Time Slot Length Research Articles

An improved sensing data cleaning scheme for object localization in edge computing environment

Abstract Radio frequency identification (RFID) is widely applied due to its fast identification speed and non-contact detection. However, the identification process of RFID tags is susceptible to interference from other tags and environmental factors, resulting in inaccurate identification data. To overcome these problem, this paper proposes an improved sensing data cleaning scheme for object localization in edge computing environment. In tag level data cleaning, we use adaptive sliding window and further consider dynamic tags and read rate in continuous reading cycle to adjust the window size timely and appropriately. In the reader level data cleaning, we estimate the tag number based on Chebyshev’s inequality through Markov chain for cyclic control and optimize different time slot lengths to improve the recognition rate. We build an edge computing environment and combine the proposed tag-level cleaning method and reader-level cleaning method to form a comprehensive RFID data cleaning process. Comparative experimental results show that the RFID data cleaning method proposed in this paper can effectively reduce redundant and missing data and improve the accuracy of tag recognition.

A neighbor discovery protocol with adaptive collision alleviation for wireless robotic networks

Recently, several movement control algorithms have been proposed to move robots to the desired locations and complete various collaborative tasks. These algorithms usually require information exchange among robots, which in turn relies on efficient neighbor discovery. However, neighbor discovery of robots is a challenging problem due to limited communication range and beacon collision. Here, how to set the time slot length and contention window to alleviate the beacon collisions is a major issue: too large or too small slot length and contention window values cannot achieve good performance in the robotic network. Therefore, we propose a neighbor discovery protocol with an adaptive collision alleviation mechanism, i.e. ND-ACA. ND-ACA adopts a simple mechanism to detect and avoid potential collisions before sending beacon, and adaptively adjusts both slot length and backoff window according to the number of historical beacon collections and beacon collisions. Extensive simulation results show the efficiency of our proposed approach. To the best of our knowledge, this is the first work in the literature that explore the joint implementation effects that simultaneously consider the robot mobility model and neighbor discovery.

Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning.

This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q -Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q -learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.

Design analysis and optimization of chaotic systems for duration and carrier parallel index modulation

With the rapid advancement in communication technology, the demand for high speed and efficient transmission has increased significantly. Traditional communication systems are unable to meet these demands, and thus, this paper proposes a new correlated delay shift keying system for duration and carrier parallel index modulation (DCIM-CDSK) that employs duration and carrier parallel index modulation. The DCIM-CDSK system utilizes index mapping to transmit extra bits, while group mapping the timeslots on the carrier. By using spare timeslots in each group to transmit modulated information bits, the system achieves high-speed transmission with high efficiency. Additionally, the system introduces a noise reduction method to optimize the information-carrying signal to improve its noise immunity. The theoretical bit error rate (BER) equations is derived in the paper for the DCIM-CDSK system under Gaussian and Rayleigh fading channels, and compared them with practical simulations to validate the theoretical derivation. The paper also analyzes the BER of the system for a single longest timeslot length and mixed longest timeslot length, respectively. The simulation results show that the proposed DCIM-CDSK system has better noise immunity and higher transmission rates than other similar systems. In today's increasingly stringent requirements for communication systems, the DCIM-CDSK system, with its high spectral and energy efficiency, provides a feasible solution for the current communication system industry to realize large user group services and high bandwidth utilization efficiency. At the same time, because of the flexibility of the system between the transmission rate and BER performance, the communication system can get a better trade-off and balance between the two contradictions of effectiveness and reliability.

New Multipriority and Variable Duration Triple Time Slot P-CSMA Protocol for Edge Servers Server Deployment

In this paper, we study the joint impact of task priority and transmission time slot variable computing services on a novel p-persistent random multiple access communication protocol (p-persistent CSMA) in an edge server network. The traditional p-persistent CSMA protocol performs better at low loads, and in order to make the protocol perform better even when the edge server network is accessed by a large number of smart terminals, we consider adding a variable success time slot length and a multichannel, multipriority access model. When the success time slot is longer, the nodes will take longer time to send data, which will reduce the number of nodes retransmitting data, thus improving channel utilization and making the throughput rate of the network increase accordingly. Then, different priority levels are assigned according to the user’s needs, and the higher the priority level, the more channels are occupied and the probability of successful message delivery increases. Finally, the superiority of the proposed protocol is verified by simulation.

