Transmission Of Delay-sensitive Data Research Articles

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

Delay sensitive data transmission optimization for energy harvesting relay satellite networks

Relay satellite network has become a promising architecture to provide rapid-response data offloading services for delay-sensitive space data. However, stochastic resources and dynamic inter-satellite contacts bring plenty of difficulties under delay constraints, which affects the transmission efficiency directly. In this paper, we first systematically analyze the data transmission process with delay guarantees and formulate a long-term stochastic optimization problem without a priori statistical knowledge. To tackle this problem, we first transform it into several sub-problems using a queue-stability-related technology. Then, we propose a transmission optimization with delay guarantees algorithm to maximize the system utility by optimizing resource allocation and channel assignment. Furthermore, we provide performance analysis to prove that the proposed algorithm can achieve long-term stability and approach near-optimal system utility. Finally, extensive simulations demonstrate the effectiveness of the proposed algorithm for system utility maximization and delay control.

Design of a Stochastic Traffic Regulator for End-to-End Network Delay Guarantees

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Since deterministic bounds are based on the worst-case traffic behavior, various frameworks for stochastic network calculus have been proposed to provide less conservative, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We design and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Two alternative implementations of the stochastic regulator are developed and compared. Numerical results are provided to demonstrate the performance of the proposed stochastic traffic regulator.

A Roadmap of Next-Generation Wireless Technology for 6G-Enabled Vehicular Networks

Ultra-reliable connectivity and delay-sensitive data transmission is the ever increasing demand for the next generation transportation system. However, substantial network congestion and long-distance data travel always create significant delays even with conventional 4G or 5G cellular technology. Situations become critical even in autonomous transportation systems in various domain-specific applications, where smart vehicles continuously generate an enormous amount of data to make optimal decisions, for example, self-driving cars, autonomous robots, and industrial transportation. Thus, the importance of beyond 5G or 6G technology in the transportation system is prominent and requires a different level of investigation. With this motivation, in this article, we briefly analyze next-generation 6G-supported technologies and their advantages on adopting 6G into the Internet of Vehicles (IoV). We also shed light on several 6G-based frameworks in IoV networks. Finally, we conclude our discussion by addressing current open research challenges and future directions of solving those challenges with 6G technology.

SPICE-IT: Smart COVID-19 pandemic controlled eradication over NDN-IoT

Internet of things (IoT) application in e-health can play a vital role in countering rapidly spreading diseases that can effectively manage health emergency scenarios like pandemics. Efficient disease control also requires monitoring of Standard operating procedure (SOP) follow-up of the population in the disease-prone area with a cost-effective reporting and responding mechanism to register any violation. However, the IoT devices have limited resources and the application requires delay-sensitive data transmission. Named Data Networking (NDN) can significantly reduce content retrieval delays but inherits cache overflow and network congestion challenges. Therefore, we are motivated to present a novel smart COVID-19 pandemic-controlled eradication over NDN-IoT (SPICE-IT) mechanism. SPICE-IT introduces autonomous monitoring in indoor environments with efficient pull-based reporting mechanism that records violations at local servers and cloud server. Intelligent face mask detection and temperature monitoring mechanism examines every person. Cloud server controls the response action from the centre with an adaptive decision-making mechanism. Long short-term memory (LSTM) based caching mechanism reduces the cache overflow and overall network congestion problem.

Fog-Based Delay-Sensitive Data Transmission Algorithm for Data Forwarding and Storage in Cloud Environment for Multimedia Applications.

Fog computing is playing a vital role in data transmission to distributed devices in the Internet of Things (IoT) and another network paradigm. The fundamental element of fog computing is an additional layer added between an IoT device/node and a cloud server. These fog nodes are used to speed up time-critical applications. Current research efforts and user trends are pushing for fog computing, and the path is far from being paved. Unless it can reap the benefits of applying software-defined networks and network function virtualization techniques, network monitoring will be an additional burden for fog. However, the seamless integration of these techniques in fog computing is not easy and will be a challenging task. To overcome the issues as already mentioned, the fog-based delay-sensitive data transmission algorithm develops a robust optimal technique to ensure the low and predictable delay in delay-sensitive applications such as traffic monitoring and vehicle tracking applications. The method reduces latency by storing and processing the data close to the source of information with optimal depth in the network. The deployment results show that the proposed algorithm reduces 15.67 ms round trip time and 2 seconds averaged delay on 10 KB, 100 KB, and 1 MB data set India, Singapore, and Japan Amazon Datacenter Regions compared with conventional methodologies.

Rendezvous agents-based routing protocol for delay sensitive data transmission over wireless sensor networks with mobile sink

The data collected by the sensor nodes will be transferred to sink in traditional wireless sensor networks. Due to the constrained energy reserves of the sensors, a transmission route should operate with minimal energy that tends to longevities network survival. A novel routing is portrayed that partitions the network area into regions and establishes rendezvous agents for the mobile sink at all of these regions and defines a method to order the areas which is followed by the mobile sink to visit the regions. The process of ordering the regions is furbished under many quality of service objectives portrayed in this manuscript. The performance of the proposed model assessed through simulation study and the same is compared with other contemporary model having similar objectives. The energy consumption efficiency under optimal packet delivery with minimal latency is the objective considered for the performance analysis.

Rendezvous agents-based routing protocol for delay sensitive data transmission over wireless sensor networks with mobile sink

Rendezvous agents-based routing protocol for delay sensitive data transmission over wireless sensor networks with mobile sink

Delay sensitive data routing optimization using rendezvous agents in wireless sensor networks with mobile sink

The method of data collection and transmission of delay sensitive data often relies on mobile sink in wireless sensor networks. This is either by transmitting directly to the mobile sink if the mobile sink is in the reachable range of the sensor that is intended to send the data, or by using the route established by the hop level sensors between the sensors that are intended to send the data and the mobile sink. The constraint of this strategy can portray as the energy consumption due to the suboptimal methods of routing between source sensor and mobile sink and the fault intolerance in data collection and transmission sequence. Hence, it is obvious to conclude that the significant contributions required about the design of optimal data collection and transmitting strategies for energy efficient and fault tolerant delay sensitive data transmission in wireless sensor networks with mobile sink. According to the context of these objectives stated, this manuscript portrayed a ‘Delay Sensitive Data Routing Optimization using Rendezvous Agents’ for energy efficient and fault tolerant delay sensitive data transmission in wireless sensor networks with mobile sink. The significance of the proposal was evaluated by experimental study using network simulation. Performance of the proposal is scaled by comparing the proposed model with the other contemporary model built under similar objectives. The traditional metrics are in use to compare and scale the performance of the proposal with the contemporary model.

An improved downlink packet scheduling algorithm for delay sensitive devices in both H2H and M2M communications in LTE-advanced networks

The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.

Energy-Efficient Routing Protocol based on Interference Awareness for Transmission of Delay-Sensitive Data in Multi-Hop RF Energy Harvesting Networks

Energy-Efficient Routing Protocol based on Interference Awareness for Transmission of Delay-Sensitive Data in Multi-Hop RF Energy Harvesting Networks

Secure Transmission of Delay-Sensitive Data Over Wireless Fading Channels

In this paper, throughput and energy efficiency of secure wireless transmission of delay sensitive data generated by random sources is studied. A fading broadcast model in which the transmitter sends confidential and common messages to two receivers is considered. It is assumed that the common and confidential data, generated from Markovian sources, are stored in buffers prior to transmission, and the transmitter operates under constraints on buffer/delay violation probability. Under such statistical quality-of-service constraints, effective capacity of time-varying wireless transmissions and effective bandwidth of Markovian sources are employed to determine the throughput. In particular, secrecy capacity is used to describe the service rate of buffers containing confidential messages. Moreover, energy per bit is used as the energy efficiency metric and energy efficiency is studied in the low signal-to-noise regime. Specifically, minimum energy per bit required for the reliable communication of common and confidential messages is determined and wideband slope expressions are identified. The impact of buffer/delay constraints, correlation between channels, source characteristics/burstiness, channel knowledge at the transmitter, power allocation, and secrecy requirements on the throughput and energy efficiency of common and confidential message transmissions is identified.

Joint Physical-Layer and System-Level Power Management for Delay-Sensitive Wireless Communications

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g., multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutions based on dynamic power management (DPM); however, there is currently no rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to achieve the minimum possible energy consumption, under delay constraints, in the presence of stochastic and a priori unknown traffic and channel conditions. In this paper, we propose such a framework. We formulate the stochastic optimization problem as a Markov decision process (MDP) and solve it online using reinforcement learning (RL). The advantages of the proposed online method are that 1) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; 2) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and 3) it obviates the need for action exploration, which severely limits the adaptation speed and runtime performance of conventional reinforcement learning algorithms. Our results show that the proposed learning algorithms can converge up to two orders of magnitude faster than a state-of-the-art learning algorithm for physical layer power-control and up to three orders of magnitude faster than conventional reinforcement learning algorithms.

Robust On-Demand Multipath Routing with Dynamic Path Upgrade for Delay-Sensitive Data over Ad Hoc Networks

Node mobility in mobile ad hoc networks (MANETs) causes frequent route breakages and intermittent link stability. In this paper, we introduce a robust routing scheme, known as ad hoc on-demand multipath distance vector with dynamic path update (AOMDV-DPU), for delay-sensitive data transmission over MANET. The proposed scheme improves the AOMDV scheme by incorporating the following features: (i) a routing metric based on the combination of minimum hops and received signal strength indicator (RSSI) for discovery of reliable routes; (ii) alocal path updatemechanism which strengthens the route, reduces the route breakage frequency, and increases the route longevity; (iii) akeep alivemechanism for secondary route maintenance which enables smooth switching between routes and reduces the route discovery frequency; (iv) apacket salvagingscheme to improve packet delivery in the event of a route breakage; and (v) low HELLO packet overhead. The simulations are carried out in ns-2 for varying node speeds, number of sources, and traffic load conditions. Our AOMDV-DPU scheme achieves significantly higher throughput, lower delay, routing overhead, and route discovery frequency and latency compared to AOMDV. For H.264 compressed video traffic, AOMDV-DPU scheme achieves 3 dB or higher PSNR gain over AOMDV at both low and high node speeds.

Fast Reinforcement Learning for Energy-Efficient Wireless Communication

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutions based on dynamic power management (DPM); however, there is currently no rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to achieve the minimum possible energy consumption, under delay constraints, in the presence of stochastic and a priori unknown traffic and channel conditions. In this report, we propose such a framework. We formulate the stochastic optimization problem as a Markov decision process (MDP) and solve it online using reinforcement learning. The advantages of the proposed online method are that (i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; (ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and (iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms. Our results show that the proposed learning algorithms can converge up to two orders of magnitude faster than a state-of-the-art learning algorithm for physical layer power-control and up to three orders of magnitude faster than conventional reinforcement learning algorithms.