internet of Things (IoT) solutions for monitoring the insect population and investigates novel methods for enhancing the quality of fruit in the market. In this work, we focus on the novel autonomous IoT insect monitoring system consisting of multiple innovative solutions for BMSB monitoring and trusted data management developed in Haly.ID . In particular, this article describes the challenges faced when integrating and deploying this monitoring system consisting of those different parts and aims at presenting valuable “lessons learned” for the realization of future deployments. We show that massive over-provisioning of power supply and network speed allows to adapt the system at run-time reflecting changing project requirements, and to conduct experiments remotely. At the same time, over-provisioning introduces new weak points impacting the system reliability, such as cables that can be unplugged or damaged.

In Europe, the C-ITS Security Certificate Management System supplies pseudonymous credentials to valid stations, allowing them to authenticate each other when exchanging messages. Detecting misbehavior and evicting offenders from the C-ITS is challenging but must be addressed to maintain security and safety. The offending station must be reported and found guilty by the Misbehavior Authority (MA) in order to revoke its credentials and protect other stations. Collecting necessary evidence is hindered by privacy requirements, allowing potential attackers to use pseudonyms to obfuscate the source of misbehavior. Privacy-Preserving Misbehavior Reporting (PriMiRe) enables the MA to aggregate and analyze evidence for unique senders without revealing their real or pseudonymous identities, thus maintaining privacy requirements. It is shown that PriMiRe requires minor changes to the existing architecture and protocols, and additional data storage requirements are modest.