Low Access Delay Research Articles

An effective data placement strategy for IIoT applications

SummaryWith the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge‐cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge‐cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on ‐constraint and Lagrangian relaxation, to reduce the data access time and meanwhile limit the data placement cost to an ideal level. As a result, our proposed data placement strategy can effectively reduce data access time and control data placement costs. Simulation and comparative analysis results have demonstrated the validity of our proposed strategy.

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Priority-based load-adaptive preamble separation random access for QoS-differentiated services in 5G networks

Fifth generation (5G) networks provide connectivity for several services including enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC) and massive Internet of things (mIoT). With the accelerated increase in the number of devices connected to the networks, and the diversity in quality-of-service (QoS) requirements, an appropriate modification in protocol design is required. Initial access in 5G networks is carried out based on the random access (RA) procedure through the random access channel (RACH). However, the limited resources, i.e. preambles, of the RACH causes intensive collisions among the massive number of contending devices, which result in low success probability and high access delay, causing failure to meet QoS requirements for different services. This paper introduces a priority-based load-adaptive preamble separation (PLPS) RA scheme in which RACH resources are separated between devices according to the service class priority and load estimation. The number of preambles assigned for each class of device is updated before each random access opportunity (RAO), based on the arrival load estimation, aimed at increasing the RACH throughput and therefore reducing the access delay for eMBB and URLLC devices. The proposed scheme is evaluated through a mathematical analysis model and extensive simulations. The results show that the proposed PLPS RA scheme succeeds in achieving the different QoS requirements of the three considered services, while increasing the RACH throughput by approximately 140%, compared to the benchmark which indicates the efficiency of the proposed scheme.

Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks

In the complex and dynamically varying underwater acoustic (UWA) channel, cooperative communication can improve throughput for UWA sensor networks. In this paper, we design a reasonable relay selection strategy for efficient cooperation with reinforcement learning (RL), considering the characteristics of UWA channel variation and long transmission delay. The proposed scheme establishes effective state and reward expression to better reveal the relationship between RL and UWA environment. Meanwhile, simulated annealing (SA) algorithm is integrated with RL to improve the performance of relay selection, where exploration rate of RL is dynamically adapted by SA optimization through the temperature decline rate. Furthermore, the fast reinforcement learning (FRL) strategy with pre-training process is proposed for practical UWA network implementation. The whole proposed SA-FRL scheme has been evaluated by both simulation and experimental data. The simulation and experimental results show that the proposed relay selection scheme can converge more quickly than classical RL and random selection with the increase of the number of iterations. The reward, access delay and data rate of SA-FRL can converge at the highest value and are close to the ideal optimum value. All in all, the proposed SA-FRL relay selection scheme can improve the communication efficiency through the selection of the relay nodes with high link quality and low access delay.

Profit-Aware Edge Server Placement

In a 5G network, mobile-edge computing (MEC) plays a key role in providing low access delay services. The placement of edge servers not only determines the quality of services on the user side but also affects the profit of running a MEC system. In this article, we study how to properly place edge servers so as to guarantee the access delay and maximize the profit of edge providers. We first propose a profit model which involves both access delay and energy consumption. In this model, we take the 5G user plane function (UPF) into consideration to calculate access delay for the first time. Then, we devise a particle swarm optimization-based algorithm to optimize the profit. In the algorithm, we introduce a weight value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> to guarantee the access delay and assign base stations properly. Moreover, a service-level agreement is adopted to balance the tradeoff between access delay and energy consumption. We take advantage of our 5G network emulator called mini5Gedge and data set from Shanghai Telecom to conduct massive experiments. The results show that our algorithm stands out in terms of achieving the highest profit.

Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks.

In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder.

