Low Latency Deterministic Network Research Articles

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study.

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.

A Pragmatic Relay Placement Approach in 3-D Space and Q-Learning-Based Transmission Scheme for Reliable Factory Automation Applications

The biggest challenge in adopting industrial wireless sensor networks (IWSNs) for factory automation applications is to provide low latency and highly reliable communication in harsh factory environments. IEEE 802.15.4e low latency deterministic network (LLDN) mode attempts to address this requirement at the medium access control (MAC) layer. However, the measures offered by this mode are inadequate considering realistic factory environments, suffering from noise, interference, multipath fading, and resulting in frequent packet losses. Cooperative diversity using relay nodes and incorporation of forward error correction (FEC) techniques are the two conventional ways to enhance communication reliability. However, the challenge lies in the placement of relay nodes considering a realistic three-dimensional (3-D) factory space and satisfying various physical, performance, and energy-related constraints. Moreover, the versatile and dynamic behavior of factory environment demand that the solutions offered to enhance communication reliability must be generic and adaptive, thereby eliminating the need for unnecessary redesigns. This paper proposes a twofold solution to enhance the communication reliability offered by 802.15.4e LLDN. First, an efficient and pragmatic relay-placement strategy based on rainbow product ranking algorithm for a 3-D factory space. Second, an adaptive transmission scheme (ATS) inspired from reinforcement learning (RL) technique called Q-learning is proposed, which incorporates cooperative diversity and Reed Solomon (RS) block codes. The effectiveness of the proposed solution is established and demonstrated using a real-world case study.

Dynamic Priority Based Reliable Real-Time Communications for Infrastructure-Less Networks

This paper proposes a dynamic priority system at medium access control (MAC) layer to schedule time sensitive and critical communications in infrastructure-less wireless networks. Two schemes, priority enabled MAC (PE-MAC) and optimized PE-MAC are proposed to ensure real-time and reliable data delivery in emergency and feedback systems. These schemes use a dynamic priority mechanism to offer improved network reliability and timely communication for critical nodes. Both schemes offer a notable improvement in comparison to the IEEE 802.15.4e low-latency deterministic networks. To ensure more predictable communication reliability, two reliability centric schemes, quality-ensured scheme (QES) and priority integrated QES, are also proposed. These schemes maintain a pre-specified successful packet delivery rate, hence improving the overall network reliability and guaranteed channel access.

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks.

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability.

Relayer-Enabled Retransmission Scheduling in 802.15.4e LLDN—Exploring a Reinforcement Learning Approach

We consider the scheduling of retransmissions in the low-latency deterministic network (LLDN) extension to the IEEE 802.15.4 standard. We propose a number of retransmission schemes with varying degrees of required changes to the LLDN specification. In particular, we propose a retransmission scheme that uses cooperative relayers and where the best relayer for a source node is learned using a reinforcement-learning method. The method allows for adapting relayer selections in the face of time-varying channels. Our results show that the relayer-based methods achieve a much better reliability over the other methods, both over static (but unknown) and over time-varying channels.

Novel MAC Layer Proposal for URLLC in Industrial Wireless Sensor Networks

Ultra⁃reliable and low⁃latency communications (URLLC) has become a fundamental focus of future industrial wireless sensor networks (IWSNs). With the evolution of automation and process control in industrial environments, the need for increased reliability and reduced latencies in wireless communications is even pronounced. Furthermore, the 5G systems specifically target the URLLC in selected areas and industrial automation might turn into a suitable venue for future IWSNs, running 5G as a high speed inter⁃process linking technology. In this paper, a hybrid multi⁃channel scheme for performance and throughput enhancement of IWSNs is proposed. The scheme utilizes the multiple frequency channels to increase the overall throughput of the system along with the increase in reliability. A special purpose frequency channel is defined, which facilitates the failed communications by retransmissions where the retransmission slots are allocated according to the priority level of failed communications of different nodes. A scheduler is used to formulate priority based scheduling for retransmission in TDMA based communication slots of this channel. Furthermore, in carrier⁃sense multiple access with collision avoidance (CSMA/CA) based slots, a frequency polling is introduced to limit the collisions. Mathematical modelling for performance metrics is also presented. The performance of the proposed scheme is compared with that of IEEE802.15.4e, where the performance is evaluated on the basis of throughput, reliability and the number of nodes accommodated in a cluster. The proposed scheme offers a notable increase in the reliability and throughput over the existing IEEE802.15.4e Low Latency Deterministic Networks (LLDN) standard.

A Priority-Aware Multichannel Adaptive Framework for the IEEE 802.15.4e-LLDN

The low-latency deterministic network (LLDN) protocol defined in the IEEE 802.15.4e amendment is intended for factory automation applications that require very low latency and large networks, such as automotive manufacturing. However, LLDN does not provide priority support to properly deal with real-time traffic or dynamic channel configuration capabilities to cope with unreliable channels. Moreover, it offers a limited scalability, as the cycle time grows linearly with the number of network nodes. This paper proposes the priority-aware multi-channel adaptive (PriMulA) framework, which introduces in the LLDN priority-aware scheduling, multichannel communication, adaptive channel selection, and channel blacklisting. PriMulA supports a higher number of network nodes than the LLDN protocol while keeping short cycle times. In addition, PriMuLA avoids deadline miss and improves the network reliability. It maintains the interoperability with LLDN standard nodes and can be implemented on commercial off-the-shelf devices. This paper presents the framework, a schedulability analysis, comparative simulations, and a proof-of-concept implementation.

Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

Providing reliable services for low-latency (LL) applications within the internet of things (IoT) context is a challenging issue. Several wireless sensor network (WSN) applications require deterministic systems that ensure a reliable and LL aggregation service. The IEEE 802.15.4e standard, which is considered as the backbone of the IoT regarding WSN, has presented the LL deterministic network (LLDN) mode that can fulfill the major requirements of LL applications. Meanwhile, several LL applications, for example in the automotive industry, demand the support of sensor node mobility which in turn affects the network performance. Node mobility triggers several dissociations from the network that will increase latency and degrade node throughput. In this paper, we investigate the impact of node mobility over the LLDN mode while defining key factors that maximize latency and degrade throughput. In addition, an enhanced version of the LLDN mode is presented and evaluated that supports node mobility while maintaining the targeted limits of LL application requirements. The proposed mobility aware LLDN technique manages to reduce the dissociation overhead by a factor of 75%, while the packet delivery ratio has been enhanced by 30%. Furthermore, this paper presents an analytical model that provides a snapshot of the tradeoff process between different metrics in the IEEE 802.15.4e LLDN design, which must be considered prior to network deployment in mobile LL applications.

Modeling and performance evaluation of the IEEE 802.15.4e LLDN mechanism designed for industrial applications in WSNs

The IEEE 802.15.4 standard has been introduced for low latency and low energy consumption in wireless sensor networks. To better support the requirements of industrial applications, where the use of this standard is limited, the low latency deterministic network (LLDN) mechanism of the IEEE 802.15.4e amendment has been proposed. In this paper, we develop a three dimensional Markov chain model for the IEEE 802.15.4e LLDN mechanism. Then, we estimate the stationary probability distribution of this chain in order to derive theoretical expressions of some performance metrics, as the reliability, energy consumption, throughput, delay and jitter. After that, we conduct a comparative study between the IEEE 802.15.4e LLDN and the IEEE 802.15.4 slotted carrier sense multiple access with collision avoidance (CSMA/CA). Numerical results show that the deterministic behavior of the LLDN mechanism significantly reduces the collision probability providing best performances in terms of reliability, energy consumption, throughput and delay compared to the IEEE 802.15.4 slotted CSMA/CA. Finally, the accuracy of our theoretical analysis is validated by Monte Carlo simulations.

TDMA-Based IEEE 802.15.4 for Low-Latency Deterministic Control Applications

In this paper, we propose a technique for making IEEE 802.15.4 standard suitable for low-latency deterministic networks for wireless control applications where the cyclic update time of sensor's information is not more than 10 ms. The IEEE 802.15.4 has shown good characteristics for deterministic networks in beacon-enabled mode, but with minimum cyclic update time for personal area network devices not less than 15.36 ms. Moreover, it is unsuitable for hard real-time systems when used in nonbeacon-enabled mode due to the random nature of channel access protocol. The proposed technique employs time-division multiple access (TDMA)-based protocol that works on slightly modified IEEE 802.15.4 in star topology. Each end device in the network transmits its data frame after certain time delay in response to periodic requests from the coordinator. This time delay is optimized for better channel bandwidth utilization and reliable data exchange. While this TDMA-based protocol eliminates the risk of frame collisions to a great extent, medium-access control sublayer modifications reduce the nondeterminism of the network and increase the bandwidth efficiency. A mathematical model is proposed to design and develop a practical communication network with commercial off-the-shelf radios that can predict worst case arrival time of data frames. Experiments were conducted to evaluate the model for the proposed protocol.

Industrial WSN Based on IR-UWB and a Low-Latency MAC Protocol

AbstractWireless sensor networks for industrial communication require high reliability and low latency. As current wireless sensor networks do not entirely meet these requirements, novel system approaches need to be developed. Since ultra wideband communication systems seem to be a promising approach, this paper evaluates the performance of the IEEE 802.15.4 impulse-radio ultra-wideband physical layer and the IEEE 802.15.4 Low Latency Deterministic Network (LLDN) MAC for industrial applications. Novel approaches and system adaptions are proposed to meet the application requirements. In this regard, a synchronization approach based on circular average magnitude difference functions (CAMDF) and on a clean template (CT) is presented for the correlation receiver. An adapted MAC protocol titled aggregated low latency (ALL) MAC is proposed to significantly reduce the resulting latency. Based on the system proposals, a hardware prototype has been developed, which proves the feasibility of the system and visualizes the real-time performance of the MAC protocol.

Energy-efficient and reliable wireless sensor networks - an extension to IEEE 802.15.4e

Collecting sensor data in industrial environments from up to some tenth of battery-powered sensor nodes with sampling rates up to 100 Hz requires energy-aware protocols, which avoid collisions and long listening phases. The IEEE 802.15.4 standard focuses on energy-aware wireless sensor networks (WSNs) and the Task Group 4e has published an amendment to fulfill up to 100 sensor value transmissions per second per sensor node (low latency deterministic network (LLDN) mode) to satisfy demands of factory automation. To improve the reliability of the data collection in the star topology of the LLDN mode, we propose a relay strategy, which can be performed within the LLDN schedule. Furthermore, we propose an extension of the star topology to collect data from two-hop sensor nodes. The proposed retransmission mode enables power savings in the sensor node of more than 33%, while reducing the packet loss by up to 40%. To reach this performance, an optimum spatial distribution is necessary, which is discussed in detail.