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The Cenozoic era is the digital age where people, things, and any device with network capabilities can communicate with each other, and the Internet of Things (IoT) paves the way for it. Almost all domains are adopting IoT from smart home appliances, smart healthcare, smart transportation, Industrial IoT and many others. As the adoption of IoT increases, the accretion of data also grows. Furthermore, digital transformations have led to more security vulnerabilities, resulting in data breaches and cyber-attacks. One of the most prominent issues in smart environments is a delay in data processing while all IoT smart environments store their data in the cloud and retrieve them for every transaction. With increased data accumulations on the cloud, most smart applications face unprecedented delays. Thus, data security and low-latency response time are mandatory to deploy a robust IoT-based smart environment. Blockchain, a decentralized and immutable distributed ledger technology, is an essential candidate for ensuring secured data transactions, but it has a variety of challenges in accommodating resource-constrained IoT devices. Edge computing brings data storage and computation closer to the network’s edge and can be integrated with blockchain for low-latency response time in data processing. Integrating blockchain with edge computing will ensure faster and more secure data transactions, thus reducing the computational and communicational overhead concerning resource allocation, data transaction and decision-making. This paper discusses the seamless integration of blockchain and edge computing in IoT environments, various use cases, notable blockchain-enabled edge computing architectures in the literature, secured data transaction frameworks, opportunities, research challenges, and future directions. [ABSTRACT FROM AUTHOR]

The low-latency property is becoming increasingly crucial in response to the demand for data processing in the Internet of Things (IoT) environment, especially in lightweight cryptography. A low-latency lightweight block cipher called LTLBC is proposed in this paper. In particular, we propose a hybrid approach of word-wise involutive mapping and a bit-wise permutation, with careful selection of the shift and permutation parameters. This scheme enables LTLBC to achieve better diffusion than Midori and MANTIS. Also, a 4 × 4 S-box which is constructed through a gate-level bottom-up circuit search has good security and latency characteristics. LTLBC mainly focuses on the hardware environment of fully unrolled architecture. LTLBC achieves a minimum latency of around 4.73 ns, with a total area of only 10007.6 μ m 2 based on NanGate 45nm technology. The comprehensive safety analysis shows LTLBC has sufficient redundancy rounds to resist attacks, such as differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis, etc. [ABSTRACT FROM AUTHOR]

Purpose: Develop a true real‐time implementation of MR signature matching (MRSIGMA) for free‐breathing 3D MRI with sub‐200 ms latency on the Elekta Unity 1.5T MR‐Linac. Methods: MRSIGMA was implemented on an external computer with a network connection to the MR‐Linac. Stack‐of‐stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground‐truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real‐time implementation of MRSIGMA. Dice coefficients between real‐time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. Results: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3–1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum‐stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. Conclusion: The real‐time implementation of MRSIGMA enabled true real‐time free‐breathing 3D MRI with sub‐200 ms imaging latency on a clinical MR‐Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion. [ABSTRACT FROM AUTHOR]

The emergence of blockchain technology, characterized by its immutable and decentralized digital ledger system, has sparked significant interest due to its potential to revolutionize several industries. Simultaneously, Long-Term Evolution (LTE) networks have emerged as the fundamental infrastructure for facilitating high-speed wireless communication. The correlation between these two technologies is still a subject that has yet to be well studied despite its potential significance for addressing the scalability issues of blockchain. The article aimed to explore the possible advantages and improvements that might arise from integrating Long-Term Evolution (LTE) networks into blockchain systems, with a specific emphasis on scalability, reliability, and efficiency. This study introduced a unique framework, incorporating Long-Term Evolution (LTE) as a supplementary communication layer to enhance the current Peer-to-Peer (P2P) blockchain architecture. The comprehensive studies included powerful simulation models, practical testing, and comparison analysis against widely used scaling options such as Layer 2 protocols and sharding. Results: The experimental findings indicate that integrating LTE technology with blockchain resulted in a substantial increase in transaction throughput and a noteworthy decrease in confirmation times. Significantly, this innovation did not compromise the inherent decentralized character of the blockchain. Moreover, the LTE-augmented blockchain exhibits a higher resilience level than conventional blockchain systems when faced with common network risks, like Sybil and Eclipse assaults. Long-Term Evolution (LTE) networks in blockchain systems create a collaborative telecom-blockchain framework. This combination could benefit banking, supply chain management, and the Internet of Things. The findings show that LTE networks help blockchain technology scale and survive.

