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As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-to-end latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the $ {E_D} $ E D . [ABSTRACT FROM AUTHOR]

With the development of communications, various applications of communication technologies, such as remote driving, delivery drones, and telesurgery, are emerging. In particular, in many cases, these applications need real-time video transmission services, and they should support low latency for operation reliability and quick response in emergencies. Sensor sharing is required to support advanced communication services, but the latency analysis of device-to-remote users or remote servers with high data traffic is insufficient. Most related works have device-to-device communication or low data traffic messages for basic device status sharing. However, the latency analysis of sensor sharing between a device and a remote server or remote user is essential to support advanced communication services such as autonomous driving utilizing data offloading and device operation by remote users via the base station and server. Therefore, in this paper, we analyze the end-to-end latency and latency elements for video sharing, which is the most representative sensor in 4G long-term evolution (LTE) and 5G new radio (NR) Uu interfaces. In addition, we derive the supportable video resolution according to the raw video and encoded video transmission in each communication system. For each video resolution level, we analyze which latency elements have a significant effect on the end-to-end latency. Depending on each communication system, we investigate the number of users for the real-time sensor-sharing system that can be supported at the same time. Simulation results show that the LTE Uu interface supports up to full high definition (FHD) video resolution, and the 5G Uu interface supports up to ultrahigh definition (UHD). Additionally, the results show that only a single user can be supported with the FHD resolution level in the LTE Uu interface, whereas up to 19 users can be supported in the 5G Uu interface.

Coordination among vehicular actors becomes increasingly important at the dawn of autonomous driving. With communication serving as the basis for this process, latency emerges as a critical limiting factor in information gathering, processing, and redistribution. While these processes have further implications on data privacy, they are also fundamental in safety and efficiency aspects. In this work, we target exactly these areas: we propose a privacy-preserving system for collecting and sharing data in high-mobility automotive environments that aims to minimize the latency of these processes. Namely, we focus on keeping high definition maps (highly accurate environmental and road maps with dynamic information) up-to-date in a crowd-sourced fashion. We employ federated analytics for privacy-preserving, low-latency, scalable processing and data distribution running over a two-tiered infrastructural layout consisting of mobile vehicular nodes and static nodes leveraging the low latency, high throughput and broadcast capabilities of the 5G edge. We take advantage of this setup by proposing queuing theory based analytical models and optimizations to minimize information delivery latency. As our numerical simulations over wide parameter-ranges indicate, the latency of timely data distribution can be decreased only with careful system planning and 5G infrastructure. We obtain the optimal latency characteristics in densely populated central metropolitan scenarios when Gb/s uplink speeds are achievable and the coverage area (map segment size) can reach a diameter of 1km.

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. [ABSTRACT FROM AUTHOR]

In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user's physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizing the maximum network E2E latency. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). In order to see the performance change according to the type of RL agent, we implemented a single-agent environment where the controller is an agent and a multi-agent environment where each node is an agent. Since the agents were unable to identify E2E latencies in the multi-agent environment, the state and reward were formulated using the estimated E2E latencies. To precisely evaluate the RL-based scheduler, we set out benchmark algorithms to compare with which a network-arrival-time-based heuristic algorithm (NAT-HA) and a maximum-estimated-delay-based heuristic algorithm (MED-HA). The RL-based scheduler, first-in-first-out (FIFO), round-robin (RR), NAT-HA, and MED-HA were compared through large-scale simulations on four network topologies. The simulation results in fixed-packet generation scenarios showed that our proposal, the RL-based scheduler, achieved the minimization of maximum E2E latency in all the topologies. In other scenarios with random flow generation, the RL-based scheduler and MED-HA showed the lowest maximum E2E latency for all topologies. Depending on the topology, the maximum E2E latency of NAT-HA was equal to or larger than that of the RL-based scheduler. In terms of fairness, the RL-based scheduler showed a higher level of fairness than that of FIFO and RR. NAT-HA had similar or lower fairness than the RL-based scheduler depending on the topology, and MED-HA had the same level of fairness as the RL-based scheduler. [ABSTRACT FROM AUTHOR]

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks. [ABSTRACT FROM AUTHOR]

Network processing in the current Internet is at the entirety of the data packet, which is problematic when encountering network congestion. The newly proposed Internet service named Qualitative Communication changes the network processing paradigm to an even finer granularity, namely chunk level, which obsoletes many existing networking policies and schemes, especially the caching algorithms and cache replacement policies that have been extensively explored in Web Caching, Content Delivery Networks (CDN) or Information-Centric Networks (ICN). This paper outlines all the new factors that are brought by random linear network coding-based Qualitative Communication and proves the importance and necessity of considering them. A novel metric is proposed by taking these new factors into consideration. An optimization problem is formulated to maximize the metric value of all retained chunks in the local storage of network nodes under the constraint of storage limit. A cache replacement scheme that obtains the optimal result in a recursive manner is proposed correspondingly. With the help of the introduced intelligent cache replacement algorithm, the performance evaluations show remarkably reduced end-to-end latency compared to the existing schemes in various network scenarios.

