Time Slot Allocation Algorithm Research Articles

Research on Teaching Resource Management in Colleges and Universities Based on Multiple Data Chain Networks

Abstract In this paper, we first take the datalink network as the entry point and construct a university teaching resource management model based on a multivariate datalink network. A Link-22 data chain time synchronization algorithm with KF-RTPT is proposed to address the actual existence and unavoidable time deviation based on accuracy and timestamp acquisition errors. Meanwhile, a time slot allocation and optimization algorithm designed to maximize message transmission benefits is being developed to optimize the allocation of node time slot requirements. Finally, the constructed teaching resource management model is empirically analyzed from three perspectives: error optimization, dynamic time slot allocation, and model application effect. According to the findings, the KF-RTPT algorithm increases motion error efficiency by 3.83 in low-speed motion states and 8.16 in high-speed motion states when compared to the conventional RTT method. This effectively eliminates motion error and boosts the effectiveness of teaching and managing resources in colleges and universities.

Retracted: Financial and Economic Sequence Forecasting Based on Time Slot Allocation Algorithm

Retracted: Financial and Economic Sequence Forecasting Based on Time Slot Allocation Algorithm

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

Financial and Economic Sequence Forecasting Based on Time Slot Allocation Algorithm

Financial and economic series prediction was very important for the study of economic time series. Because the traditional time series analysis method was mainly suitable for stable data and cannot be applied to unstable economic variables, this paper proposed a knowledge model of financial and economic series prediction time slot algorithm, which provided the basis for solving unstable financial data. Firstly, based on the analysis of relevant financial and economic series prediction methods at home and abroad, this paper applied the time slot allocation algorithm to economic series analysis and prediction and designed a path level time slot allocation algorithm, which introduced a fixed time slot to shorten the time required to complete the path level time slot allocation. Secondly, this paper abstracted the calculation of slot allocation as a convex optimization problem and proposed a round slot allocation algorithm based on constraints and optimal solution. The algorithm can improve the end-to-end throughput and the effective utilization of slots. Finally, the model proposed in this paper was used to predict the indicators of relevant industries, and the simulation results were compared with the standard data. The experimental results showed that the financial and economic series prediction system based on time slot allocation algorithm proposed in this paper can effectively improve the accuracy of financial and economic series prediction, which verified the effectiveness and feasibility of this model.

5G Wireless Networking Connection and Playback Technology Assist the Low-Latency Propagation of New Media

This article conducts an indepth study on the delay caused by time division multiple access (TDMA) technology and theoretically analyzes some methods that can reduce the TDMA delay. Traditional dynamic time slot allocation algorithms usually only consider the completion of conflict-free time slot allocation in distributed scenarios, but they do not clearly specify the order of time slot allocation. The order of time slots allocated to each node is ultimately consistent with the new data flow. The order of media dissemination is not the same. Aiming at the scheduling delay problem caused by the inconsistency of the new media low-latency propagation time slot allocation sequence and the data stream sequence, a protocol using the master time slot adaptive time slot exchange technology is proposed. The protocol designs the corresponding super frame structure and realizes the neighbor node discovery strategy and the time slot allocation based on the priority list. At the same time, the time slot switching technology is used to adjust the time slot sequence so that it tends to the data flow sequence. The exchange criterion based on the low-latency propagation data stream value of the new media is designed to solve the problem of optimizing the time slot of multiple data streams in the network. Through the simulation results and analysis, it can be seen that the architecture design proposed in this paper can fulfill the expected requirements of the wireless Mesh network and can achieve good low-latency performance for the highly dynamic network topology. It can also achieve good performance in terms of network throughput and data flow delivery rate, and it has adaptability to high dynamic topologies. By comparing with the traditional algorithm design, the design proposed in this paper has a large improvement in low latency and high submission rate. Therefore, it can be considered that the low-latency architecture design proposed in this article has better performance for new media’s low-latency propagation and highly dynamic network topology.

DeepBAN: A Temporal Convolution-Based Communication Framework for Dynamic WBANs

Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.