This study presents the design and implementation of a PLC microprocessor adhering to the IEC-61131-3 standard, executed on a Cyclone V FPGA using a DE10-NANO development board. Our microprocessor optimizes the central processing unit by streamlining the data path, achieving a remarkable simulated response time of approximately 60 ns, equivalent to three clock cycles at a 50 MHz frequency for Boolean operations. To substantiate our approach, we conducted practical experiments utilizing a FESTO conveyor station, employing relays as actuators, and incorporating optical and inductive sensors. The results underscore the feasibility of our proposed approach and serve as practical validation of its efficacy. This work introduces a promising avenue for the development of cost-effective PLCs employing SoC FPGA variants. Additionally, a thorough comparison of execution times with other early reported architectures. Our microprocessor outperforms even well-established PLCs like the S7-312, with substantial reductions in execution times of 94.54% for floating-point operations, 71.42–93.33% for word operations, and up to 78.57% for bit operations. [ABSTRACT FROM AUTHOR]

The restrictions of the pandemic forced the idea of an information system that integrated remote public participation in a live television show. The existing mechanisms were then too rigid, of poor audiovisual quality, and had an excessive and unsustainable delay between emission and reception for real-time television interaction. The creation of the Virtual Audience System (VAS) proposed the use of the most recent technology in areas such as cloud and edge computing, lowlatency audio and video transmission, interactive communication in the web environment, and the creation of immersive binaural sound to overcome the limitations of the moment. With these new technological elements, a new system of transmission and reception of audiovisual information was developed that allows the adaptation and scalability of media production to the type of event broadcasted and its audience with a minimum investment and, at the same time, generates a new concept of audience appropriate for the 21st century. The Virtual Audience System enables interactive and synchronized participation in live entertainment shows and, at the same time, allows the creation of remote virtual participants while guaranteeing a real and immersive experience for the user. The WebRTC, RTMP and SRT technology used, which is already popular and has universal compatibility; the low latency achieved, which is below two seconds for the complete cycle; and the immersive sensation of the audience--everything together predicts that this system can soon transform the television production methodology of shows with live audience participation. [ABSTRACT FROM AUTHOR]

With the introduction of 5 G technology, various communication services are becoming integrated into more sophisticated systems, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine-Type Communications (mMTC). The goal is to facilitate seamless connectivity for many devices, ensuring minimal transmission delays. This advancement finds application in diverse fields like remote surgery and intelligent transportation systems. To achieve this, one approach involves the utilization of random access protocols. Implementing multi-power-level systems under Non-Orthogonal Multiple Access (NOMA) allows devices to efficiently share the same resource block (time and frequency) for transmission while varying their transmission power. This work delves into determining the optimal power distribution profiles in power domain NOMA-based random access networks. Specifically, it examines linear, quadratic, square-root, and exponential multi-power level profiles to identify the most efficient multi-power Q-learning random access strategy. Additionally, we investigate the optimal number of power levels for these profiles, considering factors such as latency and throughput. We analyze the impact of minimum SINR requirements on the overall system throughput. [ABSTRACT FROM AUTHOR]

In this paper, we present some new observations on the branch number and study concrete differential analysis of SPEEDY. It is a new low-latency block cipher proposed at TCHES 2021. It employs SPS-type round function and consists of only 5/6/7 rounds. Since the iteration rounds are rather small so as to achieve ultra low-latency in encryption speed, it will be crucially important to analyze its security margin accurately. In this paper, we first propose a new notation of partition branch number which can describe the minimum number of active S-boxes for 2-round SPEEDY more accurately. An efficient algorithm to compute the value of partition branch number is also given. Then by extending the notation to higher-order partition branch number, we can obtain more accurate results of the minimum number of active S-boxes for 3–7 rounds. As a result, the maximum expected differential probabilities are significantly higher than the results estimated by designers. Based on this, we search for optimal differential characteristics of SPEEDY while considering the difference distribution table of S-box. We present examples of differential characteristics for 2–7 rounds. Furthermore, by utilizing the simple bit-permutation key schedule of SPEEDY, we can extend the differential trail search method and construct an efficient 6-round related-key differential trail with probability 2 - 179.2 . Based on it, we can present related-key differential attack on full round SPEEDY-7-192 with data complexity of 2 186.2 chosen-plaintexts and time complexity of 2 160.13 encryptions. [ABSTRACT FROM AUTHOR]

A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μ s under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods. [ABSTRACT FROM AUTHOR]

In recent years, advancements in telecommunication technology have led to the proliferation of high-speed, large-capacity, low-latency communication. The COVID-19 pandemic has also accelerated the adoption of remote work globally, making real-time remote communication applications like web conferencing crucial for business, education, and other sectors. Despite the various demands for streaming applications, existing services often fail to meet the requirements of scenarios demanding ultra-low latency, such as surgical operations, remote control of automobiles, and real-time collaborative performances. To address this, we explored using FPGAs to achieve ultra-low latency processing in image processing tasks, explicitly focusing on background removal. Our study demonstrated the feasibility of using commercially available FPGA devices to reduce latency in background subtraction significantly compared to conventional methods. The results indicate that FPGA-based processing can provide the ultra-low latency needed for critical applications, enhancing the performance and user experience in remote operations and real-time streaming.

