Data Transmission Scheduling Research Articles

Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective—the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments.

Reliability or Sustainability

As a typical cyber-physical system (CPS), smart water distribution networks require monitoring of underground water pipes with high sample rates for precise data analysis and water network control. Due to poor underground wireless channel quality and long-range communication requirements, high transmission power is typically adopted to communicate high-speed sensor data streams, posing challenges for long-term sustainable monitoring. In this article, we develop the first sustainable water sensing system, exploiting energy harvesting opportunities from water flows. Our system does this by scheduling the transmission of a subset of the data streams, whereas other correlated streams are estimated using autoregressive models based on the sound-velocity propagation of pressure signals inside water networks. To compute the optimal scheduling policy, we formalize a stochastic optimization problem to maximize the estimation reliability while ensuring the system’s sustainable operation under dynamic conditions. We develop data transmission scheduling (DTS), an asymptotically optimal scheme, and FAST-DTS, a lightweight online algorithm that can adapt to arbitrary energy and correlation dynamics. Using more than 170 days of real data from our smart water system deployment and conducting in vitro experiments to our small-scale testbed, our evaluation demonstrates that Fast-DTS significantly outperforms three alternatives, considering data reliability, energy utilization, and sustainable operation.

A Joint Routing and MAC Protocol for Transmission Delay Reduction in Many-to-One Communication Paradigm for Wireless Sensor Networks

We propose a joint routing and medium access control (MAC) protocol, named as JRAM, for reducing transmission delays in a many-to-one communication paradigm for wireless sensor networks (WSNs). Due to the wide variety of WSN applications, there is a need for protocol solutions optimized for specific application classes. JRAM is proposed for WSNs deployed for monitoring multiple events in the same geographic region which require prompt detection and response. In existing contention-based synchronous MAC protocols designed for this, a node gets only one chance to succeed in data transmission scheduling per cycle, and a sink can also only receive data packets from at most one node in a cycle. Therefore, endto-end transmission delay (E2ETD) and packet delivery ratio (PDR) of these protocols drastically degrade with the increase in event occurrence rate (EOR). In contrast, JRAM proposes a novel approach to provide k (k > 1) chances to a node to succeed in data transmission scheduling in a cycle, and also allows a sink to receive data packets from k nodes in the same cycle. This is done, in JRAM, by partitioning the network nodes into k disjoint sets and then using a novel cycle structure. We evaluate JRAM through extensive NS-2.35 simulations and compare its performance with existing pipelined data collection (PDC), adaptive data collection (ADC), and CROPMAC protocols, for different types of traffic loads and traffic patterns. Results suggest that in case of high EOR, JRAM outperforms PDC, ADC and CROPMAC both in terms of the E2ETD and the PDR.

Scheduling for Data Transmission in Multi-Hop IEEE 802.15.4e TSCH Networks

TSCH is one of the access behavior modes defined in the IEEE 802.15.4e standard. It combines time slotted access with multi-channel and channel hopping capabilities. TSCH is a trend used as the underlying architecture of the Internet of Things. However, IEEE 802.15.4e defines when the MAC executes a schedule but it does not specify how the schedule is built. This paper focuses on the problem of data transmission scheduling for TSCH network which uses the IEEE802 .15.4e TSCH as the MAC protocol. We adopt matching rule to resolve conflicts in the TSH network and matching scheduling algorithm(MSA) to solve the problem of communication resources allocation in tree topology. The scheduling algorithm MSA can avoid conflict, reduce the delay and energy consumption. OSA is proposed by further optimization of MSA. Through simulation experiments, the proposed method can effectively shorten the time delay, reduce the energy consumption and reduce the utilization rate of the channel.