Joint time‐slot and power allocation algorithm for data and energy integrated networks supporting internet of things (IoT)

SummaryIoT is an essential enabler for smart cities and smart society. However, its deployment at large scale faces a big challenge: battery replacement as most IoT devices are battery‐powered or even battery‐less. In a hostile environment, it is infeasible to replace batteries. Radio frequency (RF)‐enable wireless energy transfer (WET) is a promising technology to solve this problem. Since RF is also used for wireless data communication, a data and energy integrated network (DEIN) is the way forward. Based on the DEIN technology, a time allocation model is designed in this paper to manage the RF energy and uplink data transmission in different time slots. In the IoT scenario, the DEIN's primary service is to collect environmental information such as temperature, humidity, and luminance. Therefore, the uplink data transmission of the battery‐powered/battery‐less IoT nodes deserves more attention. To increase the uplink data transmission in case of consuming less energy in the DEIN system, we propose a joint time slot and power allocation algorithm to minimize the system's consumed energy for transmitting per bit of uplink data. It aims to maximize the efficiency of the DEIN system's energy utilization, which helps to achieve an energy‐efficient DEIN.

Random Shifting Intelligent Reflecting Surface for OTP Encrypted Data Transmission

In this letter, we propose a novel encrypted data transmission scheme using an intelligent reflecting surface (IRS) to generate secret keys in wireless communication networks. We show that perfectly secure one-time pad (OTP) communications can be established by using a simple random phase shifting of the IRS elements. To maximize the secure transmission rate, we design an optimal time slot allocation algorithm for the IRS secret key generation and the encrypted data transmission phases. Moreover, a theoretical expression of the key generation rate is derived based on Poisson point process (PPP) for the practical scenario when eavesdroppers’ channel state information (CSI) is unavailable. Simulation results show that employing our IRS-based scheme can significantly improve the encrypted data transmission performance for a wide-range of wireless channel gains and system parameters.

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Resource Allocation of Uplink for Multibeam Satellite Based on MF-TDMA

In this article, Flexible Joint Time Slot and Power Allocation (FTPA) algorithm considering interference is used for system resources allocation. Combined with the actual situation of satellite communication, the demand supply variance minimization is used as the objective function to analyze and solve the resource allocation problem reasonably. The Lagrangian dual and sub-gradient algorithm are used to replace the traditional method to improve the problem, and the optimal system allocation under the current situation is obtained. Compared with classical algorithm, FTPA guarantees the maximum capacity of the system and the fairness between beams.

A novel superframe structure and optimal time slot allocation algorithm for IEEE 802.15.4–based Internet of things

IEEE 802.15.4 standard is specifically designed for a low-rate and low-processing Internet of things (IoT) applications and offers guaranteed time slots. A beacon-enabled IEEE 802.15.4 consists of a superframe structure that comprises of the contention access period and contention-free period. During contention-free period, nodes transfer their data using guaranteed time slots without any collision. The coordinator node receives data transmission requests in one cycle and allocates guaranteed time slots to the nodes in the next cycle. This allocation process may cause large delay that may not be acceptable for few applications. In this work, a novel superframe structure is proposed that significantly reduces guaranteed time slots allocation delay for the nodes with data requests. The proposed superframe structure comprises of two contention access periods and one contention-free period, where contention-free period precedes both contention access periods with reduced slot size. In addition, the knapsack algorithm is modified for better guaranteed time slots allocation by allowing more guaranteed time slots requesting nodes to send their data as compared to the IEEE 802.15.4 standard. The simulation and analytical results show that the proposed superframe structure reduces the network delay by up to 80%, increases contention-free period utilization up to 50%, and allocates guaranteed time slots up to 16 nodes in a single superframe duration.

Transmission Scheduling With Order Constraints in WIA-FA-Based AGV Systems

Conventional wireless automated guided vehicle (AGV) systems based on WiFi or ZigBee suffer random network performance and fail to guarantee the ordered and reliable transmission in AGV applications. In this article, we study the transmission scheduling with order constraints in WIA-FA-based AGV systems. We first present the transmission process of data packets in the WIA-FA-based AGV system, and design a novel superframe structure to support the ordered data exchange in the AGV system. Then, we design three heuristic rules for timeslot allocation to regulate the device transmissions subject to order constraints. Finally, inspired by the designed rules, we propose a dynamic expected packet loss rate-based timeslot allocation (DELTA) algorithm and prove its time complexity rigorously. The simulation results show that the proposed DELTA algorithm outperforms the existing works in terms of transmission reliability for different channel conditions and network scales.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