Good responsiveness of autonomous vehicle computing systems is crucial to safety and performance of the vehicles. For example, an autonomous vehicle (AV) may cause an accident if the end-to-end latency from sensing a pedestrian to emergency stop is too high. However, the AV software stacks are too complex to probabilistically analye the end-to-end latency on a multi-core system. They consist of a graph of tasks with different periods, and have a large variability in the task execution times, which may lead to the maximum core utilization $U^{\max }$ U max greater than 1.0 on some cores. This paper proposes a novel stochastic analysis of the end-to-end latency over the AV stacks that allows $U^{\max }$ U max to exceed 1.0 on each core. The proposed analysis models the entire stack as a graph of task graphs under a multi-core partitioned scheduling and provides a probabilistic guarantee that the analyzed latency distribution upper-bounds the one observed from a real system under the assumption of independent task execution times. Using the Autoware stack with inter-task dependent execution times, it is shown that our analysis, combined with a task grouping to mitigate the inter-task correlations, can give a latency distribution for each task path that almost upper-bounds the observed one. [ABSTRACT FROM AUTHOR]

Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims' devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER. [ABSTRACT FROM AUTHOR]

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) mobile network technologies include ultra-Reliable Low Latency Communications (uRLLC) capability. To fully exploit uRLLC, distributed Multi-access Edge Computing (MEC) is being developed and introduced at the network edge with an architecture that supports applications and services. Some of the MEC applications will benefit from uRLLC, including virtual reality, augmented reality, education, health, online gaming, automatic manufacturing and Vehicle-to-everything. However, unique challenges and opportunities exist for 5G cellular networks and MEC due to a range of factors, including enduser device mobility and the implementation of the network Control Plane (CP) and User Plane (UP). In this regard, there is a need to optimize protocols and network architecture. This paper investigates latency and related network elements in the next generation mobile cellular network. We also analyze the 5G network latency in the CP and UP. Finally, the paper identifies protocol optimization considerations for MEC integration with 5G to achieve low end-to-end latency. [ABSTRACT FROM AUTHOR]

SMART (Single-cycle Multi-hop Asynchronous Repeated Traversal) Network-on-Chip (NoC), a recently proposed dynamically reconfigurable NoC, enables single-cycle long-distance communication by building single-bypass paths directly between distant communication pairs. However, such a single-cycle single-bypass path will be readily broken when contention occurs. Thus, packets will be buffered at intermediate routers with blocking latency from other contending packets, and extra router-stage latency to rebuild the remaining path when available, reducing the bypassing benefits that SMART NoC offers. In this article, we for the first time propose an effective contention-minimized routing algorithm to achieve maximal bypassing in SMART NoCs. Specifically, we identify two potential routes for packets: direct route, with which packets can reach the destination in a single bypass; and indirect route, with which packets can reach the destination in multiple bypasses via a (multiple) intermediate router(s). The novel feature of the proposed routing strategy is that, contrary to an intuitive approach, not the routes with minimal distance but the indirect routes via the arbitrary intermediate routers (even if they may be non-minimal) that avoid contentions yield the minimized end-to-end latency. Our new routing strategy can greatly enrich the path diversity, effectively minimize the conflicts between communication pairs, greatly balance the workloads and fully utilize bypass paths. Evaluation on realistic benchmarks demonstrates the effectiveness of the proposed routing strategy, which achieves average performance improvement by 35.48 percent in communication latency, 28.31 percent in application schedule length, and 37.59 percent in network throughput, compared with the current routing in SMART NoCs. [ABSTRACT FROM AUTHOR]