The demand for ultra-reliable low-latency communications (URLLC) has led to the adoption of grant-free (GF) access techniques by the 5G NR, with the goal of reducing uplink access time. When GF access is employed, the base station (BS) preallocates multiple transmission opportunities (TOs) that can be utilized by the user equipment (UE) as needed. However, this approach results in inefficient resource utilization as unused TOs are wasted. To overcome this inefficiency, the 5G NR allows the assignment of configured grants (CG) to a group of UEs instead of a single one. This development has led research into group-based CG (GCG) schemes, whose reliance on shared resources can result in collisions. The collisions can be prevented by the use of stop-and-wait IR-HARQ schemes. Nevertheless, the delay caused by feedback latency is also undesirable as it severely affects latency performance. This work proposes two new IR-HARQ GCG schemes to efficiently handle feedback latency. The first one is able to eliminate the feedback latency overhead and is proven to simultaneously achieve the latency of a one-shot transmission and the energy efficiency of IR-HARQ, even in the presence of non-instantaneous feedback signaling. The second one features both a feedback latency protection mechanism, similar to the first scheme, and a mechanism specifically designed to further reduce latency. The performance of the proposed schemes is compared with scenarios where each UE uses either an individual one-shot or an IR-HARQ scheme. These comparisons encompass scenarios with either power or energy constraints. The results have shown that the second scheme always outperforms the IR-HARQ scheme and that it is able to outperform the one-shot scheme on a wide interval of feedback latency values, achieving a lower latency both for power and energy constrained cases.

To achieve low-latency and energy-efficient in a 10 Gbps Ethernet Passive Optical Network (10G-EPON) having heterogeneous Optical Network Unit (ONU) propagation delays, this article proposes a resource allocation scheme based on bandwidth requirements prediction and ONU polling sequence scheduling. By a) predicting the upstream (US) and downstream (DS) bandwidth requirements of each ONU using Long Short-Term Memory (LSTM) networks and b) arranging the ONU polling sequence and the US and DS transmission start times of each ONU based on the predicted bandwidth requirements and the ONU propagation delays, the proposed scheme minimizes bandwidth waste of the network as well as the overall operating time of each ONU. Therefore, low-latency performance is achieved, and the energy consumption of ONUs is reduced as ONU sleeping time is extended. Simulation results indicate that the proposed scheme reduces network latency and improves energy efficiency effectively. Compared to the conventional Interleaved Polling with Adaptive Cycle Time scheme that adopts the shortest propagation delay first rule and ONU doze mode, the proposed scheme can reduce the average one-way packet delay by at least 28.9%, and can reduce the per bit energy consumption by 73.7% when concerning data transmitted under the 1 ms latency constraint.

Cloud gaming allows users to play high-quality games on low-end devices by offloading game processing to the cloud. However, network latency remains a significant issue affecting the gaming experience. Speculative execution is a promising approach to hide network latency by predicting and transmitting future frames early. However, existing methods generate excessive compute load and network traffic due to many potential input patterns. This paper introduces a pattern reduction method that uses a bit field representation of the input and facilitates efficient speculative execution in cloud games. There are two pattern reduction techniques: analyzing temporal patterns to detect frequent transitions and using LSTM-based predictions to estimate input probabilities. Experiments using actual gaming data show that the proposed methods significantly reduce rendered frames and network traffic versus prior speculative execution methods. The results demonstrate the method’s effectiveness and scalability across diverse game genres.

In this article, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83% and the complexity up to 73% compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.

A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μs under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods.

The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things (IIoT), automotive, robotics, etc. In this paper, we make an attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.

The automotive Ethernet is an emerging area for exploiting the high network bandwidth and scalable network design in automated driving systems. Low-latency live video streaming over the automotive Ethernet is a challenging part of this concept because of its significance for driving situation awareness in real time. In order to achieve low-latency live video streaming on resource-constrained automotive electric control units, the streaming needs to be conducted with a fine-grained resource control. This article proposes a raw format live video streaming framework that resiliently controls the number of raw format video packets with consideration of available CPU resources. The evaluation on a testbed demonstrates that the proposed framework achieves low-latency live video streaming by securing available CPU resources.