User Behavior Driven MAC Scheduling for Body Sensor Networks: A Cross-Layer Approach

In deploying body sensor networks (BSNs), sampling rates might be dynamically tuned to fit application requirements (e.g., monitoring patients’ different activities), which helps conserving energy for battery-powered sensors. However, this results in variable data rates among sensors, which further requires an efficient resource allocation to maintain reliable transmission accommodating all traffic loads. We thereby address this joint problem of transmission reliability and energy efficiency by proposing a BSN system that autonomously detects user behaviors, which, in turn, trigger dynamic sampling and resource scheduling via an adaptive MAC scheduling scheme. This cross-layer scheme uses time-slotted channel hopping (TSCH) in IEEE 802.15.4, which is a reliable low-power MAC protocol. Specifically, the proposed solution determines the best TSCH slotframe length for specific application requirements and the number of timeslots to be added/removed according to dynamic sampling rates, and then allocates timeslots via an equally spaced timeslot allocation algorithm. We implement our proposed approach on a BSN testbed, which features both state-of-the-art hardware and software architectures. The experimental results are conducted to evaluate our proposed solution in terms of throughput, packet delivery ratio, and energy per bit, which demonstrates that our cross-layer solution ensures reliable data transmission and energy efficiency compared to existing techniques.

Timeslot Switching-Based Optical Bypass in Data Center for Intrarack Elephant Flow With an Ultrafast DPDK-Enabled Timeslot Allocator

To alleviate the performance interruption between elephant and mouse flows, a combined optical timeslot switching (OTS) and L2/L3 Top of Rack (ToR) architecture is experimentally demonstrated for the first time in a data center scenario. In this system, elephant flows are identified on the fly by a newly built sFlow-based traffic analyzer and delivered through a separate OTS subsystem. In this way, elephant flows are physically isolated from mouse flows; thus, fewer mutual interruptions are expected. However, previous OTS research has not sufficiently addressed how the timeslot allocator and timeslot allocation should be implemented to adapt to traffic fluctuations, and it is obvious that the current SDN-based timeslot allocation cannot satisfy the OTS system's requirements for time efficiency. Therefore, in this paper, data-plane development kit platform was used to enable an ultrafast online timeslot allocator (TSA) that can process timeslot requests at traffic levels exceeding 1 Tb/s. This demonstration employs two Intel Xeon CPU e5 logical cores for TSA, a private protocol between the TSA and servers, and a novel timeslot allocation algorithm. Using the proposed architecture, mouse flows are seldom interrupted by elephant flows. The power penalty of the OTS system is less than 1.2 dB, which is acceptable for server-to-server interconnections. Furthermore, simulation results show that the system achieves a consistently lower end-to-end latency for all flows than does a traditional ToR-based intrarack data center network (DCN).

Quality of Service Performance Analysis of Relaying Networks With Multiple Buffer-Aided Relays

In this paper, we concentrate on a multi-relay network in which the source and the relays are equipped with limited buffers, and we study the maximum supportable arrival rates with buffer overflow quality of service (QoS) guaranteed at all buffered nodes. To this end, the effective capacity (EC) function of such a network is derived as a function of individual QoS exponents of the source and relays denoted by $\theta _S$ , $\theta _{R_1}$ , $\ldots$ , $\theta _{R_N}$ . To reduce the computational complexity, a closed form expression for the EC is computed. Then, a time slot allocation algorithm is proposed based on the defined EC function. To show the results, both scenarios of heterogeneous and homogeneous queuing at the relays are investigated. The behavior of derived EC and the proposed algorithm are evaluated by some numerical results. It is shown for $\theta _S>\theta _R$ , EC remains constant versus $\theta _R$ and varies versus $\theta _S$ and vice versa, meaning that among $\theta _S$ and $\theta _R$ , the higher one is determinative in the amount of $E_C(\theta _S,\theta _R)$ . Also, it is found that the majority of resources is allocated to the node with a more stringent QoS constraint.

