Ultra-Reliable Low-Latency Communication (URLLC) and Time-Sensitive Networking (TSN) are essential for enhancing 5G and Wi-Fi 6/7 to support real-time industrial automation. However, our survey shows that existing Medium Access Control (MAC) schemes still face unresolved latency issues. This paper introduces the Transmission Gating Time Hyperchannel (TGT-HC), a novel contention-free Carrier-Sense Multiple Access (CSMA) scheme driven by a per-flow Time-Aware Shaper (TAS) scheduler. Our analytical results, simulations, and prototyping with the Universal Software Radio Peripheral (USRP) demonstrate that TGT-HC achieves latency performance comparable to a First-Come-First-Served (FCFS) single server for real-time cyclic traffic, even under high frame error rates (FERs). Given its promising performance, we advocate for reconsidering contention-free CSMA as a viable MAC scheme in next-generation URLLC/TSN.

Radio interference or jamming can cause isolated area in advanced metering infrastructure (AMI) based on Wire-less smart utility network (Wi-SUN), in which conventional recovery techniques cannot cope with. In this paper, we deploy narrowband internet-of-things (NB-IoT) interface to some smart meters to act as emergency gateway, called ResiLite. An optimal ResiLite placement algorithm for enhancing network resilience is proposed. We define an implicit resilience metric based on path diversity and cluster closeness. The defined metric is used to form an integer linear programming (ILP) problem, then we solve the ILP to obtain optimal ResiLite placement. Simulation results show that the optimal ResiLite placement improves Wi-SUN AMI network resilience (defined as the number of surviving nodes with packet delivery ratio (PDR) above 99% under disturbance) by up to 167% compared to an AMI without ResiLite, and up to 29% compared to uniformly random ResiLite placement, respectively.

In this paper, we present the design, analytical and simulation results for a wireless Medium Access Control (MAC) protocol, referred to as the “Transmission Gating Time Hyperchannel” (TGT-HC) to support bounded low latency delivery of cyclic control traffic such as generated by close-loop distributed control systems. TGT-HC is based on the contention-free Carrier-Sense Multiple Access (CSMA) Hyperchannel protocol. To enhance its performance, we use a combination of per-flow Time-Aware Shaper (TAS) and First-In-First-Out (FIFO) scheduling to determine its sensing slot assignments. Based on the results, we find that this enhanced protocol comes close to the ideal delay performance of a First-Come- First-Serve (FCFS) single server in handling cyclic traffic.

Advanced metering infrastructure (AMI) based on Wireless Smart Utility Network (Wi-SUN) employs carrier sense multiple access (CSMA), hence suffers from poor network performance when the number of nodes increases but without proper design of data collection scheduling. In this paper, we introduce an interference-aware TDMA-like optimal data collection scheduling, in which a link-timeslot assignment problem is formed by considering interference constraint to achieve better spatial timeslot reuse. A constraint for achieving consecutive flow for multi-hop transmission is also introduced to avoid modifications on the original CSMA protocol, and hence the proposed scheduling is standard compliant. Our results show that the proposed scheduling reduces the total data collection time of Wi-SUN AMI network with 100(200) smart meters by 59%(48%), 33%(32%), and 19%(13%), respectively, when compared with conventional Wi-SUN AMI, conventional TDMA scheduling and heuristic scheduling based on 2-rank distance interference model.

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.