TARA: An Efficient Random Access Mechanism for NB-IoT by Exploiting TA Value Difference in Collided Preambles

To meet the tremendous demand of Internet of Things (IoT) applications, the 3rd Generation Partnership Project (3GPP) has specified the Narrowband IoT (NB-IoT) standard. However, collisions in the random access (RA) channel of NB-IoT can be severe due to mismatch between frequent random access attempts by a huge number of devices and the limited radio resources. In this paper, a new random access mechanism called time-alignment-value based random access (TARA) is designed to improve the efficiency of random access of NB-IoT. The key mechanism of TARA is to conduct quick retries upon failure by exploiting the difference of time-alignment (TA) values in collided preambles. To analyze TARA rigorously, a theoretical model of TARA is derived, and its validity is verified via simulations. Further simulations are carried out to evaluate TARA with respect to different system parameters. Comparisons with existing schemes are also conducted. Both analytical and simulation results show that, under various system parameters, TARA achieves 30% higher success probability of random access, 75% higher throughput, and 40% lower access delay than the original slotted Aloha mechanism of NB-IoT. It also highly outperforms other random access schemes.

On Fast Retrial for Two-Step Random Access in MTC

In machine-type communication (MTC), a group of devices or sensors may need to send their data packets with certain access delay limits for delay-sensitive applications or real-time Internet-of-Things (IoT) applications. In this case, 2-step random access approaches would be preferable to 4-step random access approaches that are employed for most MTC standards in cellular systems. While 2-step approaches are efficient in terms of access delay, their access delay is still dependent on retransmission strategies. Thus, for a low access delay, fast retrial that allows immediate retransmissions can be employed as a retransmission strategy. In this article, we study 2-step random access approaches with fast retrial as a buffered multichannel ALOHA with a fast retrial and derive an analytical way to obtain the Quality-of-Service (QoS) exponent for the distribution of queue length so that key parameters can be decided to meet QoS requirements in terms of access delay. Simulation results confirm that the derived analytical approach can provide a good approximation of QoS exponent.

MPDMAC-SIC: Priority-based distributed low delay MAC with successive interference cancellation for multi-hop industrial wireless networks

Communications in industrial applications such as wireless factory automation demand different timing requirements. Providing timely medium access of the critical traffic and its prioritization over regular traffic at the same time is a significant challenge in industrial wireless networks. Successive Interference Cancellation (SIC) technique is an effective way to decrease access delay by allowing multiple packets reception concurrently. A series of novel Medium Access Control (MAC) protocols are proposed to differentiate access delay for various traffic types or exploit SIC for unique traffic type. Our protocol in this paper (MPDMAC-SIC) is the first priority based distributed MAC protocol that employs SIC to provide low access delay and accommodate different types of traffic for multi-hop industrial wireless networks. Two major contributions of our work are: first, we propose a low delay protocol for priority based multi-hop industrial wireless networks by enabling multiple packet reception on power domain with SIC. In MPDMAC-SIC, a power contention procedure other than traditional RTS/CTS contention in CSMA/CA DCF is introduced to allocate proper transmitting power for transmitters. Second, MPDMAC-SIC is modeled by discrete time Markov chain to minimize access delay by optimizing window size in power contention. According to the simulation results, MPDMAC-SIC performs better on access delay and packet arriving rate, compared to the existing good performing priority based CSMA/CA in both single-hop and multi-hop networks.

A Grant-Free Random Access Scheme for M2M Communication in Massive MIMO Systems

A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input–multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time–frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.

Cyclical NOMA Based UAV-Enabled Wireless Network

In order to achieve high spectral efficiency and low access delay, this paper introduces cyclical non-orthogonal multiple access (NOMA) into unmanned aerial vehicle (UAV)-enabled wireless network. It allows the UAV to communicate with multiple ground users in the same time–frequency resources, cyclically. The minimum throughput over all ground users is maximized by jointly optimizing multiuser communication scheduling with cyclical NOMA and UAV trajectory. It turns out that the maximization of minimum throughput is a mixed integer non-linear non-convex optimization problem. In this paper, this problem is decoupled into two blocks, i.e., the optimization of multiuser communication scheduling with cyclical NOMA and the optimization of UAV trajectory. Then, a joint optimization algorithm is proposed based on the block coordinate descent method. The simulation results demonstrate that the proposed joint optimization method can double the minimum throughput, compared with cyclical TDMA. In addition, it reduces users’ average access delay and UAV’s flying range under the same minimum throughput.