Joint resource optimization and trajectory design for energy minimization in UAV-assisted mobile-edge computing systems

This paper studies an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, where a rotary-wing UAV equipped with computing platforms is used to assist ground users (GUs) with insufficient computing resource. Each GU offloads part of the task to the UAV for computation through the uplink, and the remaining computation task is computed by itself. Then, UAV will return the results to the corresponding GU via downlink. Considering the results for each GU is not only used by itself, but may also be sent to other GUs for data fusion, we thus design two specific scenarios in this paper: (1) homologous UAV-assisted MEC scenario, i.e. UAV receives the computation task of GUs and then returns the results to the identical GUs, and (2) non-homologous UAV-assisted MEC scenario, i.e., UAV receives the computation task of source GUs and returns the results to the corresponding destination GUs. For these two scenarios, we propose an energy minimization problem by jointly optimizing task partition, the time slot length and UAV trajectory. In particular, we use the path discretization approach to convert the problem as a problem form which is finite variables. Since the problem is non-convex, we use successive convex approximation (SCA) techniques to tackle the non-convexity. Considering the initial trajectory has an important affect on the experimental result, then a specific trajectory initialization design via combining with the Pickup-and-Delivery Problem (PDP) is proposed. Numerical results proof our proposed design is superior compared with the benchmark cases.

Resource Allocation for OFDM-Based Status Update Systems: A Timeliness Perspective

Increasingly more time-sensitive applications emerge in Internet of things (IoT), requiring fresh status updates. Efficient resource allocation is critical for improving the timeliness of information. This paper investigates how to allocate bandwidth and configure the transmission in an orthogonal frequency division multiplexing (OFDM)-based IoT systems for timely status updating. Specifically, a general resource allocation problem is formulated using age of information (AoI) as the timeliness metric. We first consider a static channel case where we propose a fixed time slot length policy, a fixed blocklength policy, an iterative optimization algorithm, and an approximate optimal policy to address the optimal resource allocation and transmission configuration. Then, we extend the study to quasi-static fading channel cases. In particular, sticking to the transmission rate and spectral efficiency, the four policies designated for static channel case are tailored for the quasi-static fading channel case. We show that the iterative algorithms can improve the timeliness of the system significantly. Besides, the approximate optimal policy achieves an AoI performance competitive with the iterative algorithm, while reduces the computation complexity in both channel cases.

An Efficient Distributed MAC Protocol for Underwater Acoustic Sensor Networks

Many marine scientific and industrial applications in underwater acoustic sensor networks (UASNs) are characterized by dynamic-high load and dense deployment. In such UASNs, the channel reservation mechanism is devised to deal with channel utilization degradation. Unfortunately, channel reservation would lead to an increase in end-to-end delay and a decrease in throughput due to long propagation delay in UASNs. To reduce end-to-end delay and improve throughput, we proposed an efficient distributed medium access control (ED-MAC) protocol for UASNs. ED-MAC reduces the impact of long propagation delays on channel reservation by removing the channel reservation confirmation process and improving channel reservation efficiency. First, the optimized time slot length for the next time frame is obtained based on the data packet number estimate and secondary transmission data packet number, which could reduce idle waiting time. Then, the channel reservation confirmation process is removed by distributed channel reservation mechanism, and efficient channel reservation is achieved through channel reservation packet collision-free transmission and channel multiplexing. Meanwhile, channel reservation is performed before packet generation. Thus, the data packet waiting delay is reduced. Finally, a distributed data packet time slot schedule scheme is proposed to achieve collision avoidance between different data packets, as well as data packets and channel reservation packets. The performance analysis and simulation results show that our proposed ED-MAC could efficiently reduce end-to-end delay and improve throughput in dynamic-high-load and dense UASNs. Simulation results show that ED-MAC can achieve a maximum 98.7% reduction in average end-to-end delay and a maximum 61.1% improvement in throughput.

Evaluation of the Fluctuation Mechanism of Behavioral Financial Market Based on Edge Computing

The global economy is growing faster and faster. Behavioral finance is a transformation of financial theory. Over the past decade, this shift has had strong repercussions in academia, challenging the dominance of traditional finance and forming its own theoretical system. With the development of the stock market, traditional financial theories and behavioral financial theories continue to converge, and traditional financial theories based on investor rationality and efficient market assumptions are subject to unprecedented conjectures. Financial markets are affected by subjective factors such as people’s behaviors and emotions. Investors always make decisions based on bounded rationality, cognitive deficits, and, ultimately, rationality. In order to avoid the complex and unpredictable risks of financial markets and understand their changing laws, the analysis of the characteristics of financial instability is conducive to understanding the nature and internal principles of financial markets. Analysis of the volatility characteristics of financial markets must give priority to the analysis of financial chronological order. Financial time series are characterized by differences in financial markets, which are indeterminate orders, and the analysis of their fluctuations becomes crucial for stimulating the microstructure of financial behavior markets. Therefore, in order to give full play to the role of edge computing and promote the controllability of behavioral financial market volatility, this paper used the calculation task load model algorithm, time slot length optimization algorithm, asymmetric thick-tail random fluctuation, and volatility analysis application algorithm to study the subject of how to learn to reduce financial market volatility, summarizing and discussing the experiment. The research results showed that the behavioral financial market volatility mechanism based on edge computing constructed in this paper improved the predictability of financial market volatility by 15%.

Internet of Things Task Migration Algorithm under Edge Computing in the Design of English Translation Theory and Teaching Practice Courses

This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.

Resource allocation based on multi-grouping and frame expansion for NOMA backscatter communication network

Non-orthogonal multiple access (NOMA) and Backscatter Communication (BackCom) have great development potential in the Internet of Things (IoT). However, the power regulation and energy harvesting capacity of backscatter node (BN) in backscatter network are limited, and it is necessary to explore an appropriate resource allocation scheme to combine NOMA with BackCom. This work presents a resource allocation scheme based on multi-grouping and frame expansion to ensure reliable communication. The BNs are grouped twice to improve the total power by setting more accurate and appropriate reflection coefficients for BNs. Considering the difference of energy harvesting efficiency between BNs, different time slot lengths are set to ensure the energy supply of BNs, and the selection criterion of minimum time slot length is given. The experimental results show that the proposed scheme can effectively improve system throughput and decoding performance.

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

Multi-task learning provides plenty of room for performance improvement to single-task learning, when learned tasks are related and learned with mutual information. In this work, we analyze the efficiency of using a single-task reinforcement learning algorithm to mitigate jamming attacks with frequency hopping strategy. Our findings show that single-task learning implementations do not always guarantee optimal cumulative reward when some jammer’s parameters are unknown, notably the jamming time-slot length in this case. Therefore, to maximize packet transmission in the presence of a jammer whose parameters are unknown, we propose deep multi-task conditional and sequential learning (DMCSL), a multi-task learning algorithm that builds a transition policy to optimize conditional and sequential tasks. For the anti-jamming system, the proposed model learns two tasks: sensing time and transmission channel selection. DMCSL is a composite of the state-of-the-art reinforcement learning algorithms, multi-armed bandit and an extended deep-Q-network. To improve the chance of convergence and optimal cumulative reward of the algorithm, we also propose a continuous action-space update algorithm for sensing time action-space. The simulation results show that DMCSL guarantees better performance than single-task learning by relying on a logarithmically increased action-space sample. Against a random dynamic jamming time-slot, DMCSL achieves about three times better cumulative reward, and against a periodic dynamic jamming time-slot, it improves by 10% the cumulative reward.

