Number Of Time Slots Research Articles

An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things.

Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.

Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks

Machine-to-Machine (M2M) networks are an emerging technology with applications in various fields, including smart grids, healthcare, vehicular telematics and smart cities. Heterogeneous M2M networks contain different types of nodes, e.g., nodes that send emergency, periodic, and normal type data. An important problem is to rapidly estimate the number of active nodes of each node type in every time frame in such a network. In this paper, we design two schemes for estimating the active node cardinalities of each node type in a heterogeneous M2M network with T types of nodes, where T ≥ 2 is an arbitrary integer. Our schemes consist of two phases- in phase 1, coarse estimates are computed, and in phase 2, these estimates are used to compute the final estimates to the required accuracy. We analytically derive a condition for one of our schemes that can be used to decide as to which of two possible approaches should be used in phase 2 to minimize its execution time. The expected number of time slots required to execute and the expected energy consumption of each active node under one of our schemes are analysed. Using simulations, we show that our proposed schemes require significantly fewer time slots to execute compared to estimation schemes designed for a heterogeneous M2M network in prior work, and also, compared to separately executing a well-known estimation protocol designed for a homogeneous network in prior work T times to estimate the cardinalities of the T node types, even though all these schemes obtain estimates with the same accuracy.

Distributed Competitive Decision Making Using Multi-Armed Bandit Algorithms

This paper tackles the problem of Opportunistic Spectrum Access (OSA) in the Cognitive Radio (CR). The main challenge of a Secondary User (SU) in OSA is to learn the availability of existing channels in order to select and access the one with the highest vacancy probability. To reach this goal, we propose a novel Multi-Armed Bandit (MAB) algorithm called \(\epsilon\)-UCB in order to enhance the spectrum learning of a SU and decrease the regret, i.e. the loss of reward by the selection of worst channels. We corroborate with simulations that the regret of the proposed algorithm has a logarithmic behavior. The last statement means that within a finite number of time slots, the SU can estimate the vacancy probability of targeted channels in order to select the best one for transmitting. Hereinafter, we extend \(\epsilon\)-UCB to consider multiple priority users, where a SU can selfishly estimate and access the channels according to his prior rank. The simulation results show the superiority of the proposed algorithms for a single or multi-user cases compared to the existing MAB algorithms.

A New Deep Q-Network Design for QoS Multicast Routing in Cognitive Radio MANETs

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.

A Successive Interference Cancellation Based Random Access Channel Mechanism for Machine-to-Machine Communications in Cellular Internet-of-Things

In Cellular Internet-of-Things, random access channel (RACH) mechanism is used by machine-to-machine communication (M2M) devices to connect to a base station (BS) for any information exchange. However, increase in the number of M2M devices increases the network contention and reduces the number of RACH successes. In order to address this problem, we propose a successive interference cancellation based non-orthogonal random access (SIC-NORA) mechanism. In the proposed mechanism, each M2M device is allowed to repeat its transmission in a finite number of time slots within a radio frame. The messages of two devices that collide in the same slot can be decoded if the difference in the arrival time of their messages is greater than the predetermined threshold. Upon the successful RACH from an M2M device, the BS applies SIC in the current and all previous slots of the radio frame to decode messages of other devices for enhanced RACH success. A Markov chain model of the proposed mechanism is developed and the corresponding steady-state probabilities are derived. Through extensive numerical results, we show that the proposed mechanism performs better than state-of-the-art RACH mechanisms in terms of number of RACH successes and average access delay. Further, the proposed mechanism improves the success rate by 65.3% and 30.3% as compared to NORA and SIC-based RACH mechanism, respectively. Moreover, there is 29.6% and 15% reduction in the number of time slots required for 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> devices to get success with the proposed mechanism as compared to NORA and SIC-based RACH mechanism, respectively, at the corresponding optimal value of radio frame length.

Research on Anti-collision algorithm of Multi-RFID tags in intelligent clinical monitoring system

In view of the collision problem of multi Rfid Tags in recognition, the long recognition time of Deterministic algorithm and the low efficiency of the random algorithm are easily hungry and thirsty; In this paper, an improved anti-collision Algorithm (DFSA-CT) is proposed based on the design of dynamic environment. Compared with other collision algorithms, this algorithm has obvious advantages in time slot number and recognition efficiency. Finally, experimental simulation is used to analyze the algorithm and its superiority is verified.

