SearcharxivSearch

arXiv subjects

Jobish John

Publications and source records attributed to Jobish John.

8 recordsLinked to original sources

Discrete Time Credit-Based Shaping for Time-Sensitive Applications in 5G/6G Networks

Future wireless networks must deliver deterministic end-to-end delays for workloads such as smart-factory control loops. On Ethernet these guarantees are delivered by the set of tools within IEEE 802.1 time sensitive networking~(TSN) standards. Credit-based shaper (CBS) is one such tool which enforces bounded latency. Directly porting CBS to 5G/6G New Radio (NR) is non-trivial because NR schedules traffic in discrete-time, modulation-dependent resource allocation, whereas CBS assumes a continuous, fixed-rate link. Existing TSN-over-5G translators map Ethernet priorities to 5G quality of service (QoS) identifiers but leave the radio scheduler unchanged, so deterministic delay is lost within the radio access network (RAN). To address this challenge, we propose a novel slot-native approach that adapts CBS to operate natively in discrete NR slots. We first propose a per-slot credit formulation for each user-equipment ({UE}) queue that debits credit by the granted transport block size~(TBS); we call this discrete-time CBS (CBS-DT). Recognizing that debiting the full {TBS} can unduly penalize transmissions that actually use only part of their grant, we then introduce and analyze {CBS} with Partial Usage ({CBS-PU}). {CBS-PU} scales the credit debit in proportion to the actual bytes dequeued from the downlink queue. The resulting CBS-PU algorithm is shown to maintain bounded credit, preserve long-term rate reservations, and guarantees worst-case delay performance no worse than {CBS-DT}. Simulation results show that slot-level credit gating--particularly CBS-PU--enables NR to export TSN class QoS while maximizing resource utilization.

cs.NI

Reliability Modeling for Beyond-5G Mission Critical Networks Using Effective Capacity

Accurate reliability modeling for ultra-reliable low latency communication (URLLC) and hyper-reliable low latency communication (HRLLC) networks is challenging due to the complex interactions between network layers required to meet stringent requirements. In this paper, we propose such a model. We consider the acknowledged mode of the radio link control (RLC) layer, utilizing separate buffers for transmissions and retransmissions, along with the behavior of physical channels. Our approach leverages the effective capacity (EC) framework, which quantifies the maximum constant arrival rate a time-varying wireless channel can support while meeting statistical quality of service (QoS) constraints. We derive a reliability model that incorporates delay violations, various latency components, and multiple transmission attempts. Our method identifies optimal operating conditions that satisfy URLLC/HRLLC constraints while maintaining near-optimal EC, ensuring the system can handle peak traffic with a guaranteed QoS. Our model reveals critical trade-offs between EC and reliability across various use cases, providing guidance for URLLC/HRLLC network design for service providers and system designers.

cs.NI

A Comprehensive Survey of Wireless Time-Sensitive Networking (TSN): Architecture, Technologies, Applications, and Open Issues

Time-sensitive networking (TSN) is expected to be a key component of critical machine-type communication networks in areas such as Industry 4.0, robotics and autonomous vehicles. With rising mobility requirements in industrial applications and the prevalence of wireless networks, wireless network integration into TSN is becoming increasingly important. This survey article presents a comprehensive review of the current literature on wireless TSN, including an overview of the architecture of a wireless TSN network and an examination of the various wireless technologies and protocols that can be or are used in such networks. In addition, the article discusses industrial applications of wireless TSN, among them industrial automation, robotics, and autonomous vehicles. The article concludes by summarizing the challenges and open issues related to the integration of TSN into wireless networks, and by offering suggestions for future research directions.

cs.NI

Industry 4.0 and Beyond: The Role of 5G, WiFi 7, and TSN in Enabling Smart Manufacturing

This paper explores the role that 5G, WiFi-7, and Time-Sensitive Networking (TSN) can play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. The paper provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, the paper includes a comparative study of these technologies, analyzing them based on a number of industrial use-cases, supported secondary applications, industry adoption, and current market trends. The paper concludes by highlighting the challenges and future directions for the adoption of these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.

cs.NI

Wireless Communications for Smart Manufacturing and Industrial IoT: Existing Technologies, 5G, and Beyond

Smart manufacturing is a vision and major driver for change in industrial environments. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communications protocols that they rely on. This paper presents an extensive review of wireless machine to machine communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research.

cs.NI

Towards Industry 5.0: Intelligent Reflecting Surface (IRS) in Smart Manufacturing

Industry 5.0 envisions close cooperation between humans and machines requiring ultra-reliable and low latency communications (URLLC). The Intelligent Reflecting Surface (IRS) has the potential to play a crucial role in realizing wireless URLLC for Industry 5.0. IRS is forecast to be a key enabler of 6G wireless communication networks as it can significantly improve wireless network performance by creating a controllable radio environment. In this paper, we first provide an overview of IRS technology and then conceptualize the potential for IRS implementation in a future smart manufacturing environment to support the emergence of Industry 5.0 with a series of applications. Finally, to stimulate future research in this area, we discuss the strength, open challenges, and opportunities of IRS technology in modern smart manufacturing.

cs.NI

Maximum Lifetime Convergecast Tree in Wireless Sensor Networks

We study the problem of building a maximum lifetime data collection tree for periodic convergecast applications in wireless sensor networks. We experimentally observe that if two nodes transmit same number of data packets, the amount of energy consumption of the nodes is approximately the same even if the payload lengths of the transmitted packets are different. This is because the major energy consumption during a packet transmission arises from radio start-up and medium access control overhead. Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets transmitted/ received, in contrast to prior works, which consider the number of data units (amount of sensor data generated by a node) transmitted/ received. Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work. We prove that this problem is NP-complete and propose an algorithm to solve it. The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree (SPT) and random data collection tree (RDCT) algorithms. The energy consumption of different nodes under the proposed algorithm are shown to be more balanced than under SPT and RDCT algorithms. Also, the performance of the proposed algorithm in large networks is studied through simulations and is compared with those of the state-of-the-art RaSMaLai algorithm, SPT, minimum spanning tree, and RDCT based data collection schemes. Our simulations show that the proposed algorithm provides a significantly higher network lifetime compared to all the other considered data collection approaches.

cs.NI

Design and Implementation of a Wireless SensorNetwork for Agricultural Applications

We present the design and implementation of a shortest path tree based, energy efficient data collection wireless sensor network to sense various parameters in an agricultural farm using in-house developed low cost sensors. Nodes follow a synchronized, periodic sleep-wake up schedule to maximize the lifetime of the network. The implemented network consists of 24 sensor nodes in a 3 acre maize farm and its performance is captured by 7 snooper nodes for different data collection intervals: 10 minutes, 1 hour and 3 hours. The almost static nature of wireless links in the farm motivated us to use the same tree for a long data collection period(3 days). The imbalance in energy consumption across nodes is observed to be very small and the network architecture uses easy-to-implement protocols to perform different network activities including handling of node failures. We present the results and analysis of extensive tests conducted on our implementation, which provide significant insights.

cs.NI