Average Transmission Delay Research Articles

EFFICIENCY EVALUATION OF SCHEDULING ALGORITHMS FOR DELAY-SENSITIVE TRAFFIC IN OFDM DOWNLINK

Introduction: Common introduction of industrial control systems based on wireless networks leads to the emergence of a new way of using wireless networks, called the Industrial Internet of Things (IIoT). This framework assumes a large but finite number of users for which you have to provide the transmission of short messages with low delay and limited delay variation. Purpose: Efficiency evaluation of resource allocation algorithms for delay-sensitive traffic in a downlink OFDM channel. Methods: A n OFDM system is presented as a set of M/G/1 queuing systems. A convex optimization problem is introduced, whose solution determines the lower boundary of the average delay of message transmission in delay-sensitive traffic over the set of scheduling algorithms under study. Results: An algorithm is proposed for calculating the lower boundary of the average delay. This algorithm is fed by system parameters: the average message size, the input flow intensity, and the average maximum throughput for each device connected to the base station. Based on these input parameters, the optimization problem is introduced and solved by a numerical method. The solution of the optimization problem determines the lower boundary of the average message transmission delay. Practical relevance : The obtained result can be used by system developers for plan ning and deploying OFDM networks . It can also serve as a reference when developing Ultra- Reliable and Low Latency Communication technology .

A Novel Load Balancing Scheduling Algorithm for Wireless Sensor Networks

Clustering is a well-known approach to cope with large nodes density and efficiently conserving energy in wireless sensor networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Distributed Energy efficient Adaptive Clustering Protocol (DEACP) with data gathering and Load-balancing is proposed. The DEACP approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed DEACP protocol increases the profit of energy, and prolongs the network lifetime duration from 32 to 40% compared to DEEAC reference protocol and from 25 to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to fist node die, and 25.5% better than FEMCHRP with respect to last node die while maintaining the average data transmission delay. We found also that DEACP achieved 66.5% and 40.6% more rounds than DEEAC and FEMCHRP respectively.

Edge-based compression and classification for smart healthcare systems: Concept, implementation and evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the network edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart healthcare systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make autonomous decisions for smart health applications. In particular, we present and implement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low computational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%.

TMED: A Spider-Web-Like Transmission Mechanism for Emergency Data in Vehicular Ad Hoc Networks

The vehicular ad hoc network (VANET) is an emerging mobile ad hoc network, which is an important component of the Internet of Things and has been widely applied in intelligent transportation systems in recent years. For large-scale VANETs, it is important to design efficient transmission schemes for time critical emergency data. Greedy perimeter stateless routing protocol is a typical geographic-based routing protocol and greedy perimeter coordinator routing protocol is typical for map-based but they do not consider the QoS of the transmission link and hence are not suitable for emergency data transmissions. Some bioinspired protocols and situation-aware protocols are more suitable for emergency situations. However, they have some limitations in terms of the computational complexity and convergence rate which can cause large time delays. In this paper, we propose a novel spider-web-like transmission mechanism for emergency data (TMED) in vehicular ad hoc networks, in which a spider-web-like transmission model combining geographic information system and electronic maps is established. In this mechanism, the request-spiders and confirmed-spiders are sent out to obtain the transmission path from a source vehicle to a destination vehicle to improve the packet delivery ratio and average transmission delay of emergency data. TMED combines a dynamic multipriority message queue management method with a restricted greedy forwarding strategy based on position prediction to significantly reduce the end-to-end delay of packets. We use SUMO and NS2 to simulate TMED in a city scenario and compare its performance with GPSR, GPCR, and ACAR based on the packet delivery ratio, average transmission delay, and routing overhead. The simulation results show that TMED outperforms these previously proposed protocols.

Cooperative Edge Caching in User-Centric Clustered Mobile Networks

With files proactively stored at base stations (BSs), mobile edge caching enables direct content delivery without remote file fetching, which can reduce the end-to-end delay while relieving backhaul pressure. To effectively utilize the limited cache size in practice, cooperative caching can be leveraged to exploit caching diversity, by allowing users served by multiple base stations under the emerging user-centric network architecture. This paper explores delay-optimal cooperative edge caching in large-scale user-centric mobile networks, where the content placement and cluster size are optimized based on the stochastic information of network topology, traffic distribution, channel quality, and file popularity. Specifically, a greedy content placement algorithm is proposed based on the optimal bandwidth allocation, which can achieve $(1-{1/e})$ -optimality with linear computational complexity. In addition, the optimal user-centric cluster size is studied, and a condition constraining the maximal cluster size is presented in explicit form, which reflects the tradeoff between caching diversity and spectrum efficiency. Extensive simulations are conducted for analysis validation and performance evaluation. Numerical results demonstrate that the proposed greedy content placement algorithm can reduce the average file transmission delay up to 45 percent compared with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the optimal clustering is also discussed considering the influences of different system parameters.