Energy Efficient Resource Allocation Algorithm in Energy Harvesting-Based D2D Heterogeneous Networks

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the device-to-device (D2D) user equipments (DUEs) multiplexing cellular user equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the EH constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block (RB) allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum RB allocation for the D2D links, which is a nonconvex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum RB allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.

Joint Multiple Relay Selection and Time Slot Allocation Algorithm for the EH-Abled Cognitive Multi-User Relay Networks

The wireless energy harvesting (EH) technique is regarded as a new way to provide an energy supply for energy-constrained cognitive relay networks (CRNs). A novel wireless EH cognitive multi-user relay network (CMRN) for the underlay protocol is investigated in this paper. In this system, there are multiple primary users (PUs) and multiple secondary users (SUs). The SUs can share the licensed spectrum and harvest energy from ambient signals. The problems of multiple relay selection by the SUs and of finding the optimal EH ratio are considered. We analytically derive the exact expression of the throughput of a secondary network. In it, there are four constraints: for the permitted peak interference with each primary transmitter (PT); for the sum interference for each PT; that the transmit power of secondary source nodes (SSNs) and secondary relays (SRs) should be less than the energy harvested; and that each secondary source node/secondary destination node (SSN-SDN) pair can only choose one SR. To obtain the optimal performance of the secondary network’s throughput, we should optimize the multiple relay selection scheme and the EH ratio. Actually, it is a classic integer optimization problem to design an optimal multiple relay selection scheme. However, the selection of the optimal EH ratio is a continuous optimization problem. The joint multiple relay selection and time slot allocation is a classical hybrid optimization problem. So, we propose a novel quantum sine cosine algorithm (QSCA) for resolving the difficulty with optimization of multiple relay selection and the EH ratio. Our simulation results verify our proposed solution by showing the influence of different parameters for the proposed model and by demonstrating good performance under the QSCA.

Traffic Priority Based Channel Assignment Technique for Critical Data Transmission in Wireless Body Area Network.

In recent days, intelligent biomedical sensors and wearable devices are changing the healthcare industry by providing various heterogeneous vital signs of patients to the hospitals, caregivers, and clinicals. This collective form of monitoring sensor devices forms a very short-range Wireless Body Area Network (WBAN) and plays a key role in the data gathering process. If any sensor node in the network detects abnormal values that should be transmitted promptly via wireless medium with less delay. A single medium allows one-way delivery of a data packet, and it may not be sufficient to satisfy the high volume of communication demand between the sensor nodes in the network. In the same way, the packet prioritization does not guarantee the packet will get there on time and sometime it may cause priority conflicts among the nodes. It is only mean that the flow of delivery service handles that critical data packet before handling other data packets. However, unexploited time slots and bandwidth wastage will occur due to inefficient backoff management and collisions. To minimize the aforementioned issues, various backoff procedures, adaptive slot allocation mechanisms, priority-based medium access control protocols have been developed but suffer limitations in the context of providing priority-based channel access with less backoff conflicts and dedicated allocation of time slots for critical nodes in all cases. Based on these deliberations, a more effective Traffic Priority-based Channel Access Technique (TP-CAT) is proposed using IEEE 802.15.6 in order to minimize the transmission delay of critical data packet and solve conflicts among other priority nodes during the backoff phases. Firstly, a Low Threshold Criticality-based Adaptive Time slot Allocation algorithm (LT-CATA) is presented to decrease the priority slot conflicts between the low threshold data traffic from the same and different type of user prioritynodes. Secondly, a High Threshold Criticality-based Adaptive Time slot Allocation algorithm (HT-CATA) is developed to reduce the priority slot conflicts between the high threshold data traffic from the same and different types of user prioritynodes. Additionally, a novel Random Overlapping Backoff value Avoidance (ROBA) technique is introduced to eliminate the overlapping issue during the selection of random backoff value among the sensor nodes. Since, the proposed technique greatly reduced the channel access delay and transmissiondelay of critical data packet as well as other types of priority data packet. The Simulation results are verified in the CASTALIA 3.2 framework using omnet++ network simulater to relatively evaluate the performance metrics of the TP-CAT technique with state-of-the-art protocols. From the analysis of the results, it is evident that the TP-CAT technique provides better performance in terms of delay, energy consumption, and throughput in healthcare monitoring environments.