SMART NoC, which transmits unconflicted flits to distant processing elements (PEs) in one cycle through the express bypass, is a high-performance NoC design proposed recently. However, if contention occurs, flits with low priority would not only be buffered but also could not fully utilize bypass. Although there exist several routing algorithms that decrease contentions by rounding busy routers and links, they cannot be directly applicable to SMART since it lacks the support for arbitrary-turn (i.e., the number and direction of turns are free of constraints) routing. Thus, in this article, to minimize contentions and further utilize bypass, we propose an improved SMART NoC, called ArSMART, in which the arbitrary-turn transmission is enabled. Specifically, ArSMART divides the whole NoC into multiple clusters where the route computation is conducted by the cluster controller and the data forwarding is performed by the bufferless reconfigurable router. Since the long-range transmission in SMART NoC needs to bypass the intermediate arbitration, to enable this feature, we directly configure the input and output ports connection rather than applying hop-by-hop table-based arbitration. To further explore the higher communication capabilities, effective adaptive routing algorithms that are compatible with ArSMART are proposed. The route computation overhead, one of the main concerns for adaptive routing algorithms, is hidden by our carefully designed control mechanism. Compared with the state-of-the-art SMART NoC, the experimental results demonstrate an average reduction of 40.7% in application schedule length and 29.7% in energy consumption. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate.

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%. [ABSTRACT FROM AUTHOR]

Due to the large bandwidth, low latency and computationally intensive features of virtual reality (VR) video applications, the current resource-constrained wireless and edge networks cannot meet the requirements of on-demand VR delivery. In this letter, we propose a joint horizontal and vertical collaboration architecture in multi-access edge computing (MEC)-enabled small-cell networks for downlink VR delivery. In the proposed architecture, multiple MEC servers can jointly provide VR head-mounted devices (HMDs) with edge caching and viewpoint computation services, i.e., horizontal collaboration (HC), while the caching and computation strategies can collaborate among vertical layers, i.e., vertical collaboration (VC). Power allocation at base stations (BSs) is considered in coordination with HC and VC of MEC servers to obtain lower end-to-end latency of VR delivery. A joint caching, power allocation and task offloading problem is then formulated. By exploiting the hidden monotonicity of the problem, a discrete branch-reduce-and-bound (DBRB) algorithm is proposed to obtain the optimal solution efficiently. Simulation results demonstrate the advantage of the proposed architecture and algorithm in terms of existing ones. [ABSTRACT FROM AUTHOR]

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper.

In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user’s physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizing the maximum network E2E latency. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). In order to see the performance change according to the type of RL agent, we implemented a single-agent environment where the controller is an agent and a multi-agent environment where each node is an agent. Since the agents were unable to identify E2E latencies in the multi-agent environment, the state and reward were formulated using the estimated E2E latencies. To precisely evaluate the RL-based scheduler, we set out benchmark algorithms to compare with which a network-arrival-time-based heuristic algorithm (NAT-HA) and a maximum-estimated-delay-based heuristic algorithm (MED-HA). The RL-based scheduler, first-in-first-out (FIFO), round-robin (RR), NAT-HA, and MED-HA were compared through large-scale simulations on four network topologies. The simulation results in fixed-packet generation scenarios showed that our proposal, the RL-based scheduler, achieved the minimization of maximum E2E latency in all the topologies. In other scenarios with random flow generation, the RL-based scheduler and MED-HA showed the lowest maximum E2E latency for all topologies. Depending on the topology, the maximum E2E latency of NAT-HA was equal to or larger than that of the RL-based scheduler. In terms of fairness, the RL-based scheduler showed a higher level of fairness than that of FIFO and RR. NAT-HA had similar or lower fairness than the RL-based scheduler depending on the topology, and MED-HA had the same level of fairness as the RL-based scheduler.

Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims’ devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER.

Designers of virtual reality (VR) systems are aware of the need to minimize delays between the user's tracked physical actions and the consequent displayed actions in the virtual environment. Such delays, also referred to as end-to-end latency, are known to degrade user performance and even cause simulator sickness. Though a wide variety of hardware and software design strategies have been used to reduce delays, techniques for measuring and minimizing latency continue to be needed since transmission and switching delays are likely to continue to introduce new sources of latency, especially in wireless mobile environments. This article describes a convenient low-cost technique for measuring end-to-end latencies using a human evaluator and an ordinary consumer camera (e.g., cell phone camera). Since the technique does not depend upon the use of specialized hardware and software, it differs from other methods in that it can easily be used to measure latencies of systems in the specific hardware and software configuration and the relevant performance environments. The achievable measurement accuracy was assessed in an experimental trial. Results indicate a measurement uncertainty below 10 ms. Some refinements to the technique are discussed, which may further reduce the measurement uncertainty to approximately 1 ms. [ABSTRACT FROM AUTHOR]