An Efficient IR-HARQ Power Allocation for Relay-Aided Cellular Networks

Energy-efficient and reliable data transmission is a challenging task in wireless relay networks (WRNs). Energy efficiency in cellular networks has received significant attention because of the present need for reduced energy consumption, thereby maintaining the profitability of networks, which in turn makes these networks “greener”. The urban cell topography needs more energy to cover the total area of the cell. The base station does not cover the entire area in a given topography and adding more number of base stations is a cost prohibitive one. Energy-efficient relay placement model which calculates the maximum cell coverage is proposed in this work that covers all sectors and also an energy-efficient incremental redundancy-hybrid automatic repeat request (IR-HARQ) power allocation scheme to improve the reliability of the network by improving the overall network throughput is proposed. An IR-HARQ power allocation method maximizes the average incremental mutual information at each round, and its throughput quickly converges to the ergodic channel capacity as the number of retransmissions increases. Simulation results show that the proposed IR-HARQ power allocation achieves full channel capacity with average transmission delay and maintains good throughput under less power consumption. Also the impact of relaying performance on node distances between relay station and base station as well as between user and relay station and relay height for line of sight conditions are analyzed using full decode and forward (FDF) and partial decode and forward (PDF) relaying schemes. Compared to FDF scheme, PDF scheme provides better performance and allows more freedom in the relay placement for an increase in cell coverage.

The impact of the superposition coding concept on admission control strategy in OFDMA-based network

Admission control is a key mechanism to manage the increasing number of the simultaneous demanding services, requiring a desired Quality of Service (QoS) in a spectrum efficient manner. To address this issue, we investigate in this work, the use of the superposition coding technique to increase the system capacity through multiuser diversity exploitation. We propose a novel joint admission control and superposition coding formalism based on different utility functions: Opportunistic (OPSC), Great Fairness (GFSC) and Proportional Fairness (PFSC). Simulation results show the superiority of our proposed approaches over other by providing higher mean of served VoIP users and higher throughput while maintaining an average VoIP packet transmission delay lower than 0.6 ms.

Proactive Caching Over Cloud Radio Access Network With User Mobility and Video Segment Popularity Awared

Caching at the edge of wireless networks is a promising means of alleviating the heavy burden of backhaul links and reducing the latency of transmission and handover in a cloud radio access network (C-RAN). Although some effective caching schemes have been introduced to C-RAN, most of them are not designed with consideration of both user mobility and video segment popularity, which have influence on the performance of wireless caching. Therefore, this paper investigates the above-mentioned two factors for a cache-enabled C-RAN, and proposes two proactive caching strategies that are based on user mobility only, and both user mobility and video segment popularity, respectively. We design a novel controller, which is able to fully utilize the computational and storage resources, and acts the role of cache decision making for the baseband unit pool and remote radio heads. Numerical simulation results demonstrate that the proposed schemes significantly outperform traditional strategies by 20% and 16% in terms of average data transmission delay and cache hit rate, respectively.

Learning-Based Delay-Aware Caching in Wireless D2D Caching Networks

Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device(D2D) communication is performed. D2D communication underlaid with cellular networks is also a new paradigm for the upcoming wireless systems. By allowing a pair of adjacent D2D users to communicate directly, D2D communication can achieve higher throughput, better energy efficiency and lower traffic delay. In this work, we propose an efficient learning-based caching algorithm operating together with a non-parametric estimator to minimize the average transmission delay in D2D-enabled cellular networks. It is assumed that the system does not have any prior information regarding the popularity of the files, and the non-parametric estimator is aimed at learning the intensity function of the file requests. An algorithm is devised to determine the best <file,user> pairs that provide the best delay improvement in each loop to form a caching policy with very low transmission delay and high throughput. This algorithm is also extended to address a more general scenario, in which the distributions of fading coefficients and values of system parameters potentially change over time. Via numerical results, the superiority of the proposed algorithm is verified by comparing it with a naive algorithm, in which all users simply cache their favorite files, and by comparing with a probabilistic algorithm, in which the users cache a file with a probability that is proportional to its popularity.