The ultra-reliable low-latency communications (URLLC) tight latency requirements paired with transmission of small payload packets motivates the development of techniques that reduce or eliminate the need for dynamic scheduling. This justifies the study of grant free (GF) leveraged techniques in order to reduce both the latency and control signaling overhead. Previous works considered preallocating resources not only for the first transmission, but also for all possible IR-HARQ transmissions, effectively reducing the scheduling latency and control signaling overhead. However, this has several drawbacks, as it translates into wasted resources. To address these issues, we propose a group-based preallocation method combined with IR-HARQ. Initially, a pool of preallocated resources is assigned to a group of users, which then cooperatively use IR-HARQ feedback signals to distribute, on the fly, the resources amongst them without collisions. The proposed method has two phases: a preallocation phase that takes place once at the group formation stage and a transmission phase which happens at each uplink transmission. The transmission parameters for all possible transmission scenarios are selected at the preallocation stage, with the goal of reducing the latency under reliability and energy constraints. The transmission parameters are obtained through a constrained latency optimization procedure, which considers the stochastic nature of the underlying process. We prove that, asymptotically, the proposed scheme is able to reduce the latency, at least, down to the average latency of any single user (SU) HARQ. The numerical results show that the latency and resources wastage is significantly reduced comparatively to single user IR-HARQ with preallocated resources.

Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times. In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics. In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE. This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution. Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS). We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players. Our testbed generates the test content (audiovisual streams). Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment. We have integrated the ITU-T P.1203 quality model inside our testbed. To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.

The entire world progression has ceased with the unexpected outbreak of the COVID-19 pandemic, and urges the requirement for contact-less and autonomous services and applications. Realizing these predominantly Internet of Things (IoT) based applications demands a holistic pervasive computing infrastructure. In this paper, we conduct a survey to determine the possible pervasive approaches for utilizing the Multi-Access Edge Computing (MEC) infrastructure in realizing the requirements of emerging IoT applications. We have formalized specific architectural layouts for the considered IoT applications, while specifying network-level requirements to realize such approaches; and conducted a simulation to test the feasibility of proposed MEC approaches.

New services with low-latency (LL) requirements are one of the major challenges for the envisioned Internet. Many optimizations targeting the latency reduction have been proposed, and among them, jointly re-architecting congestion control and active queue management (AQM) has been particularly considered. In this effort, the Low Latency, Low Loss and Scalable Throughput (L4S) proposal aims at allowing both Classic and LL traffic to cohabit within a single node architecture. Although this architecture sounds promising for latency improvement, it can be exploited by an attacker to perform malicious actions whose purposes are to defeat its LL feature and consequently make their supported applications unusable. In this paper, we exploit different vulnerabilities of L4S which are the root of possible attacks and we show that application-layer protocols such as QUIC can easily be hacked in order to exploit the over-sensitivity of those new services to network variations. By implementing such undesirable flows in a real testbed and characterizing how they impact the proper delivery of LL flows, we demonstrate their reality and give insights for research directions on their detection. [ABSTRACT FROM AUTHOR]

Short-reach transmission systems traditionally utilize intensity modulation (IM) at the transmitter and direct detection (DD) at the receiver due to their cost-effectiveness, small footprint, and low power consumption. However, with the exponential increase in bandwidth demand, coherent optical communication systems have become necessary for long-haul distances, requiring application-specific integrated circuits (ASIC) and advanced digital signal processing (DSP) algorithms coupled with high-speed digital-to-analog and analog-to-digital converters to achieve Tbit/s speeds. As coherent technology matures, it will eventually become feasible for short-reach transmission. In this context, self-coherent systems have emerged as an intermediary solution, offering advantages over traditional IM/DD systems. While comprehensive review studies exist on self-coherent transceivers, they do not cover recent advances in phase retrieval methods for short-reach optical communications. This review article highlights recent developments in cost-effective self-coherent detection for short-reach systems through comparing the benefits of single sideband (SSB) transmission and Kramers-Kronig detection to carrier-assisted phase retrieval, the Gerchberg-Saxton (GS) algorithm, and the transport of intensity equation (TIE) method. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) applications on rehabilitation a home-base exercise experiences have boomed in the last decade. This is mainly because their entertainment capacity creates a sense of immersion in the users, which enhances adherence to their use. In addition, offering body-related visual feedback is a proven approach to the physical training towards a goal. Recent literature showed the exercise of pedalling has the potential to provide a high number of flexion and extension repetitions of the lower limb in reasonable therapeutic time periods to improve muscle activity, strength and balance in elders, but also motor improvements in patients with neurological injuries. The objective of this work is to present a low-cost wireless application in virtual reality (VR) for pedalling exercises. The platform developed consists of a VR headset and an inertial measurement unit (IMU). The VR headset processes the kinematic information of the IMU to estimate the cadence of the pedalling, while the IMU sensor tracks the angle of hip flexion/extension movement of the user. In order to confirm the suitability of this cadence estimation system, our approach is confronted with a cycling platform developed and validated in a previous study. In the present study, we carried out two repeated sessions with 13 subjects at 3 set speeds: slow (30 rpm), medium (60 rpm) and fast (90 rpm). The Spearman's correlation (PC) between both systems for the 3 speeds and sessions shows high correlation values for low and medium speeds and moderate correlation for high speed. The SEM results for each system show low measurement error (about 1 cycle) for both systems at every target speed, except for the virtual cycling platform at the highest speed (SEM of VCP at 90 rpm = 3.24 cycles). The repeatability analysis based on ICC (3, 1) absolute agreement shows consistency in all measurements for both systems at high speed and also reflects the irregularity in measurements at low and medium speeds, where participants were less stable during testing due to entertainment from the VR system. All in all, it is concluded the validity of the cadence estimation system for pedalling exercises with low intensity. This development allows us to control the virtual environment by adapting the visual stimulus to cycling cadence. The proposed system can generate sensitive inputs to influence the user's pedalling cadence. [ABSTRACT FROM AUTHOR]