Electric Power Profiles Contract via Probabilistic Optimization

In this paper, we deal with a problem of how to make contracts of time series of electric energy, called electric power profiles, in electricity markets. We suppose that each energy supplier can deliver a time series of electric energy in a possible profile. In such situation, each electric power profile has its own value for suppliers and consumers and it should be traded in electric markets appropriately. However, different from a case of ordinary spot markets, which deal with a contract on each time slot independently, an electric power profile includes multidimensional variables according to the number of time slots, then to find a matching of a profile and its price between suppliers and consumers is a hard problem from computation complexity. In order to solve this difficulty, we propose a probabilistic algorithm to find a matching of an electric power profile and its price between suppliers and consumers. A kind of properness of the solution is guaranteed in a probabilistic sense and the method also can avoid the curse of dimensionality. The efficiency of the proposed algorithm and the necessary computation complexity are also verified by a numerical simulation.

Power Minimization for Massive MIMO Over-the-Air Computation With Two-Timescale Hybrid Beamforming

This letter studies the massive multiple-input multiple-output (MIMO) analog uncoded over-the-air computation (AirComp), which is a promising solution in Internet-of-Things (IoT) networks for fusion centers (FCs) to compute functions of data distributed in a large number of wireless devices (WDs). To avoid the heavy overhead of the perfect real-time channel state information (CSI) estimation, we develop a two-timescale hybrid beamforming (THB) scheme in AirComp. Under this setup, we aim to minimize the transmit power over time, while ensuring a minimum required computation distortion in terms of the mean-squared error (MSE). We propose a constrained stochastic successive convex approximation (CSSCA) based algorithm to solve the formulated stochastic non-convex optimization problem for the THB design. Specifically, the short-timescale receive digital beamforming matrix at the FC is optimized at each time slot based on the real-time effective low-dimensional CSI matrices by a sum-minimization-mean-squared-error (sum-MMSE) receiver, which can better deal with the signal misalignment. By contrast, the long-timescale transmit beamforming matrix at WDs and the receive analog beamforming matrix at the FC are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. Numerical results show that the proposed THB design algorithm achieves similar transmit power performance as that achieved by the design when the perfect real-time CSI is available.

Efficient formulation for transportation scheduling of single refinery multiproduct pipelines

Multiproduct pipeline transportation scheduling is a complex operations research problem that is characterized by the movement of the cargo rather than the carrier. Hence, it cannot be solved using vehicle routing methods. While most formulations for short-term scheduling adopt a continuous-time representation, they often lead to suboptimal solutions because of the dependence on the number of time slots in the grid, which is difficult to predict. Furthermore, some of these formulations have poor linear relaxations due to the presence of inefficient big-M constraints. In this paper, we develop a discrete-time mixed integer linear programming (MILP) model for the detailed scheduling of a straight pipeline with a single refinery and multiple depots. The proposed formulation rigorously detects interface material generated between adjacent products and considers planned shutdowns in pipeline segments due to maintenance operations, as well as local market demands occurring at multiple intermediate due dates. The main novelty is that continuous tasks can span multiple time slots to enforce minimum batch sizes on injection and delivery nodes, which allows for the model to generate better schedules than those obtained with previously proposed formulations. To ensure an efficient model by design, we rely on generalized disjunctive programming (GDP) and on the convex hull reformulation of disjunctions, which results in stronger and often more computationally efficient formulations. We present numerical results for a set of benchmark instances and show that the proposed model applies to large-scale industrial cases.

A Simple and Efficient Time-Sensitive Networking Traffic Scheduling Method for Industrial Scenarios

Time-Sensitive Networking (TSN) provides end-to-end data transmission with extremely low delay and high reliability on the basis of Ethernet. It is suitable for time-sensitive applications and will be widely used in scenarios such as autonomous driving and industrial Internet. IEEE 802.1Qbv proposes a time-aware shaper mechanism, which enables switches to control the forwarding of traffic in port queues according to pre-defined Gate Control List (GCL). The length of the GCL is limited, and the previous method of scheduling cycle with a hyper period may result in a larger GCL. Based on Satisfiability Modulo Theories (SMT), we propose a TSN scheduling method for industrial scenarios and develops a series of scheduling constraints. Different from the previous scheduling methods, the method proposed in this paper adopts the base period cycle to update GCL regularly, which can effectively reduce the number of time slots in GCL and make the configuration of GCL simpler and more efficient. In addition, compared with the traditional hyper period method, the method proposed in this paper can calculate the scheduling results faster while ensuring low latency and reducing the runtime effectively.