A cross-layer flow schedule with dynamical grouping for mitigating larger-scale TCP incast

Data center network (DCN), a type of network featuring low delays and high bandwidths, exchanges information rapidly through high-performance network switches. Currently, DCN provides various distributed services to satisfy user requirements. However, the size of buffers in the switches of DCNs is limited, thus causing throughput collapse when transmission control protocol (TCP) is used for multiple-to-one transmissions. Dropped packets result in additional retransmission costs and cause a substantial decrease in the efficient use of network bandwidths. Such a decrease in network performance is called a TCP incast problem. Previous studies have typically focused on modifying the original TCP or increasing additional switch hardware costs; rarely have studies focused on the existing DCN environments. Therefore, this study proposes using a cross-layer flow schedule with dynamical grouping (CLFS-DG) scheme to reduce the effect of TCP incast in DCNs. In CLSF-DG, data transmission schedules are organized by a receiver using application-level information. Multiple data flows are then grouped for simultaneously transmission to improve TCP throughput and satisfy deadline requirements. CLFS-DG can be applied to inexpensive switches without extra hardware support, substantially reducing hardware costs. The simulation result indicated that CLFS-DG can effectively prevent TCP incast occurrence and ensure the quality of service in both local networks and fat-tree topologies typically applied in the DCN. A realistic multiple-to-one transmission in the one-hop local network scenario had been set up and tested. The average error rate of the simulation results and actual experimental results is small and about 4.6%. The results show that CLFS-DG can actually be applied to real large networks.

Joint scheduling of data transmission and wireless power transfer in multi-channel device-to-device networks

As the mobile traffic explodes, direct device-to-device communications attract much attention as one of next-generation technologies to increase the spatial spectrum efficiency in multichannel wireless networks. On the other hand, as battery-powered mobile devices interrupt user experience due to limited battery capacity, wireless power transfer has emerged as a convenient and perpetual power supply. However, current circuit technologies cannot support both energy harvesting and information decoding at the same time, and thus the time-division-multiple-access approach is often used for either data transmission or power transfer at a time, which requires joint scheduling of data transmissions and wireless power transfer. In this paper, we investigate the joint scheduling problem of multi-channel data transmissions and wireless power transfer, formulate the joint problem using a unified framework, and identify new difficulties in efficient resource allocation. We also develop a preliminary solution that achieves good empirical performance in single-hop D2D network scenarios.

Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information

This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then, the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. Numerical results indicate that the proposed online learning-based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay, and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning-based scheduling for practical systems.

A novel contention/reservation medium access control scheme for single-hop wireless networks

The major challenge pertaining to single-hop wireless networks is to design a medium access control (MAC) scheme to efficiently utilize the scarce wireless bandwidth; whereas the most popular solution, IEEE 802.11 distributed coordination function, achieves only limited performance because of the considerable idle time and the high rate of transmission collisions caused by the backoff procedure at high loads. In this paper, we propose a novel contention/reservation MAC scheme, which aims at ensuring low control overheads and collision-free data transmission to maximize system performance. Our scheme provides an efficient control contention resolution algorithm which resolve one or at least one successful reservation in a time frame, thus only a few numbers of control minislots are necessary for a number of active mobile stations contending for reservations. Moreover, with the help of the broadcast messages from the Access Point, all mobile stations determine a nearly-round-robin and collision-free data transmission schedule in a distributed manner, and also implicitly resolves the well-known hidden terminal problem. Extensive simulation results demonstrate that the proposed MAC scheme achieves exceptional system performance under a wide range of traffic loads and various system parameters, and also shown to be robust even when under attack by malicious mobile stations.

TAS-MAC

Duty cycling improves energy efficiency but limits throughput and introduces significant end-to-end delay in wireless sensor networks. In this article, we present a traffic-adaptive synchronous MAC protocol (TAS-MAC), which is a high-throughput, low-delay MAC protocol tailored for low power consumption. It achieves high throughput by adapting time division multiple access (TDMA) to a novel traffic-adaptive allocation mechanism that assigns time slots only to nodes located on active routes. TAS-MAC reduces the end-to-end delay by notifying all nodes on active routes of incoming traffic in advance. These nodes will claim time slots for data transmission and forward a packet through multiple hops in a cycle. The desirable traffic-adaptive feature is achieved by decomposing traffic notification and data-transmission scheduling into two phases, specializing their duties and improving their efficiency, respectively. Simulation results and experiments on TelosB motes demonstrate that the two-phase design significantly improves the throughput of current synchronous MAC protocols and achieves the similar low delay of slot-stealing-assisted TDMA with much lower power consumption.