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize the service confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30 to 50 percent improvement in the confidence levels of fog and cloud latencies, respectively. [ABSTRACT FROM AUTHOR]

The recent surge in human-controlled robotics and haptic devices research is expediting a paradigm shift in today’s communication networks towards human-to-robot (H2R) centric technologies that support Industrial Internet of Things (IIoT) and Industry 5.0. In both IIoT and Industry 5.0, human skills are extended through collaboration with robots that are geographically separated from the human. Depending on the dynamicity of the actual use case, human-to-robot communications necessitate low-latency networking. While Long Term Evolution (LTE) cellular technology has been successful in fulfilling the bandwidth demands of massively-connected sensors and devices of Industry 4.0, it is insufficient to meet the low latency demands of the future Industry 5.0 where dynamic interactions between humans and robots are paramount. In reducing the latency caused by radio resource contention in wireless H2R communications, in this work, we propose a novel approach that exploits an Attention-based Recurrent Neural Network (Att-RNN) to improve the Semi-Persistent Scheduling (SPS) resource allocation scheme adopted by LTE and new radio (NR) standards developed for the fifth generation (5G) mobile networks. We conduct a series of real haptic experiments to collect H2R traffic traces to train, test and evaluate the accuracy of Att-RNN in predicting H2R traffic. Then, with extensive simulations based on the empirical H2R traffic traces, we show that our proposed Att-RNN SPS scheme outperforms classic SPS and other existing resource allocation schemes in terms of reduced latency and improved resource allocation efficiency, thus making Att-RNN SPS a suitable candidate in future Industry 5.0 deployments.

Highly immersive content in the form of extended reality (XR) is attracting attention as an alternative to conventional video services such as YouTube and Facebook. Many galleries and museums already offer online virtual reality (VR) tours where users are free to choose the spot they want to move to, beyond merely looking around. Although the ease of implementation, this key-spot hopping is still far from giving the real feeling of walking. Meanwhile, in recent volumetric or light-field-based studies, view rendering that supports free and continuous viewpoint movements has been attempted. With online services in mind, however, the high data volume and computational complexity are a big obstacle to practical applications. Path-walking VR, the target system of this paper, can be a good compromise, where the viewer can enjoy the virtual space while walking along the route. The interactive path-walking VR service is entry-level immersive video, but streaming over the network is still challenging. One of the main problems to be tackled is that the movement patterns of viewers need to be reflected in the streaming strategy to improve the quality of experience. Unlike unidirectional video, the movement of the viewer determines which images and how many images should be transmitted. This paper proposes schemes to reduce streaming delays by reflecting the viewer’s movement characteristics. It is differentiated from existing studies for omnidirectional video in that the proposed schemes control not only image quality but also view update rate. The first is a caching strategy which takes advantage of the geometrical locality of the virtual space that the viewer will soon reach a position close to the current position. This not only reduces the communication delay from the server, but also decreases the burden of server-side request handling. The second scheme uses the relationship between the viewer’s speed and the field of vision. The image quality is adjusted according to the viewer’s speed and head direction. Experimental results show that the proposed schemes achieve stable viewer’s experience by considering walking characteristics in virtual space. It is expected that the results of this paper will provide insight to those who design interactive streaming systems for immersive media applications.

Critical systems such as drone control or power grid control applications rely on embedded devices capable of a real-time response. While much research and advancements have been made to implement low-latency and real-time characteristics, the security aspect has been left aside. All current real-time operating systems available for industrial embedded devices are implemented in the C programming language, which makes them prone to memory safety issues. As a response to this, Tock, an innovative secure operating system for embedded devices written completely in Rust, has recently appeared. The only downside of Tock is that it lacks the low-latency real-time component. Therefore, the purpose of this research is to leverage the extended Berkeley Packet Filter technology used for efficient network traffic processing and to add the low-latency capability to Tock. The result is a secure low-latency operating system for embedded devices and microcontrollers capable of handling interrupts at latencies as low as 60 µs. [ABSTRACT FROM AUTHOR]

A verifiable delay function (VDF) is a function whose evaluation requires running a prescribed number of sequential steps over a group while the result can be efficiently verified. As a kind of cryptographic primitives, VDFs have been adopted in rapidly growing applications for decentralized systems. For the security of VDFs in practical applications, it is widely agreed that the fastest implementation for the VDF evaluation, sequential squarings in a group of unknown order, should be publicly provided. To this end, we propose a possible minimum latency hardware implementation for the squaring in class groups by algorithmic and architectural level co-optimization. Firstly, low-latency architectures for large-number division, multiplication, and addition are devised using redundant representation, respectively. Secondly, we present two hardware-friendly algorithms which avoid time-consuming divisions involved in calculations related to the extended greatest common divisor (XGCD) and design the corresponding low-latency architectures. Besides, we schedule and reuse these computation modules to achieve good resource utilization by using compact instruction control. Finally, we code and synthesize the proposed design under the TSMC 28nm CMOS technology. The experimental results show that our design can achieve a speedup of 3.6x compared to the state-of-the-art implementation of the squaring in the class group. Moreover, compared to the optimal C++ implementation over an advanced CPU, our implementation is 9.1x faster.

Recently, a memory safety concept called Cryptographic Capability Computing (C3) has been proposed. C3 is the first memory safety mechanism that works without requiring extra storage for metadata and hence, has the potential to significantly enhance the security of modern IT-systems at a rather low cost. To achieve this, C3 heavily relies on ultra-low-latency cryptographic primitives. However, the most crucial primitive required by C3 demands uncommon dimensions. To partially encrypt 64-bit pointers, a 24-bit tweakable block cipher with a 40-bit tweak is needed. The research on low-latency tweakable block ciphers with such small dimensions is not very mature. Therefore, designing such a cipher provides a great research challenge, which we take on with this paper. As a result, we present BipBip, a 24-bit tweakable block cipher with a 40-bit tweak that allows for ASIC implementations with a latency of 3 cycles at a 4.5 GHz clock frequency on a modern 10 nm CMOS technology.

In this paper, we study the gate depth complexity of (vectorial) Boolean functions in the basis of {NAND, NOR, INV} as a new metric, called latency complexity, to mathematically measure the latency of Boolean functions. We present efficient algorithms to find all Boolean functions with low-latency complexity, or to determine the latency complexity of the (vectorial) Boolean functions, and to find all the circuits with the minimum latency complexity for a given Boolean function. Then, we present another algorithm to build bijective S-boxes with low-latency complexity which with respect to the computation cost, this algorithm overcomes the previous methods of building S-boxes. As a result, for latency complexity 3, we present n-bit S-boxes of 3 ≤ n ≤ 8 with linearity 2n−1 and uniformity 2n−2 (except for 5-bit S-boxes for whose the minimum achievable uniformity is 6). Besides, for latency complexity 4, we present several n-bit S-boxes of 5 ≤ n < 8 with linearity 2n−2 and uniformity 2n−4.

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models. [ABSTRACT FROM AUTHOR]

The authenticated key exchange (AKE) protocol can ensure secure communication between a client and a server in the electricity transaction of the Energy Internet of things (EIoT). Park proposed a two-factor authentication protocol 2PAKEP, whose computational burden of authentication is evenly shared by both sides. However, the computing capability of the client device is weaker than that of the server. Therefore, based on 2PAKEP, we propose an authentication protocol that transfers computational tasks from the client to the server. The client has fewer computing tasks in this protocol than the server, and the overall latency will be greatly reduced. Furthermore, the security of the proposed protocol is analyzed by using the ROR model and GNY logic. We verify the low-latency advantage of the proposed protocol through various comparative experiments and use it for EIoT electricity transaction systems in a Metaverse scenario. [ABSTRACT FROM AUTHOR]

Dynamic adaptive streaming over HTTP (DASH) is the crucial factor in the rapid penetration of over-the-top (OTT) service providers for on-demand video streaming. It can also be used for live video streaming by the OTT providers. The recent advancements of the HTTP chunked transfer, and the Common Media Application Format (CMAF) echo this tendency, which introduces the possibility to deliver a video segment by small chunks before the full segment is generated. It can deliver live latency of three seconds or less on a conventional DASH player with a small buffer capacity less than the target live latency. However, legacy bitrate adaptation mechanisms inaccurately measure the available bandwidth due to idle times between the chunks at the encoder side. To resolve this problem, we utilize the Software-Defined Networking (SDN) paradigm that directly provides the network statistics with the available bandwidth. We, then, propose an SDN-assisted bitrate adaptation mechanism for live streaming with HTTP 1.1 Chunked Transfer of CMAF packages while keeping the coexistence with the legacy DASH clients. Our SDN-based central framework asynchronously sends the video bitrate levels by continuously monitoring the background traffic flows and the available capacity for DASH clients on the same shared bottleneck link. Results show that our proposed mechanism achieves a lower video freeze rate and provides a better quality-of-experience while reducing the live latency down to about three seconds in the existence of varying background traffic. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) has received significant attention since its decisive factor for modern non-cooperative communication systems. Meanwhile, the existing works on deep learning technique achieve exceptional accuracy; however, these works dissatisfy real-time requirements for 5G low-latency services. To remedy this flaw, this brief proposes a low-latency AMR method by applying temporal convolutional network (TCN). Furthermore, the principal component analysis (PCA)-based TCN and uniform subsampling-based TCN methods are leveraged to further alleviate the computation complexity and render real-time TCN viable. Experimental results demonstrate that the proposed method can achieve lower complexity and superior recognition accuracy than existing works and pave the way for 5G low-latency services. [ABSTRACT FROM AUTHOR]

Research on the design of masked cryptographic hardware circuits in the past has mostly focused on reducing area and randomness requirements. However, many embedded devices like smart cards and IoT nodes also need to meet certain performance criteria, which is why the latency of masked hardware circuits also represents an important metric for many practical applications. The root cause of latency in masked hardware circuits is the need for additional register stages that synchronize the propagation of shares. Otherwise, glitches would violate the basic assumptions of the used masking scheme. This issue can be addressed to some extent, e.g., by using lightweight cryptographic algorithms with low-degree Sboxes, however, many applications still require the usage of schemes with higher-degree S-boxes like AES. Several recent works have already proposed solutions that help reduce this latency yet they either come with noticeably increased area/randomness requirements, limitations on masking orders, or specific assumptions on the general architecture of the crypto core. In this work, we introduce a generic and efficient method for designing single-cycle glitch-resistant (higher-order) masked hardware of cryptographic S-boxes. We refer to this technique as (generic) Self-Synchronized Masking (“SESYM”). The main idea of our approach is to replace register stages with a partial dual-rail encoding of masked signals that ensures synchronization within the circuit. More concretely, we show that WDDL gates and Muller C-elements can be used in combination with standard masking schemes to design single-cycle S-box circuits that, especially in case of higher-degree S-boxes, have noticeably lower requirements in terms of area and online randomness. We apply our method to DOM-based S-boxes of Ascon and AES and compare the resulting circuits to existing latency optimized circuits based on TI, GLM, and LMDPL. The latency of all three designs is reduced to single-cycle operation and are dth-order secure. Compared to GLM-masked Ascon, our approach comes with a 6.4 times reduction in online randomness for all protection orders. Compared to 1st-order LMDPL-masked AES, our approach achieves comparable results, while it is more generic, amongst others, by also supporting higher-order designs. We also underline the practical protection of our constructions against power analysis attacks via empirical and formal verification approaches.

Integrated energy service is the main form of future energy supply. Integrated energy optimization and integration can make full use of distributed energy and renewable energy. In this paper, a set of low-latency integrated energy conversion equipment with electricity as the core have been designed to meet the comprehensive energy requirements of electricity, cooling, heating, hot water, etc. The distributed photovoltaic power generation, micro-turbine combined heat and power, electrochemical energy storage, hot water storage tanks, electric auxiliary heat, ground source heat pumps and multi-port power converter were integrated with low-latency fast local communication to achieve the coordinated control between energy portfolios. And with the lowest operating cost, maximum renewable energy utilization, and highest energy utilization efficiency as the control objects, uses power electronics converter millisecond-level high-speed response performance and inertia controllability, to support the conversion of single energy flow to multi-energy flow, ensure the reliable, economical, efficient and green operation of the user’s integrated energy system, realize multi-energy complementation, and energy cascade utilization.

Short-reach transmission systems traditionally utilize intensity modulation (IM) at the transmitter and direct detection (DD) at the receiver due to their cost-effectiveness, small footprint, and low power consumption. However, with the exponential increase in bandwidth demand, coherent optical communication systems have become necessary for long-haul distances, requiring application-specific integrated circuits (ASIC) and advanced digital signal processing (DSP) algorithms coupled with high-speed digital-to-analog and analog-to-digital converters to achieve Tbit/s speeds. As coherent technology matures, it will eventually become feasible for short-reach transmission. In this context, self-coherent systems have emerged as an intermediary solution, offering advantages over traditional IM/DD systems. While comprehensive review studies exist on self-coherent transceivers, they do not cover recent advances in phase retrieval methods for short-reach optical communications. This review article highlights recent developments in cost-effective self-coherent detection for short-reach systems through comparing the benefits of single sideband (SSB) transmission and Kramers-Kronig detection to carrier-assisted phase retrieval, the Gerchberg-Saxton (GS) algorithm, and the transport of intensity equation (TIE) method.

In the near future, 6G networks will be able to provide its users with heterogeneous tailored end-to-end network services for edge AI-powered applications. However, in many 6G use case scenarios there may not be structured network infrastructures. One of the most appealing solutions to meet this problem is to bring computing, networking and storage facilities on site by means of UAVs. The target of this paper is the definition of a management framework for Flying Ad-Hoc Networks (FANET) aimed at bringing edge computing to remote areas, guaranteeing low latency to ground devices with heterogeneous requirements in terms of mean latency. The proposed framework allows zero-touch management of different network slices realized by partitioning the Computing Elements (CE) installed on each UAV of the FANET and wireless links connecting them to each other. An Inter-Slice Orchestrator is in charge of deciding this resource partition, while an Intra-Slice Orchestrator manages horizontal offload among UAVs for each slice via Reinforcement Learning, in order to reduce end-to-end latency and delay jitter. [ABSTRACT FROM AUTHOR]

3GPP standards organization is performing an impressive effort trying to reach sub-millisecond latencies for 5G. However, such efforts may become fruitless if exogenously generated delays at transport layer are not considered. Nowadays, Radio Access Networks (RANs) are deployed with large buffers to achieve full utilization and avoid squandering wireless resources. Unfortunately, and since the data path’s bottleneck resides on the radio link, RAN’s buffers are bloated by TCP’s congestion control algorithm. Thus, a flow with low-latency requirements that encounters a bloated buffer, suffers from inevitable large sojourn times associated with the buffer depletion time, severely downgrading its Quality of Service (QoS). This paper presents different solutions for efficiently multiplexing distinct traffic patterns that share buffers on the 5G stack. Bufferbloat is extensively studied within the actual 5G QoS scenario, which presents multiple challenges inherited from the dynamic radio link nature and the presence of multiple queues at different entities. We propose and extensively emulate different algorithms in order to avoid the exogenous delay caused by the bufferbloat phenomena. We use real cellular network traces with realistic delay-sensitive and background traffic patterns in different scenarios. The outcome presents valuable insights in the algorithms that will enable low-latency services to be delivered through the 5G network stack satisfying restrictive envisioned constraints. [ABSTRACT FROM AUTHOR]

The 3rd Generation Partnership Project (3GPP) is investing a notable effort to mitigate the endogenous stack and protocol delays (e.g., introducing new numerology, through preemptive scheduling or providing uplink granted free transmission) to attain to the heterogeneous Quality of Service (QoS) latency requirements for which the fifth generation technology standard for broadband cellular networks (5G) is envisioned. However, 3GPP’s goals may become futile if exogenous delays generated by the transport layer (e.g., bufferbloat) and the Radio Link Control (RLC) sublayer segmentation/reassembly procedure are not targeted. On the one hand, the bufferbloat specifically occurs at the Radio Access Network (RAN) since the data path bottleneck is located at the radio link, and contemporary RANs are deployed with large buffers to avoid squandering scarce wireless resources. On the other hand, a Resource Block (RB) scheduling that dismisses 5G’s packet-switched network nature, unnecessarily triggers the segmentation procedure at sender’s RLC sublayer, which adds extra delay as receiver’s RLC sublayer cannot forward the packets to higher sublayers until they are reassembled. Consequently, the exogenously generated queuing delays can surpass 5G’s stack and protocol endogenous delays, neutralizing 3GPP’s attempt to reduce the latency. We address RLC’s related buffer delays and present two solutions: (i) we enhance the 3GPP standard and propose a bufferbloat avoidance algorithm, and (ii) we propose a RB scheduler for circumventing the added sojourn time caused by the packet segmentation/reassembly procedure. Both solutions are implemented and extensively evaluated along with other state-of-the-art proposals in a testbed to verify their suitability and effectiveness under realistic conditions of use (i.e., by considering Modulation and Coding Scheme (MCS) variations, slices, different traffic patterns and off-the-shelf equipment). The results reveal current 3GPP deficits in its QoS model to address the bufferbloat and the contribution of the segmentation/reassembly procedure to the total delay.

Using sensor-rich smartphones to sense various contexts attracts much attention, such as transportation mode recognition. Local solutions make efforts to achieve trade-offs among detection accuracy, delay, and battery usage. We propose a real-time recognition model consisting of two long short-term memory classifiers with different sequence lengths. The shorter one is a binary classifier distinguishing elevator scene and the longer one implements a finer classification among bus, subway, high-speed railway, and others. Light-weighted sensors are employed with a much smaller sampling rate (10Hz) compared with previous works. A two-stage setting makes it robust to scenes with different duration and therefore reduces the latency of recognition greatly. Further, the real-time system refines the classification results and attains smoothed predictions. We present experiments on accuracy and resource usage and prove that our system realizes a latency-low and power-efficient scene recognition approach by trading off a reasonable performance loss (averaged recall of 92.22%).