UAV-Assisted Data Collection for Ocean Monitoring Networks

Ocean monitoring network (OMN) is a remote oceanic data collection system, which integrates communication and networking technologies. As underwater acoustic communication has been widely used for data transmission between battery- powered underwater sensor nodes (USNs) and sink nodes (SNs), an oceanic data collection system requires energy efficient deployment to prolong the lifetime of the whole network. This work aims to propose an unmanned aerial vehicle (UAV) assisted OMN architecture, in which sensing data are transmitted first from USNs to sea surface SNs in each data collection cycle using underwater acoustic communication, and then a UAV hovering above the SNs collects and relays all the data to a ground base station via wireless communication links. To extend the network lifetime, we model SNs and UAV deployment and resource allocation as a mixed integer non-convex optimization problem. To solve the problem efficiently in a heuristic way, we design a SNs deployment scheme with the help of time division USN-to-SN access and NOMA based SN-to-UAV access schemes. Computer simulations validate the superiority of the proposed deployment and access schemes on OMN lifetime performance. in particular, increasing the number of time slots in the USN-to-SN access process can improve the performance significantly.

Performance analysis of different intensity modulation techniques over atmospheric turbulent free-space optical channels.

In this paper, we introduce a comprehensive study of the performance of intensity modulation (IM) techniques over gamma-gamma (GG) distributed free-space optical (FSO) channels. We derive closed-form expressions for the average bit error rate (BER) of thermal-noise-limited FSO systems adopting various IM techniques. The effect of atmospheric turbulence is studied. Comparisons are presented in terms of the transmitted peak power to achieve a certain BER. Results show that on-off keying, pulse amplitude modulation, and expurgated pulse-position modulation (EPPM) provide superior performance compared to other IM techniques. In addition, a proposal to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) is introduced by adding another modulation layer of EPPM. A closed-form expression for the average BER of the proposed hybrid OFDM-EPPM technique over GG turbulent FSO channel is derived. Moreover, an expression for the outage probability of the FSO system adopting the proposed OFDM-EPPM technique is presented. Results are investigated and compared to those of traditional OFDM and hybrid OFDM with PPM (OFDM-PPM). It is shown that the hybrid OFDM-EPPM technique outperforms the hybrid OFDM-PPM technique for the same data rate. Moreover, the hybrid OFDM-EPPM technique allows higher data rates with lower PAPR values compared to those of the hybrid OFDM-PPM technique for the same number of time slots.

A time-slot based signal scheme model for fixed-time control at isolated intersections

Fixed-time signal control is widely used in the real world due to its low cost of implementation. Repeated signal cycles with the same phase structure are applied in the existing fixed-time signal control schemes. However, such signal schemes do not always achieve the best performance, especially when traffic demand is unbalanced. The inclusion of sub-cycles in one cycle provides a potential solution in this situation. This study proposes a time-slot based signal scheme model with a generalized cycle structure for fixed-time signal control at isolated intersections. The proposed signal scheme can generate sub-cycles with different phase structures. It optimizes the number of sub-cycles in one cycle as well as the phase sequence, cycle length, and green splits in each sub-cycle. Certain phases may be skipped in partial sub-cycles. Nonlinear programming models are formulated to minimize average vehicle delay and maximize intersection capacity for under- and over-saturated demand. Solution algorithms for the two nonlinear models are developed by solving a series of mixed-integer-quadratic-programming (MIQP) models and mixed-integer-linear-programming (MILP) models, respectively. The numerical studies validate the advantages of proposed signal scheme in terms of intersection capacity and average vehicle delay. The sensitivity analyses show that: 1) a long maximum green time can enable the conventional dual-ring, eight-phase structure to handle unbalanced demand from the perspective of intersection capacity; 2) the time-slot number of 6 or 7 is suggested considering both the solution optimality and the model complexity.

Two-Timescale Hybrid Analog-Digital Beamforming for mmWave Full-Duplex MIMO Multiple-Relay Aided Systems

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system's complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently - namely for each time slot - based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system's sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs.

A Space-Time Correlation Model for MRC Receivers in Rayleigh Fading Channels

This paper presents a statistical model for maximum ratio combining (MRC) receivers in Rayleigh fading channels enabled with a temporal combining process. This means that the receiver effectively combines spatial and temporal branch components. Therefore, the signals that will be processed by the MRC receiver are collected not only across different antennas (space), but also at different instants of time. This suggests the use of a retransmission, repetition or space-time coding algorithm that forces the receiver to store signals in memory at different instants of time. Eventually, these stored signals are combined after a predefined or dynamically optimized number of time-slots or retransmissions. The model includes temporal correlation features in addition to the space correlation between the signals of the different components or branches of the MRC receiver. The derivation uses a frequency domain approach (using the characteristic function of the random variables) to obtain closed-form expressions of the statistics of the post-processing signal-to-noise ratio (SNR) under the assumption of equivalent correlation in time and equivalent correlation in space. The described methodology paves the way for the reformulation of other statistical functions as a frequency-domain polynomial root analysis problem. This is opposed to the infinite series approach that is used in the conventional methodology using directly the probability density function (PDF). The results suggest that temporal diversity is a good complement to receivers with limited spatial diversity capabilities. It is also shown that this additional operation could be maximized when the temporal diversity is adaptive (i.e., activated by thresholds of SNR), thus leading to a better resource utilization.