Device-to-Device Communications for Energy Management: A Smart Grid Case

The transmission of simultaneous and latency-sensitive data puts forth a significant challenge for the smart grid communications. In this paper, we investigate the application of device-to-device (D2D) communications for the energy management in the electric distribution network. Specifically, we develop a D2D-assisted relaying framework to exploit the spatial diversity and the differentiated data rate requirements, which improves the spectral efficiency, especially for the scenarios that there are faults in the electric distribution network. We study the data transmission scheduling problem under the proposed D2D-assisted relaying framework, aiming to minimize the overall information loss rate, while taking into account the uncertainties in the communication latency. To this end, we first cast the data transmission scheduling problem as a two-stage stochastic programming problem and derive the solution. Then, we develop a real-time distributed data transmission scheduling scheme based on the sample path realizations. Extensive simulation results show significant performance improvement by using the proposed D2D-assisted relaying framework compared with two baseline frameworks for a variety of different cases.

An algorithm based on differential evolution for satellite data Transmission Scheduling

Data transmission task scheduling is one of the important problems in satellite communication. It can be considered as a combinatorial optimisation problem among satellite data transmission demand, visible time window and ground station resource, which is a NP-complete problem. In this paper, we propose a satellite data transmission task scheduling algorithm which searches for an optimised solution based on differential evolution algorithm framework. In its progress of evolution, the individual's evaluating procedure is improved by a modified 0/1 knapsack-based method. Extensive experiments are conducted to examine the effectiveness and performance of the proposed scheduling algorithm. Experimental results show that the scheduling results generated from the algorithm satisfy scheduling constraints and are consistent with the expectation.

Approach for earth observation satellite real-time and playback data transmission scheduling

The scheduling of earth observation satellites (EOSs) data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization (QDPSO) is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.

Energy Efficient Smart Sensor Network Routing Protocol using Node Scheduling

needs are going more technology dependent in last decade. Technology touches live everywhere and also reach the area where humans unable to reach. One of Such important application is Wireless Sensor Networks (WSNs). This helps to collect data from different conditions where human faces challenges to gather information about atmosphere or environment. Apart of its advantage, WSN suffer from many different challenges like limited sensor node electric capacity, pattern of node deployment, data sending mechanism etc. To manage WSN with optimum resource utilization various data transmission methods have been given in recent years. This paper proposed another better method for improving network life span and increase stability of WSN. Proposed method manages all sensor nodes energy power within intelligence manor by using ON-OFF scheduling of node data transmission. Few nodes in ON state sense data and send to base station for one round of data transmission while other nodes are in power saving OFF mode by doing nothing neither sense nor transmit data. But for next round nodes are in ON status nodes goes to OFF power saving modes to save power and OFF mode nodes sense and send data to base station. This method is compared with other technique using same parameter while simulation and proposed method outperform better in all aspects of WSN comparison. Keywordsefficient, Data transmission protocol, ON mode, OFF mode, WSN, scheduling

Real-Time <inline-formula> <tex-math notation="TeX">${\cal L}_{1}$</tex-math></inline-formula> Adaptive Control for Uncertain Networked Control Systems

${\cal L}_{1}$ adaptive control is an appealing technology for control of systems in the presence of uncertainties. It addresses the issue of providing guaranteed transient performance and robustness for a class of adaptive architectures. This technical note studies the real-time output-feedback ${\cal L}_{1}$ adaptive tracking over real-time networks. Event-triggered communication protocols are presented to schedule data transmission between the plant and the controller. We show that under the proposed scheme the difference between the states of the resulting networked control systems and an ideal model is uniformly bounded, and the bound can be further reduced by improving the communication quality. Stability conditions, in terms of event thresholds, allowable transmission delays, and control parameters, are provided, which can serve as guidance in real-time scheduling.

A management architecture for client‐defined cloud storage services

SummaryCloud service providers offer virtual resources to users, who then pay for as much as they use. High‐speed networks help to overcome the limitation of geographical distances between clients and cloud servers, which encourage users to adopt cloud storage services for data backup and sharing. However, users use only a few cloud storage services because of the complexity of managing multiple accounts and distributing data to store. In this paper, we propose the client‐defined management architecture (CLIMA) that redefines a storage service by coordinating multiple cloud storage services from clients. We address practical issues of coordinating multiple cloud service providers using a client‐based approach. We implement a prototype as a realization of CLIMA, which achieves both reliability and privacy protection using erasure code and higher performance by optimally scheduling data transmission. We use our prototype to evaluate the benefits of CLIMA on commercial cloud storage service providers. Finally, CLIMA empowers clients to increase the manageability and flexibility of cloud storage services. Copyright © 2015 John Wiley & Sons, Ltd.

A data transmission algorithm for distributed computing system based on maximum flow

Data skew can lead to load imbalance and longer computation time in the distributed computing system. To avoid data skew and reduce the data computation time, it is necessary to transmit the data to appropriate machines, this may however take too much network resources. How to balance the computational resources and the network resources is a problem. In this paper, we introduce a computation model called distributed two-phase model, in which the process of a task can be divided into two independent phases: data transmission and data computation. Suppose an upper bound of relative computation time is given, we show how to schedule data transmission with minimum resources, such as data transmission time and occupied bandwidth, to meet the demand. In this paper, we present a novel algorithm to minimize data transmission time and network bandwidth usage in the data transmission phase, with the conditions that an upper bound of relative computation time of data computation phase is given. Moreover, the number of nodes that participate in data computation phase is also reduced, in this way, the computational resources are saved. The simulation results show that the occupied bandwidth can be reduced effectively (about 70 %) in the situation of large-scale data sets and large number of nodes. Our algorithm is also shown to be available in replication situation.

Implementation of several key functionalities of remote sensing data transmission for unmanned aerial vehicles

Remote sensing for unmanned aerial vehicles (UAVs) is becoming the preferred method for Earth observations. Data and command communication between an airborne remote sensing load and the ground terminal is crucial for the realization of real-time and smart observation for remote sensing. However, the current remote sensing observation equipment for UAVs cannot meet these requirements. In order to solve this problem, a real-time and smart remote control and data transmission system for UAVs is designed. The design and implementation of several key functions are presented, including multitask and multithread data transmission, transmission resumption, and task scheduling by priority.

Optimal scheduling for data transmission between mobile devices and cloud

Mobile cloud computing has emerged as a new computing paradigm promising to extend the capabilities of resource-constrained mobile devices. In this new paradigm, mobile devices are enabled to offload computing tasks, report sensing records, and store large files on the cloud through wireless networks. Therefore, efficient data transmission has become an important issue affecting user experiences on mobile cloud. Considering the limited battery energy of mobile devices and different application requirements on transmission delay, this study presents an online control algorithm (OPERA) based on the Lyapunov optimization theory for optimally scheduling data transmission between mobile devices and cloud. The OPERA algorithm is able to make control decisions on application scheduling, interface selection and packet dropping to minimize a joint utility of network energy cost and packet dropping penalty, without requiring any statistical information of traffic arrivals and link throughputs. Rigorous analysis and extensive simulations have demonstrated its distinguished performance in terms of utility optimality, system stability and service delay.

An adaptive neighbour detection scheme for rapid configuration of wireless sensor networks

The purpose of this paper is to represent an adaptive neighbour detection scheme for rapid sensor network configuration, especially in wireless sensor network WSN systems. In typical WSNs, the sensor nodes have no knowledge about the locations of other nodes and the connections between them after deployment. For the purpose of network configuration and future data transmission schedule assignments, neighbour detection process should be carried out, but this work are usually ignored in most communication protocol designs. In our real network establishment, it is proved that neighbour detection is critically important at the beginning configuration state of the entire network and when the topology is changed. Therefore, an adaptive neighbour detection scheme is introduced, which uses signalling packet broadcasting and the corresponding adaptive back-off mechanism to guarantee a high connectivity detection probability. The work is verified by simulation and testbed experiments, and has been used in practical large scale sensor networks.

Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors

Wireless technologies are pervasive to support ubiquitous healthcare applications. However, RF transmission in wireless technologies can lead to electromagnetic interference (EMI) on medical sensors under a healthcare scenario, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI to medical sensors, we propose a joint power and rate control algorithm under game theoretic framework to schedule data transmission at each of wireless sensors. The objective of such a game is to maximize the utility of each wireless user subject to the EMI constraints for medical sensors. We show that the proposed game has a unique Nash equilibrium and our joint power and rate control algorithm would converge to the Nash equilibrium. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.