Time Division Multiple Access Cycle Research Articles

Improved Obstacle Mitigation and Localization Accuracy in Narrowband Ultrasonic Localization Systems Using RoBCUL Algorithm

This article develops a method for mitigating the negative effects of obstacles in narrowband, time-division multiple access (TDMA), ultrasonic localization systems. The method builds upon the robust Bayesian classifier for ultrasonic localization (RoBCUL) algorithm that utilizes an iteratively reweighted least-squares (IRLS) scheme. This algorithm has the advantage of low computational cost but loses performance in the presence of obstacles. The improved version of the RoBCUL algorithm presented in this article uses a statistical test applied after each iteration of the regression, using a weighted-residual vector calculated from the weight matrix and residual vector. The technique was tested using experimental data with its performance being quantified by its ability to correctly classify all the signals received during a single TDMA cycle. The extended version performed significantly better in all obstacle scenarios than the original, correctly classifying 100% of TDMA cycles in the scenarios with no obstacles, 99.9% with one obstacle, and 98.7% with two obstacles.

Underwater Acoustic Sensor Node Scheduling using an Evolutionary Memetic Algorithm

Underwater Acoustic Sensor Networks (UWASNs) play an important role in monitoring the aqueous environment which has created a lot of interest for researchers and scientists. Utilization of underwater acoustic sensor node (UASN) scheduling for transmission remains, due to the limited acoustic bandwidth available, a challenge in such an environment. One of the methods to overcome this problem is to efficiently schedule UASN data using time division multiple access (TDMA) protocols the parallel transmissions, simultaneously avoiding interference. The paper shows how to optimize the utilization of acoustic sensor node bandwidth by maximizing the possible node transmissions in the TDMA frame and also by minimizing the node's turnaround wait time for its subsequent transmissions by using an evolutionary memetic algorithm (MA). The simulation of MA-TDMA proves that as the size of the network increases, every node in UWASN transmits with an average minimal turnaround transmission time. It also proves that as the TDMA cycle repeats, the overall network throughput gets maximized by increasing the possible node transmissions in the MA-TDMA frame.

An evolutionary algorithm for broadcast scheduling in wireless multihop networks

A technical challenge in successful deployment and utilization of wireless multihop networks (WMN) are to make effective use of the limited channel bandwidth. One method to solve this challenge is broadcast scheduling of channel usage by the way of time division multiple access (TDMA). Three evolutionary algorithms, namely genetic algorithm (GA), immune genetic algorithm (IGA) and memetic algorithm (MA) are used in this study to solve broadcast scheduling for TDMA in WMN. The aim is to minimize the TDMA cycle length and maximize the node transmissions with reduced computation time. In comparison to GA and IGA, MA actively aim on improving the solutions and is explicitly concerned in exploiting all available knowledge about the problem. The simulation results on numerous problem instances confirm that MA significantly outperforms several heuristic and evolutionary algorithms by solving well-known benchmark problem in terms of solution quality, which also demonstrates the effectiveness of MA in efficient use of channel bandwidth.

Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network

In this paper we describe fuzzy Hopfield neural network (FHNN) technique to solve the TDMA (time division multiple access) broadcast scheduling problem in wireless sensor networks (WSN). We formulate it as discrete energy minimization problem and map it into Hopfield neural network with the fuzzy c -means strategy to find the TDMA schedule for nodes in a communication network. The broadcast scheduling problem for wireless sensor networks is an NP-complete problem. Each time slot is regarded as a data sample and every node is taken as a cluster. Time slots are adequately distributed to the dedicated node while satisfying the constraints. The aim is to minimize the TDMA cycle length and maximize the node transmissions avoiding both primary and secondary conflicts. The FHNN reduces the processing time and increases the convergence rate for Broadcast Scheduling Problem. Simulation results show that the FHNN improves performance substantially through solving well-known benchmark problems.

A heuristic algorithm for optimum transmission schedule in broadcast packet radio networks

Packet Radio networks are to provide data communications among a set of nodes distributed over a wide region. Transmission from nodes is broadcast in nature. Where direct communication between two nodes is not possible, connection is established in multiple hops. A time division multiple access (TDMA) protocol is adopted for conflict free communication among different nodes. The goal is to find a conflict free transmission schedule for different nodes at different time-slots of a fixed length time frame, called TDMA cycle. The optimization criterion is primarily to (1) minimize the TDMA cycle length, and then to (2) maximize the number of transmissions. The problem is proved to be NP-complete. A randomized algorithm is proposed, which is very efficient to achieve the first optimization criterion. The results are shown to be superior compared to other recently reported competitive algorithms.