Low latency networking is gaining attention to support futuristic network applications like the Tactile Internet with stringent end-to-end latency requirements. In realizing the vision, cut-through (CT) switching is believed to be a promising solution to significantly reduce the latency of today's store-and-forward switching, by splitting a packet into smaller chunks called flits and forwarding them concurrently through input and output ports of a switch. Nevertheless, the end-to-end latency performance of CT switching has not been well studied in heterogeneous networks, which hinders its adoption to general-topology networks with heterogeneous links. To fill the gap, this paper proposes an end-to-end latency prediction model in a heterogeneous CT switching network, where the major challenge comes from the fact that a packet's end-to-end latency relies on how and when its flits are forwarded at each switch while each flit is forwarded individually. As a result, traditional packet-based queueing models are not instantly applicable, and thus we construct a method to estimate per-hop queueing delay via M/G/c queueing approximation, based on which we predict end-to-end latency of a packet. Our extensive simulation results show that the proposed model achieves 3.98-6.05% 90th-percentile error in end-to-end latency prediction.

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.

This letter investigates the forward error correction code (FEC) design for achieving low end-to-end (E2E) latency in a communication system with geometric packet arrival and delayed feedback. We first derive the bounds for the optimal tradeoff between the E2E latency and the channel utilization by considering the automatic repeat request (ARQ) scheme with instantaneous feedback and the blind coding with no feedback, respectively. Then, we propose a flexible code design strategy by modeling the problem as a partially-observable Markov decision process (POMDP). Various tradeoffs between the latency and channel utilization can be achieved by adjusting the weighting coefficient in the reward function of the POMDP. Simulation results show that the coding strategy obtained via solving the POMDP outperforms the benchmark schemes by achieving lower E2E latency. [ABSTRACT FROM AUTHOR]

Achieving low end-to-end latency with high reliability is one of the key objectives for future mission-critical applications, like the Tactile Internet and real-time interactive Virtual/Augmented Reality (VR/AR). To serve the purpose, cut-through (CT) switching is a promising approach to significantly reduce the transmission delay of store-and-forward switching, via flit-ization of a packet and concurrent forwarding of the flits belonging to the same packet. CT switching, however, has been applied only to well-controlled scenarios like network-on-chip and data center networks, and hence flit scheduling in heterogeneous environments (e.g., the Internet and wide area network) has been given little attention. This paper tries to fill the gap to facilitate the adoption of CT switching in the general-purpose data networks. In particular, we first introduce a packet discarding technique that sheds the packet expected to violate its delay requirement and then propose two flit scheduling algorithms, f EDF (flit-based Earliest Deadline First) and f SPF (flit-based Shortest Processing-time First), aiming at enhancing both reliability and end-to-end latency. Considering packet delivery ratio (PDR) as a reliability metric, we performed extensive simulations to show that the proposed scheduling algorithms can enhance PDR by up to 30.11% (when the delay requirement is 7 ms) and the average end-to-end latency by up to 13.86% (when the delay requirement is 10 ms), against first-in first-out (FIFO) scheduling.

Wireless body Area networks (WBANs) include a number of sensor nodes placed inside or on the human body to improve patient health and quality of life. Ensuring the transfer and receipt of data in sensitive data is a very important issue. Routing algorithms should support a variety of service quality such as reliability and delay in sending and receiving data. Loss of information or excessive data delay can lead to loss of human life. A proper routing algorithm in WBAN networks provides an efficient route with minimum delay and higher reliability for sensitive data. In this context, a routing algorithm, as it is proposed, categorizes patient data into sensitive and non-sensitive. Sensitive packets are transmitted to the destination through the shortest route to have less delay and non-sensitive packets are transmitted from other routes. Simulation shows that the proposed algorithm performs better, in terms of the throughput than the DMQoS and RL-QRP this superiority; as a result, decreases the latency of the end.

