Time Slot Allocation Research Articles

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.

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

A Blockchain System for TDMA-Based Tactical Wireless Networks With Constrained Resources

This paper proposes a blockchain system with a consensus algorithm and timeslot allocation policy for TDMA-based tactical wireless networks with constrained resources. A blockchain can serve as a distributed database that ensures all nodes can store the same data even in unreliable network environments. Blockchain technology has numerous benefits, such as decentralization, tamper resistance, traceability, and audibility. Due to these characteristics, the application range of blockchains has dramatically expanded from cryptocurrency, financial services, risk management, and IoT to various services in both public and non-public domains. In addition, there has been an increasing demand for using blockchain technology in wireless network environments. Unlike wired networks, communication resources in wireless networks for data transmission and reception are relatively limited; as such, it is necessary to design a consensus algorithm with low resource consumption and an efficient resource allocation policy for these environments. The proposed system allocates timeslots to nodes based on their contribution to blockchain management, which prevents nodes from participating in an unnecessary competition for block generation, thereby preventing the associated waste of energy. We could verify the proposed system’s practicality through numerical analysis and computer simulations under various conditions.

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

Energy-Aware Offloading and Power Optimization in Full-Duplex Mobile Edge Computing-Enabled Cellular IoT Networks

The incorporation of full-duplex (FD) communication into task offloading offers significant advantages for mobile edge computing (MEC). In this article, we investigate joint optimization of multiuser computation offloading, transmitting power, time slot, and computation resources in FD MEC-aided cellular Internet of Things (IoT) networks. For the sake of minimizing the energy consumption of the IoT nodes, we develop mixed-integer nonlinear programming with coupling constraints. To handle the coupling constraints, we derive the decoupling conditions and simplify the formulated problem into the decoupling and coupling cases. For the decoupling case, we convert the original problem equivalently into several independent subproblems. Then, for each subproblem, the transmitting power and computation offloading are optimized efficiently by the optimal condition of the objective function. After that, we derive the optimal time slot allocation by Karush–Kuhn–Tucker conditions. For the coupling case, we formulate the feasibility problem of the obtained results from the decoupling case as a two-level knapsack problem. Based on that, we develop an efficient algorithm to modify the solution of decoupling case so that its feasibility is guaranteed for the coupling constraints. Extensive simulations indicate that our proposed algorithm can effectively reduce the total energy consumption of the IoT nodes and fully improve the efficiency of MEC with respect to existing algorithms.

Global Resource Allocation for High Throughput and Low Delay in High-Density VANETs

Medium access control (MAC) plays a crucial role in ensuring proper operation in vehicular ad hoc networks (VANETs). However, existing solutions cannot meet the strict requirements of high throughput and low latency in high-density scenarios. In this paper, we propose a novel MAC protocol to meet such demands for VANETs. The roadside unit (RSU) pattern and the dual-transceiver manner are adopted to assign channel resources for all packets. The striking features of our approach are twofold: the time slot allocation based on global vehicle information and the consideration of different delay requirements of different applications. First, we design a time slot exchange mechanism, which avoids any transmission conflict between two adjacent RSUs, to reduce channel resource waste. Then, we design a packet weight allocation mechanism, by which the packets of any network edge area are assigned higher priorities to further reduce channel resource waste. Moreover, we devise a packet quality evaluation mechanism, by which our multi-objective problem is transformed into a single-objective problem. In addition, we devise a greedy branch-and-bound algorithm to address the multiple-knapsack problem, which is transformed into a single-knapsack problem. Extensive simulation results show the advantages of our approach in terms of throughput and latency.

An Adaptive MAC Protocol Based on Time-Domain Interference Alignment for UWANs

Abstract The spatial and temporal uncertainty caused by large propagation delays is a fundamental feature of Underwater Acoustic Networks (UWANs), which seriously affects the performance of the UWANs and also brings challenges to the design of MAC protocols. In this paper, we develop an adaptive MAC protocol based on deep reinforcement learning for UWANs, called ARL-MAC protocol, to intelligently allocate time slots for nodes. Firstly, we design a reward mechanism based on the idea of Time-Domain Interference Alignment (TDIA). We determine the reward according to the combination of the node action and the feedback corresponding to the action. Then, we propose a flexible training mechanism to deal with the ever-changing underwater environment, which improves the fairness of time slot allocation. In addition, we introduce the Deep Recurrent Q-Network (DRQN) algorithm to solve the partially observable information issue. Finally, we evaluate the ARL-MAC protocol with the different number of nodes and changing network environment. Simulation results reveal that the ARL-MAC protocol outperforms other MAC protocols for UWANs in terms of throughput, collision rate and service fairness.

Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN

Many-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and channel for each node is one of the most critical problems for many-to-many data aggregation in multi-channel WSNs, and this problem can be solved with the new distributed scheduling method without communication conflict outlined in this paper. The many-to-many data aggregation scheduling process is abstracted as a decentralized partially observable Markov decision model in a multi-agent system. In the case of embedding cooperative multi-agent learning technology, sensor nodes with group observability work in a distributed manner. These nodes cooperated and exploit local feedback information to automatically learn the optimal scheduling strategy, then select the best time slot and channel for wireless communication. Simulation results show that the new scheduling method has advantages in performance when comparing with the existing methods.

ASCENT: Communication Scheduling for SDF on Bufferless Software-Defined NoC

Bufferless software-defined network-on-chip (NoC) is a promising alternative to conventional dynamic routing as it offers predictable data movement with real-time guarantees. Existing time-division multiplexing (TDM)-based mechanisms for predictability assume the worst-case communication pattern (e.g., all-to-all) and compute a fixed schedule wherein the cores can only communicate during the allocated time slots. These approaches lead to low application throughput as they cannot adapt to application characteristics. In this article, we present an application specific, non-TDM-based communication scheduling mechanism for bufferless software-defined NoCs. We choose the synchronous dataflow (SDF) model of computation to represent the input streaming applications. We propose ASCENT, a novel offline approach that takes the SDF-specified streaming application and the NoC architecture as input, exploits the task interactions and the timing information in the SDF, and generates the task-to-core mapping and communication schedule that is represented compactly in hardware. ASCENT achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.8\times $ </tex-math></inline-formula> better performance on average than existing TDM-based NoCs and manages to achieve the performance of an ideal dynamically routed NoC, yet ensuring predictability.

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.

A Novel Real-Time Deterministic Scheduling Mechanism in Industrial Cyber-Physical Systems for Energy Internet

As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems.

Age-Driven Spatially Temporally Correlative Updating in the Satellite-Integrated Internet of Things via Markov Decision Process

In this article, we consider the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

A one-time pad encryption scheme based on efficient physical-layer secret key generation for intelligent IoT system

The one-time pad (OTP) is an application-layer encryption technique to achieve the information-theoretic security, and the physical-layer secret key generation (SKG) technique is a promising candidate to provide the random keys for OTP. In this paper, we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate. To maximize the efficiency of secure communication, we divide the process of secure transmission into two stages: SKG and then encrypted packet transmission. Meanwhile, we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk. Furthermore, we design a key updating protocol based on our SKG scheme for OTP encryption. Simulation results verify that our scheme can generate keys securely and efficiently, and significantly improve the secure communication performance in an intelligent IoT system.

Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks

In order to efficiently compute the primary data and support the secondary quality-of-service (QoS) requirement, we propose a cooperative hybrid non-orthogonal multiple access (NOMA) scheme for mobile edge computing (MEC) assisted cognitive radio networks. In the proposed scheme, the primary computation task is securely offloaded to the secondary base station, and the hybrid NOMA technique is adopted to provide secondary spectrum access and secure the primary offloading simultaneously. The weighted energy consumption minimization problem for both the primary and secondary systems is first studied under the constraints of the primary system’s secure outage probability and the secondary system’s QoS requirements, and a two-stage algorithm is proposed to derive the optimal power, time slot and computation task allocation. To motivate the secondary system’s cooperation, we optimally allocate the transmit power, time slot and computation task, such that the average secondary system’s rate is maximized under the primary system’s security requirement, and we derive closed-form expressions for the optimal resource allocations. Numerical results demonstrate the performance superiority of the proposed scheme compared with the full-offloading scheme in terms of the energy consumption and the average secondary rate.

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.

A distributed energy-efficient opportunistic routing accompanied by timeslot allocation in wireless sensor networks

Sensed data can be forwarded only in one direction to the base station in one-dimensional queue wireless sensor networks different from mesh structure, so the network lifetime will be shortened if some continuous neighboring nodes have run out of their energy. So designing routing protocols for balancing energy consumption is a challenging problem. However, traditional and existing opportunistic routing protocols for one-dimensional queue wireless sensor network proposed so far have not yet addressed this problem to prolong the network lifetime by introducing sleep mode. In this article, we propose a distributed energy-efficient opportunistic routing algorithm accompanied by timeslot allocation by using specific network topology of one-dimensional queue wireless sensor network. In our new algorithm, clustering and routing tree construction is performed while introducing the optimal relay transmission distance achieved by using opportunistic routing principle, and at the same time, interference-free wake up time is scheduled, which may optimize energy consumption and decrease the number of various control messages as possible to prolong the network lifetime. Furthermore, this improves energy efficiency by introducing the operation mode giving up cluster head role. Simulation results show that the proposed protocol can significantly improve the network performance such as energy consumption and network connectivity, when compared with other existing protocols.

Age of Information in Energy Harvesting Aided Massive Multiple Access Networks

Given the proliferation of the massive machine type communication devices (MTCDs) in beyond 5G (B5G) wireless networks, energy harvesting (EH) aided next generation multiple access (NGMA) systems have drawn substantial attention in the context of energy-efficient data sensing and transmission. However, without adaptive time slot (TS) and power allocation schemes, NGMA systems relying on stochastic sampling instants might lead to tardy actions associated both with high age of information (AoI) as well as high power consumption. For mitigating the energy consumption, we exploit a pair of sleep-scheduling policies, namely the multiple vacation (MV) policy and start-up threshold (ST) policy, which are characterized in the context of three typical multiple access protocols, including time-division multiple access (TDMA), frequency-division multiple access (FDMA) and non-orthogonal multiple access (NOMA). Furthermore, we derive closed-form expressions for the MTCD system&#x2019;s peak AoI, which are formulated as the optimization objective under the constraints of EH power, status update rate and stability conditions. An exact linear search based algorithm is proposed for finding the optimal solution by fixing the status update rate. As a design alternative, a low complexity concave-convex procedure (CCP) is also formulated for finding a near-optimal solution relying on the original problem&#x2019;s transformation into a form represented by the difference of two convex problems. Our simulation results show that the proposed algorithms are beneficial in terms of yielding a lower peak AoI at a low power consumption in the context of the multiple access protocols considered.

Active learning by means of team-based versus traditional learning methods in undergraduate dental students

Abstract Introduction: Team-based learning (TBL) is gaining popularity because it allows students to achieve desired intellectual tasks through teamwork, discussion, and professionalism, in contrast to traditional (Trad) learning, which fails to keep the students' continued attention throughout the teaching session, resulting in low grades and retention rates. Aims: Comparing the outcomes between TBL and Trad learning sessions. Materials and Methods: A cross-sectional pilot study was carried out. Seventy-two undergraduate dental students were grouped as TBL and Trad. Each group consisted of 36 students and was made up of good, average, and below-average achievers based on their most recent exam results. The control group (Trad) received lecture-based teaching on a topic, while the case group (TBL) received discussion-based teaching on the same topic using the TBL approach. Allocated time slot for each method was equal. Following the completion of the teaching, a test was taken in the same session, consisting of 20 well-constructed multiple-choice questions (MCQs). The outcomes of the MCQ tests were subjected to item analysis. Chi-squared test was used to compare test parameters between the control and case groups using SPSS (v. 23). Results and Discussion: Overall, the TBL groups yielded significantly higher (58.33%) learning outcomes than the Trad groups (44.4%) (P = 0.01). In general, the TBL approach showed highly significant performance results (P = 0.000) compared to Trad approach. The Cronbach's alpha for TBL and trad learning was 0.967 and 0.949, respectively, indicating an excellent reliability of our findings. Conclusion: TBL approach has a potential to improve learning outcome by increasing knowledge and encouraging self-directed learning.

Smart EV Charging Strategies Based on Charging Behavior

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

Development of a generic timetabling and scheduling system based on the combinations of Meta-Heuristics algorithm

This research paper examines the usage of combining the meta-heuristic approaches for solving critical real-world optimization problems due to the hard and soft constraints. For the last few decades, developing an optimal solution for these problems were considered as a difficult task, and manual timetabling required an excessive amount of effort and time to deliver the quality results. This paper's objective is to address several approaches and develop a generic solution for solving a wide range of timetabling and scheduling problems. Many techniques and benchmark instances have been reviewed and presented to find a feasible solution in the literature section. This paper also investigates the performance based on the combination of meta-heuristic approaches to solve the problem and introduce the algorithm to allocate time slots to several resources according to their constraints satisfaction and violations. However, this algorithm may consider as a comprehensive benchmark instance and significant improvement for future researchers. Furthermore, the computational and experimental results of the algorithm determine the feasibility, effectiveness, robustness, and optimality of the proposed approach.