Modeling and Analysis of Slotted ALOHA Systems With Energy Harvesting Nodes and Retry Limit

We analyze the performance of slotted ALOHA systems with energy harvesting nodes and the retry limit by developing a node-centric two-dimensional discrete-time Markov chain (DTMC) model. We consider not only the number of energy packets consumed when a data packet is transmitted but also a general probability distribution of an energy packet arrival process. We assume that the capacities of data and energy buffer at a node are one packet and $E$ packets, respectively. According to the concept of the equilibrium point analysis, we derive a fixed point equation with respect to the ratio of nodes transmitting a data packet. The accuracy of theoretical results obtained from the fixed point equation is verified by computer simulation. Numerical results under Poisson arriving process of energy packets indicate that throughput, the offered traffic, and the discard probability demonstrate the weak symmetric relationship between the data packet generation probability and the average number of arriving energy packets. However, the average transmission delay rapidly increases when the average number of arriving energy packets becomes small.

Improving Common Control Channel Capacity and Performance for Cognitive Radio Networks

Cognitive radio (CR) is a promising solution to cope with the inefficient usage of the frequency spectrum. CR lets its users leverage empty or idle parts of spectrum opportunistically. The medium access control (MAC) sub-layer in CR based networks is playing a crucial role in controlling user’s access to the shared medium and their interaction with Primary Users as well. Coordination and controlling users’ access to common control channel (CCC) at MAC sublayer can be a challenging issue in this type of networks. High availability of this channel is very important and crucial, so designing a mechanism which contributes to this, is of great importance. The CCC’s limited capacity alongside a large number of contending nodes for it causes channel saturation and consequently, leads to its unavailability. The paper introduces a useful method to compute supported saturation capacity of the CCC. It also introduces a new access mode for CCC called “Channelization”, to improve saturation capacity for it. The effect of “Channelization” scheme is also investigated on average packet delay, and according to it desirable parameters for channelization is calculated. Analytical analysis alongside simulation result shows that the channelization method can have a great impact on the increasing saturation capacity, and lower average packet transmission delay.

Ant colony optimization based load balancing routing and wavelength assignment for optical satellite networks

An ant colony optimization (ACO) based load balancing routing and wavelength assignment (RWA) algorithm (ALRWA) was put forward for the sake of achieving a fairy load balancing over the entire optical satellite networks. A multi-objective optimization model is established considering the characteristic of global traffic distribution. This not only employs the traffic intensity to modify the light path cost, but also monitors the wavelength utilization of optical inter-satellite links (ISLs). Then an ACO algorithm is utilized to solve this model, leading to finding an optimal light path for every connection request. The optimal light path has the minimum light path cost under satisfying the constraints of wavelength utilization, transmission delay and wavelength-continuity. Simulation results show that ALRWA performs well in blocking probability and realizes efficient load balancing. Meanwhile, the average transmission delay can meet the basic requirement of real-time business transmission.

Performance analysis and saturation bound research of cyclic-quorum multichannel MAC protocol based on Markov chain model

In high diversity node situation, single-channel MAC protocols suffer from many collisions. To solve this problem, the research of multichannel MAC protocol has become a hotspot. And the cyclic quorum-based multichannel (CQM) MAC protocol outperformed others owing to its high frequency utilization. In addition, it can avoid the bottleneck that others suffered from and can be easily realized with only one transceiver. To obtain the accurate performance of CQM MAC protocol, a Markov chain model, which combines the channel hopping strategy of CQM protocol and IEEE 802.11 distributed coordination function (DCF), is proposed. The metrics (throughput and average packet transmission delay) are calculated in performance analysis, with respect to node number, packet rate, channel slot length and channel number. The results of numerical analysis show that the optimal performance of CQM protocol can be obtained in saturation bound situation. And then we obtain the saturation bound of CQM system by bird swarm algorithm (BSA). Finally, the Markov chain model and saturation bound are verified by Qualnet platform. And the simulation results show that the analytic and simulation results match very well.

Rate allocation and relaying strategy adaption in wireless relay networks

In this paper, we propose a dynamic rate allocation and relaying strategy adaption scheme for data transmission over wireless relay networks, where autonomous nodes expose node-selfishness, i.e. being unwilling to forward other nodes’ data packets, for saving their energy resources. Aided by an incentive mechanism, we develop a virtual node-selfishness queue (VSQ) to portray the node’s dynamic selfish characteristic in terms of its energy resource and incentives. Then, a stochastic optimization model is employed to maximize the average network throughput whilst keeping the network stable and bounding the node-selfishness. The problem of stochastic optimization is further decomposed into two subproblems via the Lyapunov optimization theory, which corresponds to the rate allocation for source and the relaying strategy adaption for autonomous relay, respectively. After that, a joint rate allocation and relaying strategy adaption algorithm is developed to accommodate the wireless network only according to the current network state information, i.e. the data queue sate information, VSQ information at relays and the channel state information of the links. The explicit tradeoff between network throughput and average data transmission delay is theoretically proven. Simulation results validate the theoretical analysis of our proposed scheme.

