Machine Learning-Based Feasability Checks for Dynamic Time Slot Management

Random Access techniques are many, most of which are designed for a limited number of mobile nodes. However, in a 5G Internet of Things environment, the design of random access must accommodate a very large number of devices which are heterogeneous in nature, and packets are in general short and come in a variety of lengths. Variable lengths of short packets may waste bandwidth if each packet transmits at the beginning of a time slot. In this paper, packet squeezing allows for a packet to make a selection between aligning to the beginning and aligning to the ending of a time slot. Two or more nodes transmitting at the same time slot may not result in a collision if two conditions are met simultaneously. First, one node selects an alignment mode different from that of the remaining nodes. Secondly, the sum of the time durations of two “representative” packets is shorter than the duration of a time slot. The performance analysis demonstrates that the throughput gain due to the packet squeezing is significant. The gain of packet squeezing can be enhanced by time slot management, where a transmitting node randomly selects a set of points of Finite Projective Plane and transmits at the time slots corresponding to the points of the chosen set. Due to the rather uniform distribution of the transmitting nodes and fewer empty time slots, the gain of packet squeezing is further improved for the entire frame. Based on the simulation results and analysis, we have observed that the time slot management extends the range of packet arrival rate of peak throughput for packet squeezing. Lastly, we compare and contrast the combining of packet squeezing and time slot management with well-known random access protocols of IoT such as slotted ALOHA and NOMA, and simulate real systems to verify the results of performance analysis.

Achieving efficient spectrum usage is a major challenge in the management of a complex communication system. With multiple users having conflicting objectives who share a common spectrum, some of whom may be hostile, careful resource allocation is essential for the effective utilization of the available frequency. Conventionally, spectrum sharing is achieved via orthogonal transmission schemes whereby the available frequency band is divided into multiple tones (or bands) which are pre-assigned to all the users on a non-overlapping basis. However, such “static orthogonal spectrum sharing” approach can lead to low bandwidth utilization. In fact, various recent spectrum occupancy studies have demonstrated that a typical geographical region has wide swathes of frequencies (up to 2/3 of the allocated radio spectrum) that are not used at any given time. While the utilization of spectrum varies with time, a significant amount of spectrum is available for opportunistic wireless applications among secondary users.Spectrum-sensing cognitive radio technology allows devices to dynamically and automatically seek out and use the optimum frequencies and bandwidth. To take advantage of the unused spectrum capacity, the users dynamically adapt to the spectral environment and change transmission or reception parameters on the fly. This allows for more efficient wireless communication without causing harmful interference with legacy systems or other devices using the same frequency bands. In these systems all users are allowed to use all the tones simultaneously. In comparison with the static spectrum sharing policies, this setup offers significantly greater freedom in utilizing the spectrum. A major challenge in the development of opportunistic spectrum sharing technology is to devise efficient algorithms for the distributed management of frequency slots and transmit power.This tutorial will describe various optimization and game theoretic formulations of the dynamic spectrum management and present some recent results on its complexity, duality and approximation.

After reformulating Clark and Scarf's (1960) classical serial multi-echelon model so that the lead time between adjacent echelons is one week (period), the option to expedite between each resulting echelon is added. Thus, each week requires a decision to be made at each echelon on how many units to expedite in from the next upstream echelon (to be received immediately) and how many to regular order (to be received in one week), with the remainder detained (left as is). The model can be interpreted as addressing dynamic lead time management, in which the (remaining) effective lead time for each ordered unit can be dynamically reduced by expediting and/or extended. Use of Clark and Scarf's (1960) idea of echelon stocks reduces a complex, multidimensional stocking problem to the analysis of a series of one-dimensional subproblems. What are called top-down base stock policies, which are readily amenable to managerial interpretation, are shown to be optimal. Myopic policies are shown to be optimal in the stationary, in1nite horizon case. The results are illustrated numerically.