Spatiotemporal Model for Uplink IoT Traffic: Scheduling and Random Access Paradox

The Internet-of-Things (IoT) is the paradigm where anything will be connected. There are two main approaches to handle the surge in uplink (UL) traffic that the IoT is expected to generate, namely, scheduled UL (SC-UL) and random access uplink (RA-UL) transmissions. SC-UL is perceived as a viable tool to control quality-of-service levels while entailing some overhead in the scheduling request prior to any UL transmission. On the other hand, RA-UL is a simple single-phase transmission strategy. While this obviously eliminates scheduling overheads, very little is known about the scalability of RA-UL. At this critical junction, there is a dire need to analyze the scalability of these two paradigms. To that end, this paper develops a spatiotemporal mathematical framework to analyze and assess the performance of SC-UL and RA-UL. The developed paradigm jointly utilizes stochastic geometry and queuing theory. Based on such a framework, we show that the answer to the scheduling versus random access paradox actually depends on the operational scenario. Particularly, the RA-UL scheme offers low access delays but suffers from limited scalability, i.e., cannot support a large number of IoT devices. On the other hand, SC-UL transmission is better suited for higher device intensities and traffic rates.

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things.

With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

DARS: A dynamic adaptive replica strategy under high load Cloud-P2P

In Cloud-P2P system, replica strategy is utilized for obtaining low access delay and high load balance, which requires paying more attention to the time of replica creation. Most of current replica strategies usually utilize the method of afterwards adjustment fixed threshold. However, these strategies may increase a large number of overload nodes and then lead to aggregate effect of being overloaded, especially under high load condition. To deal with the problem, this paper proposes a Dynamic Adaptive Replica Strategy (DARS) based on the node’s overheating similarity. DARS addresses the problem of replica creation time, including obtaining the replica creation opportune moment and finding the optimal replica placement node, by a decentralized self-adaptive manner. Based on the overheating similarity, DARS applies fuzzy membership function to obtain the replica creation opportune moment, which enables the node to create replica before its overload. Meanwhile, DARS adopts the fuzzy clustering analysis method to find out the node with high node degree and low load which is used as optimal placement node to store replicas. Extensive experiments demonstrate that DARS obtains superior performances in access latency around 15%∼20% on average and better load balance than other similar methods under high load condition.

A performance comparison of in-band full duplex and dynamic TDD for 5G indoor wireless networks

In-band full duplex has emerged as a solution for high data rate and low access delay for 5G wireless networks after its feasibility has been demonstrated. However, the impact of the in-band full duplex on the system-level performance of multi-cell wireless networks has not been investigated thoroughly. In this paper, we conduct an extensive simulation study to investigate the performance of in-band full duplex for indoor 5G small cell wireless networks. Particularly, we compare the in-band full duplex with static and dynamic time division duplexing schemes which require much less hardware complexity. We examine the effects of beamforming and interference cancellation under various traffic demands and asymmetry situations in the performance comparison. Our objective is to identify under which condition and with which technology support the in-band full duplex becomes advantageous over the simpler duplexing schemes. Numerical results indicate that for highly utilized wireless networks, in-band full duplex should be combined with interference cancellation and beamforming in order to achieve a performance gain over traditional duplexing schemes. Only then in-band full duplex is considered to be advantageous at any number of active mobile stations in the network and any downlink to uplink traffic proportion. Our results also suggest that in order to achieve a performance gain with the in-band full duplex in both links, the transmit power of the access points and the mobile stations should be comparable.

Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access

For transmitting Explosive Bursts Big Data of Mobile Cloud Computing applications, the 4G LTE/LTE-A standards are specified to provide extreme high data rate and low access delay for various real-time-demanded cloud services. In the uplink, data packet transmissions of different classes of traffic of various UEs need randomly contend for the limited number of preambles through the Uplink RACH channel time slots. Clearly, the extremely explosive data contentions certainly yield serious collisions, and then significantly increase access delay and packet dropping rate. That is, the quality of service (QoS) of the delay-sensitive-based real-time traffic and the loss-sensitive-based non-real-time traffic cannot be guaranteed satisfyingly. For overcoming the critical random access issue in cloud services over 4G LTE-A, 3GPP specifies the Uniform Distribution Backoff Procedure and Access Class Barring (ACB) as the random access mechanism. The Random Access CHannel (RACH) for random contentions in 3GPP LTE-A neglects some key factors: 1) different classes of traffic requiring different delay bounds, 2) how to reducing collision probability, 3) intensive congestion traffic and 4) differentiating the collision domains. This paper thus proposes an adaptive random contention approach (ARC) that consists of three phases: 1) Sigmoid-based Access Class Barring algorithm, 2) Dynamic Preamble Selection Range (DPSR) algorithm, and 3) Dynamic Initial Backoff (DIB) algorithm. The main contribution of ARC is based on the adaptive Sigmoid feature analysis of Cumulative Distribution Function of Normal Distribution according to the successful contention probability and the RACH congestion state. Numerical results demonstrate that the proposed approach outperforms the compared approaches in collision probability, goodput and access delay. Furthermore, the mathematical analytical model for the proposed approach is analyzed. The analysis result is very close the simulation result. It justifies the correctness and efficiency of the proposed approach.