On the design of an embedded wireless sensor network for aircraft vibration monitoring using efficient game theoretic based MAC protocol

This paper presents the design of a wireless sensor network architecture suitable for monitoring vibration on an aircraft. The main objective is to improve the flight safety by detecting vibration intensity among all the components of the airplane structure and communicating this information to the cockpit computer to react before any component can reach resonance. Therefore, the study of sensor nodes deployment for specific types of airplanes and the definition of appropriate network topology are conducted. Furthermore, to enhance the responsiveness of the structural health monitoring system, a medium access protocol based on game theory was developed. The main idea is to adjust the wake time period of a sensor node according to the sensed vibration level. This parameter will be used to configure the game-based medium access protocol. The performance evaluation work shows that the proposed vibration and game-based communication scheme enhances the transmission efficiency as it reduces the packet loss and the average transmission delay.

Proactive vs. reactive multipath routing for mobility enabled RPL

The IPv6 routing protocol for low power and lossy networks (RPL) was proposed as a routing standard for wireless sensor networks (WSNs). However, the RPL performance drastically degrade in the case of nodes' mobility. Therefore, we propose in this paper multipath routing schemes to adapt RPL to mobility scenarios. The first mobility-based braided multipath RPL (MBM-RPL) scheme establishes an alternative path braided with the primary one to replace the loose link (the up to fail link) along the first network route. The second proactive multipath mobility-based RPL (PMM-RPL) approach establishes all node disjoint routes between each node in the network and the sink. Data would not be duplicated on secondary paths until a primary path vulnerability detection. Both MBM-RPL and PMM-RPL performance are evaluated in terms of packet loss rate (PLR), average transmission delay (ADT) and the generated network overhead using the Cooja/Contiki simulation environment.

A multi‐channel cooperative clustering‐based MAC protocol for V2V communications

AbstractThe Internet of vehicles (IoV) is an emerging networking technology, which can support information sharing and interactions among users, vehicles, and infrastructures. Various applications can be provided by IoVs, and they have very different quality‐of‐service (QoS) requirements. It is a great challenge to design an efficient MAC protocol to meet the different QoS demands of various applications in IoVs, because of unreliable links and high vehicle mobility. On the other hand, cooperative communication is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. In this paper, a multi‐channel cooperative clustering‐based MAC (MCC‐MAC) protocol, under the Dedicated Short Range Communication (DSRC) multi‐channel architecture, is presented to improve the transmission reliability of safety messages and provision QoS for different applications in IoVs. Further, we analyze the performance of MCC‐MAC, in terms of average transmission delay. In addition, extensive simulations with ns‐2 are conducted to demonstrate the performance of the proposed MCC‐MAC. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A novel hybrid medium access control protocol for inter-M2M communications

A scalable medium access control (MAC) protocol is crucial for machine-type devices to access the channel simultaneously in a machine-to-machine (M2M) network. There can be two types of scenarios in an M2M network. First, where the M2M devices communicate with the base station one-by-one, and second, where the M2M devices directly communicate with each other within a group or cluster without any human intervention (Inter-M2M communication). Considering the second type of scenario, the devices can use basic contention or reservation-based MAC protocols. But with such a large number of M2M devices, adaptability, and scalability become bottlenecks. Therefore, in this paper, we propose a novel scalable hybrid-MAC protocol, which combines the benefits of both contention-based and reservation-based medium access schemes. We assume that the contention and reservation portion of M2M devices is a frame structure, which mainly has two parts: contention interval (CI) and data transmission interval (DTI). The devices contend for the channel access during CI. After contention, the successful devices win time-slots and transmit data packets during DTI. In our proposed hybrid-MAC scheme, each M2M device is IEEE 802.11 DCF enabled, and within each time slot during DTI, the devices share data with each other. The analytical and simulation results show that the proposed hybrid-MAC protocol performs better than slotted-ALOHA, p-persistent CSMA, and TDMA in terms of aggregate throughput, average transmission delay, channel utility, and energy consumption.

An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks

Clustering is a well known approach to cope with large nodes density and efficiently conserving energy in Wireless Sensor Networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Hierarchical Energy-Balancing Multipath routing protocol for Wireless Sensor Networks (HEBM) is proposed. The HEBM approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed HEBM protocol increases the profit of energy, and prolongs the network lifetime duration from 32% to 40% compared to DEEAC reference protocol and from 25% to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to FND (Fist node die), and 25.5% better than FEMCHRP with respect to LND (last node die) while maintaining the average data transmission delay. We found also that HEBM achieved 66.5% and 40.6% more rounds than DEEAC and FEMCHRP respectively.

Delay-Aware Wireless Powered Communication Networks—Energy Balancing and Optimization

In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs’ random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay.