Fast Data Delivery Research Articles

Optimization of data transmission in sensor networks for enhanced control of ozonator efficiency

The main object of the research is the efficiency of real-time ozonator control based on sensor networks. The study addressed the issue of low efficiency in ozonator control systems and the lack of reliability and speed in real-time data transmission. The research revealed that changes in pressure and temperature have a direct impact on ozone concentration. This finding made it possible to increase the ozonator's productivity by 15 %, reduce energy consumption by 10 %, and improve system reliability by 20 %. The key features of the results include the ability to monitor ozone levels in real-time, maintaining the stability of the ozonator, and optimizing its performance. Additionally, sensor networks ensured fast and accurate data delivery, enhancing the energy efficiency and reliability of the system. These results were explained based on experimental data that demonstrated how changes in pressure and temperature affect ozone concentration. The use of sensor networks contributed to increased system stability, reduced energy consumption, and improved control accuracy. The obtained results can be applied to ozonator systems and other fields requiring real-time environmental monitoring and control. The methods proposed in the study provide opportunities for optimizing industrial processes, reducing costs, and achieving sustainable development goals

Analytical study of Restricted Access Window with short slots for fast and reliable data delivery from energy-harvesting sensors

To address the peculiarities of various Internet of Things applications and improve the performance of Wi-Fi-based sensor stations, a recent Wi-Fi HaLow standard introduces a mechanism called Periodic Restricted Access Window (PRAW). PRAW allows mapping groups of stations to periodic time intervals during which only these stations can transmit while the others cannot. This approach reduces contention and saves energy. However, the standard does not provide any recommendations on how to select the PRAW parameters according to scenario-specific requirements, leaving room for the research. A typical IoT scenario considers low-intensity traffic and requires fast and reliable data delivery from energy-harvesting devices while using the channel efficiently: there shall be still time for transmissions of other stations in the network. This paper develops an analytical model of PRAW that evaluates the probability of delivering data in time from every sensor station that harvests energy, accumulates it in small capacitors, and, thus, may not have enough energy for multiple retries. The model allows selecting such parameters of PRAW that minimize the channel resource consumption of a Wi-Fi HaLow network, provided that the delay and energy requirements are satisfied.

Fractional Bandwidth up to 24% and Spurious Free SAW Filters on Bulk 15°YX-LiNbO3 Substrates Using Thickness-Modulated IDT Structures

To cope with ubiquitous wireless connectivity and the increased and faster data delivery in 5G communication, surface acoustic wave (SAW) filters are progressively requiring wider bandwidths. Conventional bulk 15°YX-LiNbO3 substrates with a large coupling coefficient (K2) are attractive for the low-cost mass production of wideband SAW filters, but these generally suffer from spurious responses, limiting their practical application. In this work, a novel and simple SAW configuration is proposed that uses thickness-modulated interdigital transducer (IDT) structures to overcome the limitations set by spurious responses. Different from the conventional design where the thicknesses of the IDT electrodes in the series and parallel resonators generally kept the same, the proposed configuration adopts IDT electrodes of different thicknesses in the series and shunt resonators to suppress or remove unwanted spurious Rayleigh modes from the filter passband. Two different ultra-wideband SAW filter designs employing thickness-modulated IDTs were designed and fabricated to validate the effective suppression of spurious modes. The SAW filters experimentally featured spurious-free responses in the passband as well as a large 3 dB fractional bandwidth (FBW) in the 18.0% and 24.1% ranges and low insertion losses below 1 dB. This work can significantly broaden the range of applications for SAW devices and can open a pathway to commercialize ultra-wideband SAW filters in 5G communication systems.

Learning-Based FEC for Non-Terrestrial Networks With Delayed Feedback

Packet-level forward erasure correction (FEC) is effective for achieving low-latency transmissions in non-terrestrial networks (NTNs), which often contain lossy links with long propagation delay. This letter considers the problem of dynamically sending FEC coded packets based on delayed feedback to achieve fast in-order data delivery without retransmissions. We show that the problem can be formulated using reinforcement learning (RL) by tracking history feedback and system state. With carefully devised state space and reward signal, the RL scheme is shown to be effective for achieving high and smooth in-order goodput with low delay. For example, smooth goodput can be achieved at a FEC code rate of more than 90% of the capacity with the delay of all the packets being lower than 1.5 round-trip time, which would be incurred by a single packet loss if using retransmissions.

AI-Based Communication-as-a-Service for Network Management in Society 5.0

This paper explores the concept of AI-based Communication-as-a-Service (ACUTE) to reduce transmission delay and energy consumption, while transmitting data from end-devices to the cloud in the context of 5.0. 5.0 revolutionizes connected living with the help of a unified system that provides fully automated and end-to-end services, while addressing the demands of all the citizens or users in a society. On the other hand, 6G is one of the promising communication platforms that offers the communication requirements of 5.0 by provisioning dense network deployment and fast data delivery. Building 5.0 founded on the 6G architecture enables serialized data transmission in the connected living fabric by allowing a user to connect with an access point and transmit data over a single path. Without concurrent and intelligent data transmission, the communication framework of Society 5.0 increases network delay and overall energy consumption and affects the Quality-of-Service (QoS). To address these issues, we propose a solution founded on the concept of Communication-as-a-Service (CaaS), which offers an architecture to facilitate intelligent access point virtualization for enabling concurrency in data transmission across individual users in a 6G-enabled 5.0. In ACUTE, a virtual module (VM) employed at each edge device performs concurrent data transmissions by associating with a virtual access point (VAP), which is a set of access points optimally selected using Fuzzy C-Means. Thereafter, the VM forms a virtual path (VP), which maps to a set of paths between physical access points and VAPs. ACUTE distributes the data through the VAP and associated VP and randomizes data sequence for transmission across VP. Experimental results show that ACUTE outperforms the state-of-the-art while reducing the network delay by 27%, energy consumption by 95%, packet loss by 95%, and service cost by 26%.

The ATLAS Data Carousel Project Status

The High Luminosity upgrade to the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions at an energy of 14 TeV, is expected to start operation in 2028/29 and will deliver an unprecedented volume of scientific data at the multi-exabyte scale. This amount of data has to be stored, and the corresponding storage system must ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data management model will not be able to provide the required infrastructure growth, even taking into account the expected hardware technology evolution. To address this challenge, the Data Carousel R&D project was launched by the ATLAS experiment in the fall of 2018. State-of-the-art data and workflow management technologies are under active development, and their current status is presented here.

Prototype of the Russian Scientific Data Lake

The High Luminosity phase of the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions is expected to start operation in eight years. An unprecedented scientific data volume at the multiexabyte scale will be delivered to particle physics experiments at CERN. This amount of data has to be stored and the corresponding technology must ensure fast and reliable data delivery for processing by the scientific community all over the world. The present LHC computing model will not be able to provide the required infrastructure growth even taking into account the expected hardware evolution. To address this challenge the Data Lake R&D project has been launched by the DOMA community in the fall of 2019. State-of-the-art data handling technologies are under active development, and their current status for the Russian Scientific Data Lake prototype is presented here.

Priority-Aware Fast MAC Protocol for UAV-Assisted Industrial IoT Systems

Many hazardous industrial incidents can occur due to the inadequate and inefficient monitoring of the offshore plants. Manual inspections of the offshore plants on a regular basis is not only time consuming but also dangerous regarding to human safety. For considering the safety measurement and alleviating the burden of the manual inspection, unmanned aerial vehicles (UAVs) can be effectively utilized to collect data from the remote industrial environment. In an industrial scenario, less delay is required for emergency packets and high throughput is needed for monitoring packets. This paper proposes a priority-aware fast MAC (PF-MAC) protocol for UAV-assisted industrial Internet-of-things (IIoT) systems, ensuring fast and robust data delivery. At first, the IoT devices under the UAV communication range transmit a reservation frame to the UAV to catch transmission opportunities using CSMA/CA. The devices utilize static traffic priority and a novel adaptive backoff mechanism during CSMA/CA. After receiving the reservation frames from the IoT devices, the UAV calculates the dynamic device priority based on their static traffic priority, communication duration, sampling frequency, and remaining energy. Then, time slot is assigned by the UAV to each device for data transmission. To ensure fairness, if a device fails in contention during the CSMA/CA period, the static traffic priority is raised in the next retransmission. There is no prior work in the literature that considers both the traffic priority and the device priority to ensure Quality of Service in IIoT and related systems. According to our performance study, the proposed PF-MAC outperforms the conventional protocols in terms of delay and throughput.

Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing

AbstractThe requisition for electrical energy, smart grid, and renewable energy paradigm extend a new space for Electrical Energy Data Management and Processing Systems (EEDMS), in such a way that can mitigate the consumption of electrical energy. Similarly, the implementation and maintenance of the EEDMS is a challenging task. Moreover, the heterogeneous energy data generated from residential and commercial sector are the leading challenges for standard Internet of Things (IoT) architecture. This contributes enormous energy data preprocessing and analyzing solutions to IoT landscape. To overcome these challenges, we present a scalable multitasking Internet of Things Gateway (IoTGW) for the modern era of IoT by placing reliance on a new entity called Data Loading and Storing Module (DLSM). The provided DLSM module combine with the Gateway module services like orchestrator, flexibility of bridging front end grid, back end grid and fast formatted data trade between sensing domain and application domain enables a high dynamic distributed framework. Specifically, we add Adaboost‐Multilayer Perceptron hybrid data classifier module to the proposed work to enhance service provision of IoT gateway toward various IoT application services and protocols to facilitate IoT demands such as multitasking, interoperability, classification, and fast data delivery between different modules. IoTGW is implemented and tested using a real‐time IoT data streaming network. The experimental results confirms the superiority of proposed work in terms of scalability to serve novel applications and facilitate broad scope of IoT.

A Reputation Value-Based Early Detection Mechanism Against the Consumer-Provider Collusive Attack in Information-Centric IoT

As the Internet of Things (IoT) has connected large number of devices to the Internet, it is urgently needed to guarantee the low latency, security, scalable content distribution of the IoT network. The benefits of Information-Centric Networking (ICN) in terms of fast and efficient data delivery and improved reliability have raised ICN as a highly promising networking model for IoT environments. However, with the widely spread of the viruses and the explosion of kinds of network devices, the attackers can easily control the devices to form a botnet such as the Mirai. Once the devices are under control, the attackers can launch a consumer-provider collusive attack in the Information-Centric IoT context. In this attack, the malicious clients issue Interest packets that can only be satisfied by the malicious content provider, and the malicious provider replies to the clients just before exceeding the Pending Interest Table entry’s expiration time, to occupy the limited resources. In this paper, we expound the model of the consumer-provider collusive attack and analyze the negative effect of the attack. Then we propose a Reputation Value based Early Detection (RVED) mechanism to relieve the impact of the collusive attack. The method aims to adjust the packet dropping rates of different interfaces based on their reputation value, thus to protect the legitimate packets from being dropped as possible. We implement the consumer-provider collusive model and evaluate our defend mechanism in the simulator, and simulation results verify the feasibility and effectiveness against the collusive attack of the RVED mechanism.

Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network

The unattended time-sensitive mobile sink(s) sensor network applications not only require longer network lifespan but also require fast and reliable data delivery to sink within bounded time to take preventive measures. In these applications, high sink mobility in large dense network may lead an overhead in terms of traffic load, collision, packet loss, delay and rapid energy consumption of nodes. Most of the routing schemes developed for time-sensitive applications do not consider all these issues in a single protocol and most of the protocols are developed for homogeneous sensor network environment. Whereas, many of these applications demand heterogeneous sensors because of the node heterogeneity benefits such as enhanced network lifetime and reliability. So, we have proposed a novel delay aware energy efficient reliable routing (i.e. DA-EERR) technique for data transmission in heterogeneous sensor environment. In the proposed scheme, restricted search space is defined for guaranteeing timely delivery of delay sensitive data. Furthermore, within the defined search space, an algorithm is developed to select an energy delay balanced path between source and sink which enables fast data delivery through an energy efficient hop. The proposed DA-EERR scheme also improves percentage of successfully received data packets at sink in large dense network by using data aggregation and by providing proper load balancing across the network.

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Information-centric networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current Internet protocol-address-based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, and caching the content at intermediate nodes to provide reliable, efficient data delivery, and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoT) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoT attracts both industry and research communities. IoT is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoT by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification. Furthermore, we present operating systems and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoT.

Autonomous Cache Resource Slicing and Content Placement at Virtualized Mobile Edge Network

Nowadays, the unprecedented growth of mobile traffic is only rivaled by the demands for a better quality of service and faster data delivery. Caching the content closer to the end users is one solution to cope with these demands, but when service providers share a common physical infrastructure, managing the limited cache resources becomes more challenging. With this increased complexity, the deep reinforcement learning (DRL) presents itself as an optimal solution. In this work, we propose a dynamic DRL-based management framework for virtual cache slicing and customized content placement. The virtualization problem is formulated as a Markov decision process, and it utilizes a deep Q-network to autonomously make network-wide slicing decisions and optimize the cache resource allocation. These global decisions are then mapped to the small cell stations with a dynamic resource provisioning algorithm. Lastly, a customized content placement algorithm is used to find the optimal content placement policy for each service provider. The content placement problem is first formulated as a convex optimization problem and is then solved with a distributed alternating direction method of multipliers (ADMM). Finally, the simulation results are presented to show the effectiveness of the proposed solution.