With network slicing, physical networks are partitioned into multiple virtual networks tailored to serve different types of service with their specific requirements. In order to optimize the utilization of network resources for delay-critical applications, we propose a new multi-domain network virtualization framework based on a novel multipath multihop delay model. This framework encompasses a novel hierarchical orchestration mechanism for mapping network slices onto physical resources and a mechanism for dynamic slice resizing. The main idea is to locally redefine the delay requirements on each network domain depending on the conditions in the rest of the network. Delays larger than threshold (debt) are allowed in certain domains if there is a possibility to compensate such excessive delays in other segments of the network that can transmit the messages with less latency (credit). This tradeoff or delay threshold redefinition on different segments of the route is referred to as network latency equalization. For performance comparison, minimum cost routing with latency constraints is used as a baseline. We show that our approach enables significantly better utilization of the network resources measured in the number of slices with the same latency requirements that can be accommodated in the network. [ABSTRACT FROM AUTHOR]

In this paper, we consider the packet-level forward error correction (FEC) code design, without feedback or with delayed feedback, for achieving the minimum end-to-end latency, i.e., the latency between the time that packet is generated at the source and its in-order delivery to the application layer of the destination. We first show that the minimum-latency FEC design problem can be modeled as a partially observable Markov decision process (POMDP), and hence the optimal code construction can be obtained by solving the corresponding POMDP. However, solving the POMDP optimally is in general difficult unless its state and action space is very small. To this end, we propose an efficient heuristic algorithm, namely the majority vote policy, for obtaining a high quality approximate solution. We also derive the tight lower and upper bounds of the optimal state values of this POMDP, based on which a more sophisticated $D$ -step search algorithm can be implemented for obtaining near-optimal solutions. The simulation results show that the proposed code designs via solving the POMDP, either with the majority vote policy or the $D$ -step search algorithm, strictly outperform the existing schemes, for both cases, without or with only delayed feedback. [ABSTRACT FROM AUTHOR]

Different communication models have been historically adopted in the automotive domain for allowing concurrent tasks to coordinate, interact and synchronize on a shared memory system for implementing complex functions, while ensuring data consistency and/or time determinism. To this extent, most automotive OSs provide inter-task communication and synchronization mechanisms based upon memory-sharing paradigms, where variables modified by one task may be concurrently accessed also by other tasks. A so-called "effect chain" is created when the effect of an initial event is propagated to an actuation signal through sequences of tasks writing/reading shared variables. The responsiveness, performance and stability of the control algorithms of an automotive application typically depend on the propagation delays of selected effect chains. Depending on the communication model adopted, the propagation delay of an effect chain may significantly vary, as may be the resulting overhead and memory footprint. In this paper, we explore the trade-offs between three communication models that are widely adopted for industrial automotive systems, namely, Explicit, Implicit, and Logical Execution Time (LET). A timing and schedulability analysis is provided for tasks scheduled following a mixed preemptive configuration, as specified in the AUTOSAR model. An end-to-end latency characterization is then proposed, deriving different latency metrics for effect chains under each one of the considered models. The results are compared against an industrial case study consisting of an automotive engine control system. [ABSTRACT FROM AUTHOR]

Future multimedia communication systems aim to support high-rate data transmission and deliver millisecond-scale latency performance. To achieve this goal, this letter revisits the multimedia compression strategy in the framework of mobile edge computing from the perspective of latency minimization. The uniqueness of this letter is that the delay caused by both data compression/decompression and data transmission are considered. According to where to compress and decompress data, three different schemes are investigated and the end-to-end latency of each scheme is analyzed. We first present some comparative theoretical results on the latency performance of different schemes for given compression ratio. Then, we prove that the optimal compression ratio to minimize the end-to-end latency has a binary structure. This initial work reveals that it is optimal to transmit without compression in the case that the communication capacity is sufficiently large. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a widely known short-range wireless technology used for various Internet of Things (IoT) applications. Recently, with the introduction of BLE mesh networks, this short-range barrier of BLE has been overcome. However, the added advantage of an extended range can come at the cost of a lower performance of these networks in terms of latency, throughput and reliability, as the core operation of BLE mesh is based on advertising and packet flooding. Hence, efficient management of the system is required to achieve a good performance of these networks and a smoother functioning in dense scenarios. As the number of configuration points in a standard mesh network is limited, this paper describes a novel set of standard compliant Quality of Service (QoS) extensions for BLE mesh networks. The resulting QoS features enable better traffic management in the mesh network, providing sufficient redundancy to achieve reliability whilst avoiding unnecessary packet flooding to reduce collisions, as well as the prioritization of certain traffic flows and the ability to control end-to-end latencies. The QoS-based system has been implemented and validated in a small-scale BLE mesh network and compared against a setup without any QoS support. The assessment in a small-scale test setup confirms that applying our QoS features can enhance these types of non-scheduled and random access networks in a significant way.

For buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hops increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop network with millimeter-wave (mm-wave) as an example, we derive lower bounds on the average end-to-end latency, where Nakagami- $m$ fading is considered. Numerical results demonstrate that, compared with the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency. [ABSTRACT FROM AUTHOR]