A New Intelligent, Dynamic and Real Time Management System of Sewerage

Dynamic Traffic Signal Timing and Predictive Traffic Management Using R-CNN and Probe Vehicle Data to Improve Urban Mobility

RETRACTED: Dynamic simulation modelling of software requirements change management system

In this paper, we derive analytical expressions to assess the performance of Time Division Duplexing (TDD) Direct Sequence-Code Division Multiple Access (DS-CDMA) systems with coherent bi-dimensional (2D) Rake receivers. Considering either hexagonal macrocellular and microcellular cross-shaped networks, we propose and evaluate different approaches to model the effects of the intercellular multiple access interference (MAl) on the performance of DS-CDMA systems with 2D Rake receivers. These results are then used on the performance comparison between coherent and optimum 2D Rake receivers. It is shown that soft handoff allows gains of 2 dB in the Signal-to-Interference-plus-Noise-Ratio (SINR) at the 2D Rake receiver output. Simulation results, ratified by theoretical upper bounds, show that the dynamic time slot allocation schemes allow considerable increase of the SINR at the receiver output with a concomitant improvement on the throughput.

One of the many techniques used to solve the University Course Timetable Problem (UCTP) is Genetic Algorithm (GA) which is a technique in the field of Evolutionary Computation. However, GA has high computational complexity due to the large number of evolutionary operators that must be performed during the evolutionary process, so it takes a long time to produce an optimal timetable. The computation time will also increase when the number of optimized variables is very large, such as in UCTP. Of course, this makes the application less reliable by users. Therefore, this article proposes a parallelization model for GA to reduce computation time in solving UCTP problems. The proposed AG is designed with a multithreading CPU scheme and implements a guided creep mutation mechanism and eliminates the recombination mechanism to reduce more computation time. The proposed system was tested and evaluated using two different UCTP datasets from the University of Technology Yogyakarta which contained 878 and 1140 lecture meetings in even and odd semesters. Unlike the previous ones, this study discusses UCTP with dynamic time slots where the duration of the lecture depends on the course credits. From the tests that have been done, it is found that the GA that was built is able to generate optimal course timetable without any clashes in a relatively fast time, that is less than 60 minutes for 1140 lecture meetings and less than 20 minutes for 878 lecture meetings. The use of the multithreading CPU model has succeeded in reducing computation time by 62% when compared to the conventional model which only uses one thread.

This paper discusses about one of the most popular technique in Evolutionary Computation, i.e., Genetic Algorithm to solve the UCTP that has dynamic teaching time slot. Duration of a lecture is determined by the course's credit which means that the structure of course timetable can be different between one day to the others. This kind of timetable structure is more complex since the flexible time slot could produce high number of schedules possibility. Hence, a flexible chromosome is developed to deal with dynamic time slot and enhanced the efficiency of the system by eliminating the crossover process which usually time consumed. A Guided Creep Mutation is proposed to act as an evolutionary operator that guides the chromosomes to find global optimum by changing certain gene value gradually in every generation. Our system successfully generated optimum schedule for 878 and 1140 courses in odd and even semester with zero collision in rooms, time, lecturers, also satisfied all soft constraints given. Based on our experiments, the system needs relatively small number of generations with few chromosomes to generate an optimum schedule.

Radio Frequency Identification (RFID) is a radio-based communication technology used to collect and identify data from tagged objects. In recent years, RFID technology has been widely applied in both industrial and daily life, driving a continuous increase in the demand for tag reading systems. In large-scale tag environments, RFID systems often encounter collisions due to simultaneous tag responses, which results in severe collisions within the same reading frame and decreases reading efficiency. The key to solving this issue lies in improving the speed and accuracy of the tag number estimation algorithm. This paper presents a novel tag number estimation algorithm that combines the Dynamic Framed Slotted ALOHA (DFSA) algorithm with Transformer and LSTM neural networks. Compared to traditional algorithms, this approach ensures more accurate tag number predictions, reduces the time consumption of the reading system, and enhances system throughput. Simulation results show that the proposed algorithm significantly improves the recognition accuracy of RFID systems while minimizing slot wastage.

At present, the rapid development of the national economy consumes a lot of mineral resources, but the national resource extraction efficiency is low. The traditional prospecting methods consume time, money, and personnel, which cannot meet the national demand for resources in a short time. Therefore, technological innovation is needed to reduce consumption in all aspects. With the continuous development of Internet of Things technology, its application is also more extensive. This paper studies the application of Internet of Things technology in mineral remote sensing monitoring. First of all, this research mainly uses the wireless communication technology in the Internet of Things to achieve data transmission and sharing in a short distance and, second, based on the remote sensing monitoring results of the mine environment to comprehensively understand the mining area and find the suitable mining mineral resources at the fastest speed; finally, the dynamic frame time slot ALOHA algorithm is used to estimate the existing data to ensure the availability of the data. The experimental data show that vector point encryption is performed on the center line of the mine at intervals of 10, 15, 20, 30, and 50 m, respectively, and the dynamic frame slot algorithm is used to calculate the appropriate mine mining curve radius. The experimental results show that the application of Internet of Things technology in mineral remote sensing monitoring can accelerate the mining of mineral resources.

With the continuous expansion and deepening of international exchanges and cooperation, language differences have become the biggest obstacle to exchanges and cooperation. At present, English translation software is the translation between natural languages made by humans on the basis of computers. As an important technical means to break through language barriers, machine translation is a software that uses computers to process spoken or written language. This paper aims to study the innovative system of English translation software, using the Internet of Things and big data algorithms to analyze some loopholes and deficiencies in the innovative system, and provide some data research for the system update. This paper proposes to analyze the innovative system of translation software based on the big data of the Internet of Things (IoT). Among them, the big data of the Internet of Things chooses the dynamic frame time slot ALOHA algorithm and the lower limit estimation algorithm. Its application in system analysis is very innovative. The system consists of a translation engine and a machine dictionary, which solves the basic problem of insufficient translation database of current translations, and plays a role in helping the English translation software innovation system to improve the accuracy of translations. The experimental results of this paper show that the translation system’s translation software achieves 61.3% of the A-target fluent translations, and 22.7% of the B-targets can translate the original text. It can be seen that English translation software has important research significance.

In this paper, we propose enhanced power management techniques for low-power UWB communications. The proposed power management scheme is performed by using a real-time beacon parsing method and a time slot assignment technique based on the WiMedia MAC standard. It includes normal hibernation mode features and also includes a dynamic power management scheme using the hardware-controlled sleep mode switching method. By adopting several hardware-based power management features, we can easily decrease the power consumption while maintaining the overall system performance unchanged.

This paper reports on the application of the Project Based Learning (PBL) methodology in the teaching of Electronics Instrumentation to undergraduate students using as e-learning platforms the well-known Moodle framework and Redmine, a project management web application. In this PBL implementation each student group develops a different measurement or data acquisition project over the course of an entire academic term, maintaining customer-provider relationships with other groups. In this context, Redmine complements Moodle by providing dynamic time tracking and task management capabilities. In addition PBL is enriched by introducing the students to the use of a project management application.

Dynamic time slot frame ALOHA algorithm is currently the most widely used anti-collision technology in radio frequency identification (RFID) system. Based on the traditional ALOHA algorithm analysis, we propose an improved algorithm. The algorithm is based on a packet adaptive ALOHA anti-collision algorithm (PA-ALOHA). First, the reader scans and counts the time slots which are randomly selected by tags, and sends it to each tag. The tags, then, adjust accordingly the time slot that enable the reader to skip idle slots and collision slots, adaptively allocate effective slots, and then identify tags rapidly. The algorithm employs packet and dynamically adjusts the frame size and other strategies, in order to reduce the time of processing slots. Simulation results show that PA-ALOHA algorithm improves the efficiency and stability of the system and reduce the transmission overhead. Especially, when the number of tags is over 1000, the algorithm throughput is still above 70% that has been greatly improved system efficiency than conventional ALOHA algorithm.