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Industry 4.0 is being enabled by a number of new wireless technologies that emerged in the last decade, aiming to ultimately alleviate the need for wires in industrial use cases. However, wireless solutions are still neither as reliable nor as fast as their wired counterparts. Closed loop communication, a representative industrial communication sce...
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Context 1
... with RAW for slow control loops with low traffic demands, but fast control loops with cycle times below 100 ms are not a suitable candidate for TWT given the overheads and negotiation delays. Without TWT, the AP must first indicate the presence of DL traffic in a beacon to ensure the slave will be awake to receive the DL data. As illustrated in Fig. 2, this can introduce substantial delay in downlink because the slaves need to wait for the next beacon to be notified that they have pending DL data, and then wait for their RAW slot after the next beacon to retrieve their DL data. This makes the CLs with cycle times shorter than the beacon interval infeasible with IEEE 802.11ah without ...
Context 2
... with RAW for slow control loops with low traffic demands, but fast control loops with cycle times below 100 ms are not a suitable candidate for TWT given the overheads and negotiation delays. Without TWT, the AP must first indicate the presence of DL traffic in a beacon to ensure the slave will be awake to receive the DL data. As illustrated in Fig. 2, this can introduce substantial delay in downlink because the slaves need to wait for the next beacon to be notified that they have pending DL data, and then wait for their RAW slot after the next beacon to retrieve their DL data. This makes the CLs with cycle times shorter than the beacon interval infeasible with IEEE 802.11ah without ...
Citations
... A similar issue is observed in the Restricted Access Window (RAW) mechanism, introduced in the 802.11ah amendment and studied in [17][18][19][20][21]. The RAW creates time intervals during which only a predefined group of stations can transmit data, while others are forbidden to access the channel. ...
To provide limited delays for remote sensing and control, gaming, and virtual reality applications, the Wi-Fi 7 standard introduces the Restricted Target Wake Time (R-TWT) mechanism, which reserves time intervals for particular stations with such real-time traffic. As legacy stations do not support R-TWT, the access point forbids channel access during these intervals for legacy stations. Quiet Intervals have been announced for this purpose. Since the support for the Quieting Framework can be configured as mandatory in some networks, Quiet Intervals are assumed to be valid protection for R-TWT. The paper describes experimental results with mass-market devices that disprove this assumption. The paper reveals significant inconsistencies between the standard and widely used devices, e.g., the inability to schedule multiple Quiet Intervals. It will be a significant problem for Wi-Fi 7 devices using R-TWT in heterogeneous networks with legacy devices and will require much effort from academia and industry to solve.
... Authors of Wang et al. (2016) had optimized the energy efficiency and delay by proposing an algorithm with the estimation of number of slots and its duration. Authors of Seferagić et al. (2021) proposed a nonlinear programming problem by optimizing RAW configuration in closed loop communication. Without further contention, the authors of Ahmed and Hussain (2020) predict the service interval and planning of subsequent frames. ...
IEEE 802.11ah, known as Wi-Fi HaLow standard, specifically promoted for next-generation Internet of things (IoT) applications. Restricted access window (RAW) mechanism is introduced in IEEE 802.11ah medium access control (MAC) layer. The RAW mechanism reduces the collisions among contending devices by partitioning RAW period into RAW sots and allocates a RAW slot to each group. Thus, the specific group of devices alone contend for channel access in the respective RAW slot. In this paper, we develop an accurate analytical model to compute the throughput and energy efficiency of IEEE 802.11ah with the RAW mechanism. In IEEE 802.11ah, the choice of optimal number of RAW slots is an open research problem. Thus, we present a gated recurrent unit (GRU) based deep learning-recurrent neural network (DL-RNN) to estimate the optimal RAW slots that improves the performance of IEEE 802.11ah for dense IoT networks. From the results, we observe that throughput and energy efficiency performance are significantly improved by using optimal number of RAW slots obtained using GRU. The analytical works conducted are validated with extensive simulation works.
... The requirements toward the underlying IEEE 802.11ah networks are expected to include supporting a large number of power-constrained stations over a long transmission range, where these messages are small and sent infrequently with a non-critical delay [7]. Adversely, IEEE 802.11ah is also envisioned as an enabler in the context of Smart Industries [8], primarily as a replacement of wired infrastructures. IEEE 802.11ah will in this context serve for supporting time-constrained control loops, which find their utilization in applications such as connected lighting or communication with mobile infrastructures such as drones, robots, cranes, etc. ...
... IEEE 802.11ah will in this context serve for supporting time-constrained control loops, which find their utilization in applications such as connected lighting or communication with mobile infrastructures such as drones, robots, cranes, etc. In such scenarios, the most stringent application requirement for IEEE 802.11ah is the short communication delay in the sub-second range [8]. Finally, IEEE 802.11ah is envisioned as an enabler in large IoT contexts, where it will serve as a "hotspot" for high data-rate communication tasks such as firmware updates or data offloading, while another technology with a longer range is expected to provide basic connectivity. ...
Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism.
... Moreover, it is worth noting that latency for technologies like BLE or UWB is conditioned by the selected beaconing intervals. Nonetheless, in the case of Wi-Fi HaLow, there is recent research that mitigates the mentioned dependency on beaconing intervals by adjusting the Restricted Access Window (RAW), a configurable medium access feature of IEEE 802.11ah [167]. ...
Industry 5.0 follows the steps of the Industry 4.0 paradigm and seeks for revolutionizing the way industries operate. In fact, Industry 5.0 focuses on research and innovation to support industrial production sustainability and place the well-being of industrial workers at the center of the production process. Thus, Industry 5.0 relies on three pillars: it is human-centric, it encourages sustainability and it is aimed at developing resilience against disruptions. Such core aspects cannot be fully achieved without a transparent end-to-end human-centered traceability throughout the value chain. As a consequence, Auto-Identification (Auto-ID) technologies play a key role, since they are able to provide automated item recognition, positioning and tracking without human intervention or in cooperation with industrial operators. Although the most popular Auto-ID technologies provide a certain degree of security and productivity, there are still open challenges for future Industry 5.0 factories. This article analyzes and evaluates the Auto-ID landscape and delivers a holistic perspective and understanding of the most popular and the latest technologies, looking for solutions that cope with harsh, diverse and complex industrial scenarios. In addition, it describes a methodology for selecting Auto-ID technologies for Industry 5.0 factories. Such a methodology is applied to a specific use case of the shipbuilding industry that requires identifying the main components of a ship during its construction and repair. To validate the outcomes of the methodology, a practical evaluation of passive and active UHF RFID tags was performed in an Offshore Patrol Vessel (OPV) under construction, showing that a careful selection and evaluation of the tags enables product identification and tracking even in areas with a very high density of metallic objects. As a result, this article serves as a useful guide for industrial stakeholders, including future developers and managers that seek for deploying identification and traceability technologies in Industry 5.0 scenarios.
The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.
This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.