Routing in Energy Harvesting Wireless Sensor Networks With Dual Alternative Batteries

This article considers an energy-harvesting wireless sensor network (EH-WSN) where nodes have a dual alternative battery system. We aim to design a routing protocol that transmits a given amount of data to a sink using the minimum number of time slots. To this end, we first formulate the routing problem as a mixed-integer linear program (MILP) and model the charging and discharging process at each node. Moreover, we also propose a distributed multiarmed bandit-based routing algorithm (MABRA), where each node learns the best next hop(s) to forward its data based on the battery and data information of its neighbors. We study how the number of nodes that generate data or sources, solar panel area, and path exploration probability impacts the end-to-end delays computed by MILP and MABRA. Numerical results show that the end-to-end delays computed by MILP increase with the number of sources. It, however, decreases if nodes use larger solar panels. Critically, nodes experience end-to-end delays that are 55% lower than the average end-to-end delay of a competing routing algorithm.

An effective hybrid local search approach for the post enrolment course timetabling problem

We address the post enrolment course timetabling (PE-CTT) problem in this paper. PE-CTT is known as an NP-hard problem that focuses on finding an efficient allocation of courses onto a finite number of time slots and rooms. It is one of the most challenging resources allocation problems faced by universities around the world. This work proposes a two-phase hybrid local search algorithm to address the PE-CTT problem. The first phase focuses on finding a feasible solution, while the second phase tries to minimize the soft constraint violations of the generated feasible solution. For the first phase, we propose a hybrid of Tabu Search with Sampling and Perturbation with Iterated Local Search. We test the proposed methodology on the hardest cases of PE-CTT benchmarks. The hybrid algorithm performs well and our results are superior compared to the recent methods in finding feasible solutions. For the second phase, we propose an algorithm called Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P). The SAR algorithm is used to minimize the soft constraint violations by exploiting information collected from the preliminary runs. We test the proposed methodology on three publicly available datasets. Our algorithm is competitive with the standards set by the recent methods. In total, the algorithm attains new best results for 3 cases and new best mean results for 7 cases. Furthermore, it is scalable when the execution time is extended.

A $$\beta $$-hill climbing optimizer for examination timetabling problem

Examination timetable is a non-trivial task for administrators of the academic institutions repeated every semester. In terms of optimization, examination timetabling is a combinatorial optimization problem concerned with assigning a set of exams to a predefined number of timeslots and rooms with accordance to a given constraints. In this paper, the extended version of hill climbing algorithm called $$\beta $$ -hill climbing is utilized to tackle the examination timetabling problem. $$\beta $$ -hill climbing is a new local search-based method that has two operators ( $$\beta $$ -operator and $${\mathcal {N}}$$ -operator) to iterate towards the optimal solution. The saturation degree heuristic method is utilized in the improvement loop of $$\beta $$ -hill climbing to ensure the solution feasibility. For experimental evaluation, Carter dataset is used comprising 12 instances selected from several real-world universities. Eight convergence scenarios are designed to sensitively analyze the behavior of the proposed algorithm. For comparative evaluations, the results produced by $$\beta $$ -hill climbing are comparatively comparable with previous methods that utilized the same Carter instances.

Efficient Link Scheduling in Wireless Networks Under Rayleigh-Fading and Multiuser Interference

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity $O(n\ln n)$ , where $n$ is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of $O(\ln n)$ is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

Graph Theory Based Beam Scheduling for Inter-Cell Interference Avoidance in MmWave Cellular Networks

The application of high directional beam in millimeter wave leads to a new downlink inter-cell interference (ICI) characteristic that the ICI is high only if the beam of a neighboring cell points towards a user in local cell. This characteristic indicates that the ICI can be avoided if the beams in the network are scheduled coordinately. In this paper, we investigate the time-domain beam scheduling problem for downlink ICI avoidance by modeling the entire network as a graph, where the number of time slots occupied by each beam is considered as a constraint to guarantee service quality. The beams in each cell are classified according to the neighboring cells they may interfere with. If two adjacent cells simultaneously use the beam which may interfere with each other, beam collision occurs, leading to strong ICI. Based on graph theory, we propose a least beam collision (LBC) algorithm to minimize the number of beam collisions, and we prove that this LBC algorithm is capable of acquiring the global minimum beam collision solution. Our simulation results verify that the strong ICI between two neighboring cells can be efficiently eliminated, which benefits the transmission reliability and the network's sum rate.