Number Of Time Slots Research Articles

The Bit Query for Labels in a Binary Tree-Based Anti-Collision Recognition Algorithm

With its benefits including recognition distance, penetration ability, and multi-object recognition, radio frequency identification technology is a non-contact automatic identification method that is currently being used extensively in a variety of industries, including document anti-counterfeiting, automation, transportation, control management, and product services. An efficient anti-collision mechanism, such as anti-collision algorithms or anti-collision protocols, must be established in order to coordinate communication between the tag and the reader in the event that an RFID system experiences signal interference in the wireless channel, conflict, or collision, which will result in tag recognition or data collection failure. In response to tag conflicts in radio frequency identification (RFID) systems, a novel hybrid blocking query tree algorithm is suggested that leverages blocking prediction and child node hedging techniques to overcome the issues of lengthy time intervals and high communication complexity. The technique uses quadruple and bifurcated query trees as the foundation for its conflict avoidance strategy. The approach minimizes the conflict time and prevents the construction of idle subunits by forcing the reader to create new query prefixes via prediction, which is accomplished by employing locking commands to extract and forecast information about the conflicting bits. According to simulation data, this strategy performs better in terms of lowering the overall number of time slots and communication complexity as well as enhancing identification and labelling abilities than the current adaptive multi-tree search (RLAMS) and improved hybrid query tree (IHQT) techniques. It greatly increases the effectiveness of label identification while lowering the overall amount of time slots and communication complexity.

Unmanned-Aerial-Vehicle-Aided Integrated Sensing and Computation With Mobile-Edge Computing

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this paper, we investigate the unmanned aerial vehicle (UAV) aided ISAC with mobile edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a system-wise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict non-convexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.

Near-Optimal Time–Energy Tradeoffs for Deterministic Leader Election

We consider the energy complexity of the leader election problem in the single-hop radio network model, where each device v has a unique identifier ID ( v ) ∈{ 1, 2, ⋖ , N } . Energy is a scarce resource for small battery-powered devices. For such devices, most of the energy is often spent on communication, not on computation. To approximate the actual energy cost, the energy complexity of an algorithm is defined as the maximum over all devices of the number of time slots where the device transmits or listens. Much progress has been made in understanding the energy complexity of leader election in radio networks, but very little is known about the tradeoff between time and energy. Chang et al. [STOC 2017] showed that the optimal deterministic energy complexity of leader election is Θ (log log N ) if each device can simultaneously transmit and listen but still leaving the problem of determining the optimal time complexity under any given energy constraint. Time–energy tradeoff: For any k ≥ log log N , we show that a leader among at most n devices can be elected deterministically in O ( k ċ n 1+ε ) + O ( k ċ N 1/k ) time and O ( k ) energy if each device can simultaneously transmit and listen, where ε > 0 is any small constant. This improves upon the previous O ( N )-time O (log log N )-energy algorithm by Chang et al. [STOC 2017]. We provide lower bounds to show that the time–energy tradeoff of our algorithm is near-optimal. Dense instances: For the dense instances where the number of devices is n = Θ ( N ), we design a deterministic leader election algorithm using only O (1) energy. This improves upon the O (log* N )-energy algorithm by Jurdziński, Kutyłowski, and Zatopiański [PODC 2002] and the O (α ( N ))-energy algorithm by Chang et al. [STOC 2017]. More specifically, we show that the optimal deterministic energy complexity of leader election is \(\Theta (\max \lbrace 1, \log \tfrac{N}{n}\rbrace)\) if each device cannot simultaneously transmit and listen, and it is \(Θ (\max \lbrace 1, \log \log \tfrac{N}{n}\rbrace)\) if each device can simultaneously transmit and listen.

Field test of quantum key distribution with high key creation efficiency.

Quantum key distribution (QKD) promises unconditional security for communication. However, the random choices of the measurement basis in QKD usually result in low key creation efficiency. This drawback is overcome in the differential-phase-shift QKD, provided that each photon can be prepared in a large number of time slots with a proper waveform. In this work we develop a miniature room-temperature 1550-nm single-photon source to generate narrowband single photon in 50 time slots with a nearly optimal waveform for achieving unity key creation efficiency. By utilizing these single photons in the field test, we demonstrate the differential-phase-shift QKD with a key creation efficiency of 97%. Our work shows that the practical QKD can benefit from the narrowband single photons with controllable waveforms.