A Link Distance Division Based Time Division Multiple Access Protocol for Directional Aeronautical Relay Networks

In directional aeronautical relay networks, the airplane relay explores the advantages of the directional antenna, in terms of long transmission distance, low transmission power, small interference range and so on, to help the ground nodes transmitting data. However, data transmission delay extension problem occurs when the distance of the transmission link extends, where the wireless signal transmission time approximates the data transmission time such that the wireless signal transmission time cannot be omitted. To address the data transmission delay extension problem, a link distance division based time division multiple access protocol, LDD-TDMA, is proposed in this paper. Different from the traditional TDMA protocol, where the time slots are equal and are determined by the longest transmission link, the length of the time slots are different and are determined by different transmission links. Furthermore, the concept of communication coverage ring is proposed where the nodes located in the same ring communicate with the relay utilizing the same time slot length. The relationships between the number of rings, the ring radius and the transmission range of the LDD-TDMA are modelled and derived as a closed formula, where the ring radius are optimized such that the gain is maximized. Simulation results show that LDD-TDMA outperforms TDMA by 13.37% when the transmission range is 200 km and the ring number is 4.

Analysis of the Influence of Per-Second Billing on Virtual Machine Allocation Costs in Public Clouds

For a long time, the common billing time slot used by cloud providers was the hour, but recently they have changed it to one second with a minimum charge of one minute. In this paper, the impact of this change on virtual machine allocation strategies is analyzed. With the minimum one-minute charge, the state-of-the-art allocation strategies are no longer optimal. This paper proposes a new analytic model to obtain the optimal allocation that minimizes the total cost considering this new billing scheme. However, the optimization problem using this model is unmanageable due to its computational complexity. Therefore, other sub-optimal allocations strategies are proposed. The performance of these strategies (in terms of total allocation cost and computational effort) is analyzed in order to assess the influence of the time slot length. Allocation time slots of one hour, one minute and one second are compared. The experimental work is carried out using synthetic workloads based on two real public traces. The study concludes that the time slot of one minute offers the best trade-off between allocation cost and computational effort. The experimentation shows that the proposed strategy can save up to 14 percent over using the time slot of one hour.

Massive Random Access of Machine-to-Machine Communications in LTE Networks: Throughput Optimization With a Finite Data Transmission Rate

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

Photonic-Frame-Based TCP proxy architecture in optically interconnected data center networks

Optical switching is a good candidate for achieving the key requirements of high scalability, low latency and low power consumption in future large-scale data center networks (DCNs). However, circuit-based optical switching has a disadvantage of a long reconfiguration time. Packet-based optical switching also faces challenges such as the complexity of optical header processing and the difficulty of buffering in optical switches to resolve contention. To address these problems, we present a packet-switched optical network architecture design with a photonic frame wrapper that can generate variable-size photonic frames for optically interconnected intra-DCN. The proposed architecture forms hybrid DCN architecture consisting of a flattened optical switch layer for the data path and a conventional electrical switch layer for the control path. The photonic frame wrapper can increase bandwidth utilization by minimizing guard-time insertion in the optical switch domain. We also introduce a photonic frame wrapper with proxy solution to fully exploit the gain of the proposed architecture even in inter-DCN communication with long delays, which is compliant with standard TCP instances at end nodes. In intra-DCN, the average queuing delay is analyzed to find the efficient combinations between the guard time and time-slot length for the variable-sized photonic frame. We also analyzed the bandwidth overhead of guard time, to show it is comparable to that of Ethernet based DCN. In inter-DCN, the proxy solution demonstrates a noticeable TCP performance enhancement compared with the photonic frame wrapper in terms of bandwidth efficiency and end-to-end delay by theoretical and OPNET simulation analyses.

Reliability and Energy Efficiency Enhancement for Emergency-Aware Wireless Body Area Networks (WBANs)

Medium access control protocols based on time division multiple access (TDMA) can improve the reliability and efficiency of wireless body area network (WBAN). However, traditional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading. This paper presents two novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. Both techniques synchronize nodes adaptively while tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to IEEE 802.15.4 and IEEE 802.15.6 in both situations, the normal and emergency contexts. This improvement has been achieved in terms of packet loss, up to 90%, and energy consumption, up to 13%, confirming the significant enhancements made by the developed scheduling techniques.

Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis

This paper discusses an experimental study of the home appliances scheduling problem that incorporates realistic aspects. The residential load scheduling problem is solved while considering consumer’s preferences. The objective function minimizes the weighted sum of electricity cost by earning relevant incentives, and the scheduling inconvenience. The objective of this study is five-fold. First, it sought to develop and solve a binary integer linear programming optimization model for the problem. Second, it examined the factors that might affect the obtained schedule of residential loads. Third, it aimed to test the performance of a developed optimization model under different experimental scenarios. Fourth, it proposes a conceptual definition of a new parameter in the problem, the so-called “flexibility ratio”. Finally, it adds a data set for use in the literature on the home appliance scheduling problem, which can be used to test the performance of newly-developed approaches to the solution of this problem. This paper presents the results of experimental analysis using four factors: problem size, flexibility ratio, time slot length and the objective function weighting factor. The experimental results show the main and interaction effects, where these exist, on three performance measures: the electricity cost, inconvenience and the optimization model computation time.

Flexible real-time transmission scheduling for wireless networks with non-deterministic workloads

Wireless Sensor Networks (WSNs) are increasingly used in industrial applications such as the Internet-of-Things, Smart City technologies and critical infrastructure monitoring. Industrial WSNs often operate in a cluster or star configuration. To ensure real-time and predictable performance, link access is typically managed using time-slotted superframe methods. These methods generally use static and potentially inefficient slot assignments. In this paper, we propose to dynamically readjust time slot lengths as a technique to minimize overall energy consumption. Our approach combines real-time performance guarantees with energy conservation methods through a set of dynamic modulation based adaptive packet transmission scheduling algorithms that are designed to reclaim unused slot times. To support our reclaiming method in a wireless environment we introduce a novel low-power listening technique called reverse-low-power listening (RLPL) as part of an overall Hybrid Low-Power Listening (HLPL) protocol. We evaluate our algorithms using Castalia simulator against an oracle-based approach, and show that our dynamic slot reclaiming approach, coupled with HLPL, can introduce substantial power savings without sacrificing real-time support which may be a new approach towards improving industrial wireless standards.

Towards Concurrent Data Transmission: Exploiting Plasmid Diversity by Bacterial Conjugation.

The progress of molecular communication (MC) is tightly connected to the progress of nanomachine design. State-of-the-art states that nanomachines can be built either from novel nanomaterials by the help of nanotechnology or they can be built from living cells which are modified to function as intended by synthetic biology. With the growing need of the biomedical applications of MC, we focus on developing bio-compatible communication systems by engineering the cells to become MC nanomachines. Since this approach relies on modifying cellular functions, the improvements in the performance can only be achieved by integrating new biological properties. A previously proposed model for molecular communication is using bacteria as information carriers between transmitters and receivers, also known as bacterial nanonetworks. This approach has suggested encoding information into the plasmids inserted into the bacteria which leads to extra overhead for the receivers to decode and analyze the plasmids to obtain the encoded information. Another scheme, which is proposed in this paper, is to determine the digital information transmitted based on the quantity of bacteria emitted. While this scheme has its simplicity, the major drawback is the low-data rate resulting from the long propagation of the bacteria. To improve the performance, this paper proposes a distributed modulation scheme utilizing three bacterial properties, namely, engineering of plasmids, conjugation, and bacterial motility. In particular, genetic engineering allows us to engineer the different combinations of genes representing the different series of bits. When compared with binary density modulation and the M-ary density modulation, it is shown that the distributed modulation scheme outperforms the other two approaches in terms of bit error probability as well as the achievable rate for varying quantity of bacteria transmitted, distances, as well as time slot length.