Context-Aware Dynamic Resource Allocation for Cellular M2M Communications

While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions and degrade the successful probability as well as delay of network connection establishment. To address this issue, current LTE spec has specified access class barring (ACB) and enhanced access barring (EAB), but both methods lack a contention detection method to decide when to activate it as well as a proper way to dynamically adjust the parameter. In this work, we first proposed a contention detection method that can work with current long-term evolution (LTE) standard and is easy to be implemented. To further improve the random access performance in dynamic world, we proposed a context-aware dynamic resource allocation (CADRA) mechanism, which is a two-phase method to resolve random access contention: Phase I for the estimation of random access attempts, and Phase II for resource allocation. By CADRA, high resource efficiency and low random access delay can be achieved without prior knowledge about random access arrival traffic, and thus this approach is competent in diverse applications and scenarios of Internet of Things (IoT). Simulation results show that the proposed contention detection method works great with ACB and EAB. Our proposed CADRA has good performance in resource efficiency and delay while satisfying success probability.

A Cross-Layer MAC Protocol for Underwater Acoustic Sensor Networks

One of the major challenges for the deployment of underwater acoustic sensor networks is the development of a medium access control (MAC) protocol catering for the harsh underwater environment. In particular, an underwater MAC protocol should provide high end-to-end throughput, low channel access delay, and fair share of the scarce network bandwidth. In this paper, a cross-layer MAC protocol is proposed. It interacts with a price-based rate allocation scheme at the network layer. To accurately reflect the clique constraint of the wireless medium, the clique-based price is generalized to act as the congestion signal, which controls the end-to-end rates of multihop flows. The MAC protocol then schedules contention-free packet transmissions of single-hop subflows in each maximum clique. Both the MAC protocol and rate allocation algorithm are simple and direct, thus owning low computational complexity. Through analysis and simulation, we show that the proposed MAC protocol enables multihop flows to acquire the max-min fair share of the network bandwidth from the end-to-end perspective.

How far is Facebook from me? Facebook network infrastructure analysis

Facebook is today the most popular social network with more than one billion subscribers worldwide. To provide good quality of service (e.g., low access delay) to their clients, FB relies on Akamai, which provides a worldwide content distribution network with a large number of edge servers that are much closer to FB subscribers. In this article we aim to depict a global picture of the current FB network infrastructure deployment taking into account both native FB servers and Akamai nodes. Toward this end, we have performed a measurement-based analysis during a period of two weeks using 463 Planet- Lab nodes distributed across 41 countries. Based on the obtained data we compare the average access delay that nodes in different countries experience accessing both native FB servers and Akamai nodes. In addition, we obtain a wide view of the deployment of Akamai nodes serving FB users worldwide. Finally, we analyze the geographical coverage of those nodes, and demonstrate that in most of the cases Akamai nodes located in a particular country service not only local FB subscribers, but also FB users located in nearby countries.

Short-term fairness in slotted WDM rings

Single-hop WDM ring networks are promising architectures for future broadband access and metro networks. However, ring networks exhibit significant fairness issues, which must be handled by a fairness enforcing protocol. Fairness is usually ensured over a time window of several network propagation delays. Thus, data flows might experience large access delays which might be not compatible to support time-sensitive applications. We solve this issue proposing the Multi-Fasnet protocol, which is able to enforce fairness in a relative short time scale, in the order of few propagation delays, without trading off the aggregated throughput network performance. We discuss Multi-Fasnet limitations and propose several novel strategies that achieve high and fair network throughput as well as low, bounded and fair access delays.