Maximizing Packets Collection in Wireless Powered IoT Networks With Charge-or-Data Time Slots

This paper studies data collection in a wireless powered Internet of things (IoT) network with a hybrid access point (HAP). A fundamental problem at the HAP is to determine the number of time slots over a given planning horizon that is used to charge and collect data from devices. To this end, we outline a mixed integer linear program (MILP) to determine (i) the mode (charge or data) of each slot, (ii) the HAP’s transmit power allocation, and (iii) transmitting devices in data slots. Further, we propose a receding horizon approach whereby the HAP solves the said MILP over a time window using channel estimates from a Gaussian mixture model (GMM). We also outline a data-driven approach. In its offline stage, an IoT network operator first solves the said MILP over an exhaustive collection of channel power gains. The MILP solution for each channel power gain realization is then stored in neural networks. In the online stage, the HAP accesses the trained neural network of each time slot to retrieve its mode. The results show that the rolling horizon and data-driven approach allow the HAP to receive up to 106% and 90% more packets as compared to competing approaches.

The Upper Bounds of Cellular Vehicle-to-Vehicle Communication Latency for Platoon-Based Autonomous Driving

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

A New Notion of Reserve for Power Systems With High Penetration of Storage and Flexible Demand

Modern power systems face important demand uncertainties due to increasing penetration of behind-the-meter renewable generation. System operators need to account for such uncertainties when solving the unit commitment and economic dispatch problem. The research literature has proposed advanced methods for decision making under uncertainty but, in practice, actual system operators put more trust in the tried-and-true approach of dealing with future uncertainty by committing reserves. In this paper, the unit commitment and economic dispatch problem is formulated for a system with high penetration of storage and the inadequacy of methods based on the traditional notion of reserves is exposed. Namely, in contrast to a generator, a storage unit can provide reserve capacity in a number of timeslots but it cannot provide an analogous reserve activation in all of those timeslots due to the battery's energy being depleted. After discussing two plausible but inadequate approaches, a new, generalized notion of reserves is proposed, which addresses these issues while not abandoning the practical, reserve-based approach for the operator's problem, thus making the best of both worlds. The proposed scheme enables storage units to provide reserves, without putting the system at risk of energy scarcity, which is shown to result in substantial cost savings.

Equalising the Transmission Properties of Graph-Modelled Networks by Introducing the Control of the Resources Used to Transmit Information

The object of the presented paper is to demonstrate the possibility of equalizing the transmission properties of networks described by graphs with an equal number of nodes of the same degree but differing in diameter and average path length. A node of a graph is understood to be a specialised device performing commutation and transmission functions, while an edge is understood to be a transmission link. It was assumed that this alignment can be achieved by controlling the transmission resources assigned to specific edges of a graph through which the network nodes communicate with each other. The concept of transmission resource, counted in contractual units, is understood as, for example, the number of time slots or the bandwidth of the links used to transmit information. The authors show that with the proposed method, the equalization of the mentioned network properties is achieved with a minimal increase in the global resources used for transmission.

Tile low-rank approximations of non-Gaussian space and space-time Tukey g-and-h random field likelihoods and predictions on large-scale systems

Large-scale statistical modeling has become necessary with the vast flood of geospace data coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) is widely considered for modeling geospace data by estimating a set of statistical parameters related to a predefined covariance function. This covariance function describes the correlation between a set of geospace locations where the main goal is to model given data samples and impute missing data. Climate/weather modeling is a prevalent application for the MLE operation where data interpolation and forecasting are highly required. In the literature, the Gaussian random field is often used to describe geospace data as one of the most popular models for MLE. However, real-life datasets are often skewed and/or have extreme values, and non-Gaussian random field models are more appropriate for capturing such features. In this work, we provide an exact and approximate parallel implementation of the well-known Tukey g-and-h (TGH) non-Gaussian random field in the context of climate/weather applications. The proposed implementation alleviates the computation complexity of the log-likelihood function, which requires O(n2) storage and O(n3) operations, where N is the number of geospace locations, M is the number of time slots, and n=N×M. Based on tile low-rank (TLR) approximations, our implementation of the TGH model can tackle large-scale problems. Furthermore, we rely on task-based programming models and dynamic runtime systems to provide fast execution for the MLE operation in space and space-time cases. We assess the performance and accuracy of the proposed implementations using synthetic space and space-time datasets up to 800K. We also consider a 12-month precipitation dataset in Germany to demonstrate the advantage of using non-Gaussian over Gaussian random field models. We evaluate the prediction accuracy of the TGH model on the precipitation dataset using the Probability Integral Transformation (PIT) tool showing that the TGH model outperforms the Gaussian modeling in the real dataset. Moreover, our performance assessment indicates that TLR computations allow solving larger matrix sizes while preserving the required accuracy for prediction. The TLR-based approximation shows a speedup up to 7.29X and 2.96X over the exact solution.

