Frame Size Selection Research Articles

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

A novel-Q DFSA algorithm for passive RFID system

The tag detection ability of Passive Radio Frequency Identification (RFID) systems are critically challenged by the collision occurrence due to simultaneous responding tags during the identification process. The dynamic scheduling of the frame size governed by Dynamic Frame Size ALOHA(DFSA) process, by adjusting the frame lengths according to the size of tag population can avoid the collisions during the identification. However, the performance of DFSA majorly depends on the frame size selection policy which in previous studies was adopted to achieve the target of throughput maximization during a frame. This condition is obtained at the cost of equating the frame size up to the number of estimated tags responding during the time frame. This approximation enhances the throughput value but contributes to massive energy wastages as the frame lengths approach to a very large value in large tag population size. Therefore, it is essential to develop the new frame size estimation policy for DFSA achieving the aim of optimization between throughput and energy for improved time and energy performance. In this paper, we have proposed an EPC C1G2 standards based Novel-Q DFSA algorithm which optimizes the frame size accounting both the energy and the throughput. The combined throughput and energy trade-offs are measured through Energy–Time-Delay (ET) cost which is minimum for our proposed algorithm compared to the existing solutions. Furthermore, the Throughput-Time Delay product approves the stability in large population size making it suitable for numerous identification applications.

Algorithm for Deriving Optimal Frame Size in Passive RFID UHF Class1-Gen2 Standard Using Combinatorial Model Boundaries

MAC level efficiency constraints of the RFID Reader-Tag communication require proper frame size selection in order to achieve maximum throughput. To set optimal frame size, one needs to estimate the number of tags. Deriving optimal frame size results in reducing time for identification of large RFID tag populations, and thus increases throughput of RFID system. In accordance with the standard Dynamic Framed Slotted ALOHA (DFSA) protocol, we propose model for frame size adaptation using number of collisions (C) and successful slots (S) within the frame length L. We found that expected number of tags N , linearly depends on S in the frame of length L, for fixed C. Proposed model for estimation of the number of tags can be described with 4 characteristic points, which can be calculated from proposed combinatorial model. Algorithm implementation in the first step estimates number of tags, and then in the second step, using the estimates, set the optimal frame size. In order to compare proposed system performance with the other approaches, we use standard measures, System Efficiency and Collision Ratio. Simulation results show that our method provide better performance than the Q-selection algorithm, specified within the UHF-Class1-Gen2 standard.

Frame size selection in CSMA‐based cognitive radio wireless local area networks

AbstractThe performance of cognitive radio‐based wireless local area networks (WLANs) is affected by channel conditions, the contention processes of unlicensed/secondary users (SUs) and collisions between SUs and licensed/primary users (PUs). Given these effects, it is feasible to adjust the frame size to maximise goodput. Existing frame size adjustment mechanisms for WLANs are based on the assumption that optimal frame size depends on either the channel conditions or the contention among nodes. These approaches do not consider the collisions between SUs and PUs. Thus, they lead to suboptimal solutions and cannot be directly applied to cognitive radio‐based WLANs. In this paper, an analytical model to determine the optimal frame size for carrier sense multiple access/collision avoidance‐based cognitive radio networks based on the probability of frame error, the probability of collisions among SUs and the probability of collision between SUs and PUs is proposed. Numerical and simulation results indicate that selecting the optimal frame size significantly improves goodput. Copyright © 2014 John Wiley & Sons, Ltd.

Energy Efficient Tag Estimation Method for ALOHA-Based RFID Systems

Radio Frequency Identification (RFID) technology has become an important tool for items identification and tracking. Its rapid deployment in dynamic industry areas enables variety of new applications in behavioural analysis and processes automation. In such systems it is crucial to identify all RFID tags before they leave the interrogation area. To accomplish fast identification, it is necessary to estimate a number of tags which are in the reader interrogation area. In this paper we revisit the problem of tag quantity estimate and adapting frame size of Dynamic Frame Slotted ALOHA (DFSA) widely used as RFID Medium Access Control (MAC) mechanism. The main disadvantage in the implementation of the state-of-art estimation algorithms includes the number of required computations, along with the temporary storage of large numbers which appear before estimation. As a consequence, such algorithms are energy inefficient and may require specific computer architecture to support the calculus. In order to address stated issues, we present Improved Linearized Combinatorial Model (ILCM) algorithm for optimal frame size selection which, due to linear property of the estimator, allows significant reduction in the estimate computation with acceptable trade-off in accuracy. Simulations analyse the required number of slots to identify tag population and the Floating Point Operation (FLOP) costs required to compute estimate. In addition, to emphasize the importance of reducing computational cost, we give a case study which compares energy consumed by the mobile RFID reader processor to compute the estimate and its energy equivalent of the required radio front-end energy to identify tags.

Algorithm for Deriving Optimal Frame Size in Passive RFID UHF Class1-Gen2 Standard Using Combinatorial Model Boundaries

MAC level efficiency constraints of the RFID Reader-Tag communication require proper frame size selection in order to achieve maximum throughput. To set optimal frame size, one needs to estimate the number of tags. Deriving optimal frame size results in reducing time for identification of large RFID tag populations, and thus increases throughput of RFID system.In accordance with the standard Dynamic Framed Slotted ALOHA (DFSA) protocol, we propose model for frame size adaptation using number of collisions (C) and successful slots (S) within the frame length L. We found that expected number of tags N, linearly depends on S in the frame of length L, for fixed C. Proposed model for estimation of the number of tags can be described with 4 characteristic points, which can be calculated from proposed combinatorial model. Algorithm implementation in the first step estimates number of tags, and then in the second step, using the estimates, set the optimal frame size.In order to compare proposed system performance with the other approaches, we use standard measures, System Efficiency and Collision Ratio. Simulation results show that our method provide better performance than the Q-selection algorithm, specified within the UHF-Class1-Gen2 standard.

The realization of arbitrary downsizing video transcoding

In order to transmit the precoded bit stream through bandwidth constrained network, transcoding technique has been introduced to reduce the overall bit rate generated. Downsizing transcoding is one of the commonly used methods since it can reach very low bit rate without losing any motion information. Many efforts have been devoted to this research area. With the proposal of arbitrary downsizing, fine gradual reduction of bit rate and video quality becomes feasible. However, there is little research focusing on frame size selection for arbitrary downsizing transcoding. In this letter, the realization of arbitrary downsizing transcoding is discussed. Rate estimation scheme is applied on video sequences to choose the suitable frame size for the target bit rate. An efficient method is proposed to estimate the number of bits allocated to residue in requantization process. Experimental results show that, it can provide an accurate estimation and make a reliable decision in frame size selection.