Fast Data Delivery for Many-Core Processors

Server workloads operate on large volumes of data. As a result, processors executing these workloads encounter frequent L1-D misses. In a many-core processor, an L1-D miss causes a request packet to be sent to an LLC slice and a response packet to be sent back to the L1-D, which results in high overhead. While prior work targeted response packets, this work focuses on accelerating the request packets. Unlike aggressive OoO cores, simpler cores used in many-core processors cannot hide the latency of L1-D request packets. We observe that LLC slices that serve L1-D misses are strongly temporally correlated. Taking advantage of this observation, we design a simple and accurate predictor. Upon the occurrence of an L1-D miss, the predictor identifies the LLC slice that will serve the next L1-D miss and a circuit will be set up for the upcoming miss request to accelerate its transmission. When the upcoming miss occurs, the resulting request can use the already established circuit for transmission to the LLC slice. We show that our proposal outperforms data prefetching mechanisms in a many-core processor due to (1) higher prediction accuracy and (2) not wasting valuable off-chip bandwidth, while requiring significantly less overhead. Using full-system simulation, we show that our proposal accelerates serving data misses by 22 percent and leads to 10 percent performance improvement over the state-of-the-art network-on-chip.

Harnessing the GPS Data Explosion for Interdisciplinary Science

More GPS stations, faster data delivery, and better data processing provide an abundance of information for all kinds of Earth scientists.

Performance of highly connected photonic switching lossless metro-access optical networks

The present work analyzes the performance of photonic switching networks, optical packet switching (OPS) and optical burst switching (OBS), in mesh topology of different sizes and configurations. The “lossless” photonic switching node is based on a semiconductor optical amplifier, demonstrated and validated with experimental results on optical power gain, noise figure, and spectral range. The network performance was evaluated through computer simulations based on parameters such as average number of hops, optical packet loss fraction, and optical transport delay (Am). The combination of these elements leads to a consistent account of performance, in terms of network traffic and packet delivery for OPS and OBS metropolitan networks. Results show that a combination of highly connected mesh topologies having an ingress e-buffer present high efficiency and throughput, with very low packet loss and low latency, ensuring fast data delivery to the final receiver.

Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast message, whereas the individual sensor nodes replies back to sink as unicast messages. In the proposed work, the fuzzy approach and “A Star” algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of message transmissions in the network. For the unicast message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor node’s lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.

Multi-criteria Routing in a Partitioned Wireless Sensor Network

Energy efficiency is one of the key challenges for designing routing algorithms for wireless sensor networks (WSN). Fast data delivery from source node to sink node is also required for many applications. Sometimes data transmission towards the sink may be interrupted because of failure of nodes in a particular area. The various such requirements of routing data in a WSN are sometimes contradictory and routing algorithms must be designed keeping in mind all these requirements. In this paper three routing algorithms are proposed for multi-sink partitioned network. In these three algorithms, the source nodes or intermediate nodes select a next node to forward the data to the destination or sink. This process repeats until the data reaches the sink. In the first technique, the next node is chosen considering its distance from sink node as a criterion. In the second technique, remaining energy of the neighboring nodes is used as a criterion for selection of the next node. The combination of these two criteria is considered in the third technique and a multiple criteria decision analysis technique is used. A recovery technique from area failure is also proposed and incorporated in the above mentioned algorithms. The algorithms are designed and implemented in multi-sink environment for partitioned wireless sensor networks. Performances of the algorithms are studied in a simulation environment and compared with other well-known algorithms to understand their effectiveness.

Fast approximate 1D modelling and inversion of transient electromagnetic data

ABSTRACTA novel, fast, and approximate forward modelling routine for time‐domain electromagnetic responses is presented. It is based on the separation of the forward problem into a configuration‐independent part, mapping conductivity as a function of depth onto apparent conductivity as a function of time, and a configuration‐dependent part, i.e., the half‐space step response. The response of a layered model is then found as the half‐space response for a half‐space conductivity equal to the apparent conductivity. The mapping is ten times faster than traditional accurate forward modelling routines, and through stochastic modelling, it is found that the standard deviation of the modelling error is 0.7 %. The forward mapping lends itself to integration in a modern state‐of‐the‐art inversion formulation in exactly the same way as traditionally computed responses, and a field example is included where inversion results using the approximate forward response are compared with those of an accurate forward response for helicopterborne transient electromagnetic data. In addition to being used in its own right in inversion of transient data, the speed and accuracy of the approximate inversion mean that it is well suited for quality control and fast turnaround data delivery of survey results to a client. It can also be used in hybrid inversion formulations by supplying initial iterations and high‐quality derivatives in an inversion based on accurate forward modelling.