A RFID anti-collision approach for efficient query tree

In the radio frequency identification technology system, there are too many time slots which are caused by tag collision, and the communication complexity is high. On the basis of the anti-collision algorithm of the binary tree and the quadtree query tree, the methods of locking the collision bit and predicting the child node are used, a new lock-bit hybrid querytree (novel lock-bit hybrid query tree, NLHQT) algorithm is proposed. In the algorithm, the collision bit information is extracted through the lock instruction, and the extracted collision bit information is predicted. While reducing the collision time slot, the reader generates a new query prefix through prediction, thereby avoiding the generation of idle child nodes. Compared with the improved hybrid query tree (IHQT) anti-collision algorithm and the regression lock adaptive multi-tree search (RLAMS), the algorithm in this paper is more effective in reducing the total number of time slots and communication complexity, and the efficiency of identifying tags can be effectively improved.

TGVx: Dynamic Personalized POI Deep Recommendation Model

Personalized points-of-interest (POI) recommendation is very important for improving the service quality of location-based social network applications. It has become one of the most popular research directions in the industry and academia. However, the realization of high-quality personalized POI recommendation faces three major challenges: (i) the interest drift issue caused by the spatiotemporal dynamics of user check-in behavior, (ii) how to integrate as much heterogeneous information as possible to alleviate data sparseness and cold start issues, and (iii) how to use implicit feedback to model complex high-order nonlinear user-POI interactions. To jointly address all these challenges, we propose the TGVx recommendation framework and establish a dynamic personalized POI deep recommendation model, where T and G respectively represent time and geographic factors, V represents out-of-town visitors, and x represents time slot number. TGVx is composed of x parallel TGV models where the TG module mines high-order nonlinear user-POI interaction relationships and integrates multisource heterogeneous information, and the V module transfers the check-in records of out-of-town visitors in hometowns and generates pseudo check-in records in the target city. Technically, we design a new unsupervised deep learning network T-SemiDAE for the TG module. We built a POI-word heterogeneous network for the V module and used graph embedding technology to match the most similar POIs across cities and transfer check-in records. The experimental results on the actual datasets show that the TGVx model is always better than other advanced models in terms of accuracy and diversity for local and out-of-town recommendation scenarios. Compared with the best baseline model semi-deep auto-encoder with a conditional layer the average improvement rates of accuracy and diversity of TGVx are 17.1% to 58.6% and 2.25% to 28.86%, respectively. In theory, our research effectively uses data science and analysis methods to design a recommender system. In practice, our research is motivated by practical problems, and the research results have high practical promotion value. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71831003, 72293563, and 72172025], the Dalian Science and Technology Talent Innovation Support Policy Project [The High-level Talent Innovation Team Project 2022RG17], and the Liaoning Provincial Department of Education [Basic Scientific Research Project LJKMZ20221582]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1286 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0010 ) at ( http://dx.doi.org/10.5281/zenodo.7407123 ).

Spatial Modulation Using Signal Space Diversity

In this letter, a novel scheme, called spatial modulation (SM) using signal space diversity (SM-SSD), is proposed for future multiple-input multiple-output (MIMO) systems. In this scheme, consecutive time slots are modulated jointly, and the technique of signal and space diversity (SSD) is applied to spread the real and imaginary parts of data symbols over the time domain. In addition, novel active antenna activation pattern and time activation pattern selection algorithms are introduced. An upper bound expression for bit error rate (BER) is obtained and a diversity analysis is performed. Besides, a suboptimal solution for the optimization of rotation angles is put forward to maximize the minimum coding gain distance (MCGD). Monte Carlo simulations are performed to compare the BER performance of SM-SSD with the benchmark schemes, in the presence of both correlated and uncorrelated channels. Lastly, the change in the spectral efficiency is analyzed for different numbers of time slots and transmit antennas.

On Data Collection in SIC-Capable Space–Air–Ground Integrated IoT Networks

This article considers routing and uplinks scheduling in space–air–ground integrated networks. Specifically, it considers a successive interference cancellation-capable unmanned aerial vehicle (UAV) that collects data from ground devices operating in an Internet of Things network. Its objective is to maximize the minimum flow rate to a terrestrial gateway over a fixed time horizon. It outlines a mixed integer linear program (MILP) to optimize in each time slot: the routing from the UAV to the gateway, the set of links from ground devices to the UAV, and the flow rate over links. It also proposes a novel algorithm, called iterative flow and path reservation (IFPR), for use by the UAV to select paths. Further, IFPR uses two methods to schedule communications between ground devices and the UAV. The first method, called simplified MILP (SMILP), aims to maximize the sum rate from ground devices. The second method, called less data schedule first, prioritizes ground devices that have uploaded the least amount of data to the gateway. Numerical results show satellite links help the UAV collects 61% more data from ground devices. Moreover, as compared to the MILP, IFPR collects 23% less data. Further, for both MILP and IFPR, their Jain’s fairness index reaches around one when the number of time slots is sufficiently large. Finally, when IFPR uses SMILP, the gateway collects more data but at the expense of fairness.

Energy Harvesting Modulation for Integrated Control State and Energy Transfer in Industrial IoT

With the development of wireless communications, the technology of industrial Internet of Things (IIoT) has been considered as a candidate for the next generation of mobile communications, which could provide automatic services for the smart factory. Massive low-power devices are swarming into the scenario for providing network intelligence. These energy-thirsty devices all require efficient control state delivery. In this letter, an energy harvesting modulation (EHM) scheme is proposed for integrated control state and energy transfer (ICSET) towards these low-power devices. The performance of wireless energy transfer (WET) and wireless control state transfer (WCST) are also analysed theoretically. The ICSET performance is optimized by ensuring the control reliability. Simulation results validate the theoretical analysis. They also demonstrate that an appropriate time slot number within a period should be selected to increase the effective control rate, while ensuring the energy harvesting performance and the control reliability of IIoT.

Secure Transmission Over Multiple Access Wiretap Channel by Cross-Time Interference Injection

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Completion Time Minimization for Data Collection in a UAV-enabled IoT Network: A Deep Reinforcement Learning Approach

In this paper, we study the completion time minimization in an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) network, where the UAV tries to collect all the data generated by the ground IoT devices for further processing. To simplify the analysis, the continuous time horizon is discretized into several time slots. The duration of each time slot is set to be less than the pre-defined threshold such that the UAV's location can be considered as unchanged during each time slot. In our work, we aim to minimize the completion time of the UAV by optimizing the association scheme of the IoT devices, the location (i.e., the trajectory) and velocity of the UAV at each time slot. However, the formulated problem is challenging to solve by traditional optimization methods considering the unknown number of time slots (which leads to the unknown number of decision variables) and non-convex functions. We thus reformulate it as a Markov decision process (MDP) and propose a deep deterministic policy gradient (DDPG)-based method to efficiently solve it. The DDPG-based algorithm uses deep function approximators instead of finding the action that maximizes the state-action value, and is therefore well suited to solve high-dimensional, continuous control problems. Extensive numerical results are presented to validate the effectiveness of our proposed algorithm.

SATMAC: Self-Adaptive TDMA-Based MAC Protocol for VANETs

Rapid development and deployment of vehicular ad-hoc networks (VANETs) require an efficient and scalable media access control (MAC) protocol to support high-priority safety applications and infotainment requirements. This paper proposes SATMAC, a self-adaptive time division multiple access (TDMA)-based MAC protocol for VANETs. In order to improve the stability of the time slot scheduling in VANETs, a slot status updating strategy is carefully designed, which utilizes accurate information of the two-hop neighbors and the rough information of the three-hop neighbors to detect the potential packet collisions and avoids potential collisions by adjusting the occupied time slot. Besides, an adaptive frame length (the number of time slots contained in a frame) approach is proposed on the basis of the slot adjustment to support various densities of vehicles, where the frame length between neighbors can be inconsistent. We conduct theoretical analysis and extensive simulations in a realistic VANET environment to evaluate SATMAC. Simulation results show that compared with IEEE 802.11p and LTE-V2X PC5 Mode 4, SATMAC significantly improves PDR of beacons over 60%. Moreover, our SATMAC design is further implemented and validated on our FPGA-based testbed.

Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.

A Game Theoretic Horizon Decomposition Approach for Real-Time Demand-Side Management

This article introduces two instances of rolling horizon algorithms, viz, shrinking horizon proximal decomposition algorithm (SHPDA) and distributed horizon proximal decomposition algorithm (DHPDA), to solve the demand-side management problem in real time. Both these algorithms are comprised of “two-step”. The number of time-slots to be considered in each iteration is decided in first step. In second step, the solution for considered time-slots is obtained through a non-cooperative game-theoretic approach using the proximal decomposition algorithm (PDA). An investigation is carried out to establish the existence and multiplicity of Nash equilibrium for the formulated game. The convergence of PDA has a strong dependency on system parameters and is unique for a system. Hence an analysis is performed to derive the convergence criteria for the formulated problem. The system model includes distributed energy storage, distributed dispatchable generation, time-shiftable, power-shiftable (PSD), and essential devices. The reduction in power consumption of PSD caused due to the optimization process creates discomfort to users. Therefore, we have incorporated an objective to minimize the discomfort and reduce consumers’ energy bills. Simulation is carried out to show the efficacy of developed algorithms in terms of the energy bill, peak reduction, and solution time. Also, eight scenarios have been analyzed to show the impact of adding discomfort cost on the above-mentioned system parameters.

TDMA policy to optimize resource utilization in Wireless Sensor Networks using reinforcement learning for ambient environment

Data packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor.