Data Queue Research Articles

Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks.

The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold () or the waiting time is equal to the aggregation timer (), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline () and the aggregation threshold () are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of and to reduce delay, and the nodes near the sink are set to a large value of and to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%.

Cooperative Secure Communication in Two-Hop Buffer-Aided Networks

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.

An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks

In Body Area Networks (BANs), how to achieve energy management to extend the lifetime of the body area networks system is one of the most critical problems. In this paper, we design a body area network system powered by renewable energy, in which the sensors carried by patient with energy harvesting module can transmit data to a personal device. We do not require any a priori knowledge of the stochastic nature of energy harvesting and energy consumption. We formulate a user utility optimization problem. We use Lyapunov Optimization techniques to decompose the problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an online resource allocation algorithm to achieve two major goals: (1) balancing sensors’ energy harvesting and energy consumption while stabilizing the BANs system; and (2) maximizing the user utility. Performance analysis addresses required battery capacity, bounded data queue length and optimality of the proposed algorithm. Simulation results verify the optimization of algorithm.

Reinforcement Learning-Based Adaptive Transmission in Time-Varying Underwater Acoustic Channels

This paper studies adaptive transmission in an underwater acoustic (UWA) point-to-point communication system that operates on an epoch-by-epoch basis for a long term. A fixed amount of information bits periodically arrive at the transmitter data queue, and wait for transmission via a number of packets within each epoch. To trade off energy consumption with transmission latency, the transmitter decides the transmission action at the beginning of each epoch, including to transmit or not, and the transmission power and the modulation-and-coding parameters, based on the data queue status and the predicted channel conditions in the current and future epochs. To describe both the fast fading and the large-scale shadowing of UWA channels, the channel within each epoch is characterized by a compound Nakagami-lognormal distribution, and the evolution of the distribution parameters is modeled as an unknown Markov process. Given that the channel can only be observed during active transmissions, we formulate the adaptive transmission problem as a partially observable Markov decision process, and develop an online algorithm in a model-based reinforcement learning framework. The algorithm recursively estimates the channel model parameters, tracks the channel dynamics, and computes the optimal transmission action that minimizes a long-term system cost. Emulated results based on channel measurements from two-field experiments demonstrate that the proposed algorithm achieves decent performance relative to a benchmark method that assumes perfect and non-causal channel knowledge.

Delay Skew Packet Flow Control in Wireless Systems With Dual Connectivity

The paper presents a new data flow controller for use in 4G and 5G wireless applications where the incoming internet data flow is split in support of multipoint downlink wireless transmission. The different paths of the data from the node of the split to the wireless transmission nodes and on to the mobile may then result in different travel times. The mobile can cope with small values of this delay skew by means of buffers and reordering protocols. The control objective is therefore to keep the delay skew within a prespecified interval of time, when the packets arrive in the mobile. The proposed algorithm solves the problem by cascade control. The packet dwell time of each wireless transmission node data queue is controlled by an inner loop, with the reference dwell times being determined by an outer delay skew control loop. The delay skew control loop exploits a deadzone to achieve interval control. In a further contribution the $\mathcal{L}_2$ -stability of the controller is analyzed for the dual connectivity functionality of the coming 5G cellular network deployment. A combined use of the input–output Nyquist- and Popov-criteria is required for the analysis. The performance is also evaluated by a laboratory testbed implementation.

IEC 61850 Client Redundancy for Substation Automation System

This paper presents an overview of System level redundancy implementation in an IEC61850 based Client application and method used to reduce recovery time after failure of the station level application (IEC 61850 Client, HMI and Database). The authors have presented the implementation of a dual Server hot-standby redundancy architecture, Network Driver (NDIS-Network Driver Interface Specification) for heartbeat communication, network teaming for redundant network redundancy and database replication for substation data integrity and availability of substation data/event. This paper also discusses different redundancy modules (Redundancy Manager, IEC 61850 Client Driver, HMI, Database Module) integration like API integration between Network driver (NDIS) and IEC 61850 Client application. The implementation of data queue in IEC 61850 Client to avoid data loss during server switch over is discussed in this paper. The authors have presented the various fail-over mechanisms in this system. As the redundancy has a direct impact on the cost and complexity of the system, the proposed solution in this paper has addressed the needs of application criticality and customer requirements.

Dynamic power allocation for a multiuser transmitter with hybrid energy sources

In this paper, we investigate the problem of dynamic power allocation for a multiuser transmitter supplied by hybrid energy sources in details. Specifically, we focus on the hybrid energy sources which include both the traditional power grid and various renewable sources whereby there are a few issues in considerations: (1) The energy harvested jointly from various renewable sources is time-varying and possibly unpredictable and is stored in a limited capacity buffer with battery leakage. (2) At the meantime, the data arrives randomly to the transmitter and queues according to the individual receivers to wait to be transmitted. (3) In addition, the wireless channels fluctuate randomly due to fading. Taking into account the time variant and dynamic features of this system, we develop a dynamic power allocation algorithm for the transmitter with the aim of minimizing the average amount of energy consumption from the power grid over an infinite horizon, subject to all data in queues cannot exceed a given deadline of receivers. The research question is formulated as a stochastic optimization problem, then we utilize Lyapunov optimization to exploit an online algorithm with low complexity, and it does not require prior statistical knowledge of the stochastic processes. Performance analysis of the proposed algorithm is carried out in theory, which shows that the proposed algorithm performs arbitrarily close to the optimal objective value; meanwhile, the algorithm ensures that the maximum delay of all data queues cannot exceed a given value. Finally, performance comparison shows that our proposed algorithm provides not only better performance but also less time delay than other two algorithms.

Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.

TARS

Efficient medium access control (MAC) is desirable for underwater sensor networks (UWSNs). However, designing an efficient underwater MAC protocol is challenging due to the long propagation delay of the underwater acoustic channel and spatial-temporal uncertainty. In this article, we propose a novel Traffic-Adaptive Receiver-Synchronized underwater MAC protocol, TARS, for throughput maximization. We divide time into equal-sized slots, each the size of one packet transmission time plus a guard time to cope with network dynamics. We adjust the packet transmission phase in a slot, determined by the sender-receiver distance, to align packet receptions for collision reduction. Both the sound propagation speed variation and the node mobility are considered in setting the transmission phase and slot size. We employ a queue-aware utility-optimization framework to determine the optimal transmission strategies dynamically, taking into account both the interference and data queue status. Extensive simulation results show that compared to the existing representative protocols, TARS achieves better performance with higher network throughput and lower packet delay (e.g., about 13%−146% higher in throughput and 13%−21% lower in delay than others in a mobile ad hoc network), as well as robustness under network mobility. Thus, TARS is highly suitable for mobile and traffic-varying UWSNs.

A Novel DC-Power Control Method for Cascaded H-Bridge Multilevel Inverter

The cascaded H-bridge converter has been widely used in industry. The ability of dealing with unequal power generation among dc links is necessarily required in many applications, such as large-scale photovoltaic power plants and electric vehicles. In this paper, a novel dc-power control method (PCM) is proposed to keep the ac-current balance and maintain power generation ratios of each dc source at their expected values. A simplified modulation strategy is introduced to implement the proposed method, in which the duration times are modified directly by the correction value derived from the PCM method. To calculate and adjust the power generation ratios, a method based on first-in first-out (FIFO) data queue is utilized. The limitation and ranges of power generation ratio are analyzed in detail. DC power per phase can be controlled accurately by the proposed method in variable-frequency condition, even under larger power generation ratios. Results obtained from simulations and experiments are presented to verify the feasibilities and superiorities of the proposed method.

Power–Delay Tradeoff in Wireless Powered Communication Networks

In this paper, we investigate the power–delay tradeoff for the newly emerging wireless powered communication networks, in which wireless powered devices (WPDs) harvest energy from a power station via wireless energy transfer in the downlink and then communicate with an information receiving station in the uplink. Each WPD is equipped with an energy buffer and a data buffer to store the random harvested energy and the bursty data arrivals, respectively. To minimize the time-averaged power consumption, a stochastic optimization problem is formulated subject to both constraints of data queue stability and harvested energy availability. By employing Lyapunov optimization theory, we transform the stochastic optimization problem into a series of deterministic problems, where each is further proved to become the standard convex optimization problem and, hence, can be effectively and optimally solved by standard convex optimization techniques. Correspondingly, we propose an Online Power and TIMe Allocation (Optima) algorithm, which requires no a priori distribution knowledge of channel states and data arrivals. Most importantly, the proposed algorithm achieves the power–delay tradeoff as $[\mathcal{O}(\text{1}/V),\mathcal{O}(V)]$ , with $V$ being a system control parameter, and provides a significant method to control the power–delay performance on demand in system design. Simulation results verify the theoretical analysis and validate the effectiveness of the proposed algorithm.

Event-Trigger Based Robust-Optimal Control for Energy Harvesting Transmitter

This paper studies an online algorithm for an energy harvesting transmitter, where the transmission (completion) time is considered as the system performance. Unlike the existing online algorithms which more or less require the knowledge on the future behavior of the energy-harvesting rate, we consider a practical but significantly more challenging scenario where the energy-harvesting rate is assumed to be totally unknown. Our design is formulated as a robust-optimal control problem which aims to optimize the worst-case performance. The transmit power is designed only based on the current battery energy level and the data queue length directly monitored by the transmitter itself. Specifically, we apply an event-trigger approach in which the transmitter continuously monitors the battery energy and triggers an event when a significant change occurs. Once an event is triggered, the transmit power is updated according to the solution to the robust-optimal control problem, which is given in a simple analytic form. We present numerical results on the transmission time achieved by the proposed design and demonstrate its robust-optimality.

Energy and Traffic Aware Full-Duplex Communications for 5G Systems

In this paper, we consider the problem of resource allocation in a dense small-cell network. Each small-cell base station is powered by a renewable energy source and operates in the full-duplex mode. We account for the rate-dependent energy term for data decoding into the total energy consumption at the small-cell base station. Owing to this new energy term, the transmitter and receiver operations now draw the energy from a common source. For a new energy consumption model and high interference scenario, which arises due to full-duplex communications, we formulate an energy and load aware resource management optimization problem under the energy causality and total transmit power constraints of the small-cell base station and uplink user equipments. In particular, the problem minimizes the data queue length of each network user equipment by jointly designing the beamformers, power, and sub-carrier allocation and their scheduling. Owing to the non-convexity of the problem, a global solution is inefficient; thus, we opt for the successive parametric convex approximation method to obtain a sub-optimal solution. This method solves for the convex approximate of the non-convex problem in each iteration and leads to faster convergence. For practical implementation, we further develop a distributed algorithm by using the dual decomposition framework, which relies on limited exchange of information between the involved base stations. Numerical simulations compare the network scenario which accounts for uplink channel rate-dependent energy consumption with that which ignores it. Results advocate the need for redesigning of the resource allocation scheme. In addition, numerical simulations also validate the usefulness of full-duplex communications over the half-duplex communications in terms of minimizing the sum data queue length of the users.

Performance Analysis of Buffer-Aided Relaying System Based on Data and Energy Coupling Queuing Model for Cooperative Communication Networks

We focus on the performance analysis of the buffer-aided relaying system which allows data and energy packets to arrive independently and depart interactively. First, we profile the cooperative relaying system model as a data arrival and energy arrival coupling queuing model. Considering the influence of channel condition on the data departure rate, a new relay transmit protocol which permits exhausting more energy packet to send one data packet in the bad channel environment is proposed. Second, the joint data packet and energy packet handling problem is ascribed to a Coupled Processor Queuing Model which could achieve its steady state transition probability by Quasi-Birth and Death method. Third, the expressions of throughput, delay, and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the analytical results under different data arrival rate, energy arrival rate, and relaying strategy.

Optimal Stochastic Power Control for Energy Harvesting Systems With Delay Constraints

This paper studies stochastic power control problems over a fading channel, where the transmitter randomly harvests renewable energies from environment and stores them in a battery for future data transmissions. Moreover, data packets are assumed to arrive at the data queue of transmitter with constant rate μ. To incorporate delay quality-of-service guarantees, two delay constraint models are separately considered, namely average delay model with maximum average delay constraint and statistical delay model with maximum delay outage probability constraint. Under each delay constraint model, the stochastic power control problem aims at maximizing μ considering the randomness of channel fading and energy harvesting (EH) processes. The resulting optimization problems can be formulated as infinite-horizon Markov decision processes. Under average delay model, the optimal power control policy needs to keep track of current data queue-length state in addition to the battery state. On the other hand, under statistical delay model, a sufficiently large queue-length region is assumed, hence, the optimal policy does not depend on the data queue-length state. We study various structural properties of the optimal control policies and develop online power control algorithms that converge to the optimal solutions without requiring statistical knowledge of channel fading and EH processes. By defining and learning the so-called post-decision state-value functions, the proposed learning algorithms require less complexity and converge faster than the conventional reinforcement learning algorithms. Numerical results demonstrate the effectiveness of the online learning algorithms for different delay constraint models and EH settings.

Diphase: Characterizing Packet Delay in Multi-Source Energy Harvesting Systems

We consider multi-source energy harvesting communication systems, where the energy harvested from two independent processes is used for the transmission of the data packets. The data packets arrive randomly and wait in a queue for accumulation of sufficient energy and for service completion of previously arrived packets. Thus, the data queue dynamics are influenced jointly by the energy arrival process, the data arrival process, and the data service process. This coupling between the data and energy queues makes an exact system analysis extremely hard, and has led researchers to resort to either computationally intensive numerical solutions or to make simplifying approximations. In this paper, we employ Diphase , a two phase queueing formulation, which decouples the wait stages for the energy arrival process and the service process, to derive closed-form expressions for the average packet delay and the probability of data packet loss due to buffer overflow. These expressions are shown to be exact when the service time is negligible, and robust for a relatively wide range of values of the average service time. We show that these expressions are useful in selecting system design parameters, which maximize the throughput while meeting the required quality of service constraints.

Optimal Partial Relaying for Energy-Harvesting Wireless Networks

In this paper, we asses the benefits of using partial relaying in energy-harvesting networks. We consider a system composed of a source, a relay, and a destination. Each of the source and the relay has energy-harvesting capability and generates its own traffic. The source is helped by the relay through a partial relaying network-level cooperation protocol. The relay regulates the arrivals from the source by accepting only a proportion of the successfully received packets at the relay. The relaying parameter, which determines the proportion of packets to be accepted, is selected based on the parameters of the network to ensure the stability of the source and the relay data queues. In this work, we provide an exact characterization of the stability region of the network. We derive the optimal value of the relaying parameter to maximize the stable throughput of the source for a given data arrival rate to the relay. Also, we compare the stability region of the proposed strategy with partial relaying to the stability regions of simple transmission strategies. Finally, we consider the problem of network utility optimization in which we optimize over the value of the relaying parameter for a given pair of data arrival rates for the source and the relay.

A joint data transmission and queue management for emergency application in delay-tolerant mobile sensor networks

A joint data transmission and queue management is proposed for emergency application in delay-tolerant mobile sensor networks DT-MSNs. Based on AHP method, data queue can be prioritised in an organised way via structuring hierarchy, comparing weight metrics and then making decision. With the joint transfers of control and initiate data it can achieve fast response to emergency and deal with the urgent case timely. Evaluation results show that the proposal outperforms the conventional schemes with more effective data management and lower transmission delay for urgent data.

Stochastic Optimal Control for Wireless Powered Communication Networks

In this paper, we propose a stochastic optimal control algorithm for the wireless powered communication networks (WPCNs), in which the access point (AP) supplies energy to wireless nodes by means of the RF energy transfer technology. The energy beamforming is used to enhance the RF energy transfer efficiency by concentrating the radiated power on target nodes. Each wireless node is equipped with an energy queue and a data queue. We propose an algorithm that minimizes the expected energy transmission power from the AP while stabilizing the data queues of all nodes. The proposed algorithm is an online algorithm that adaptively decides the beamforming vector, the data scheduling, and the data transmission power, only based on the current state of the energy and the data queues. The proposed algorithm dynamically steers the energy beam to nodes that currently have low energy in the energy queue. We apply the Lyapunov optimization technique to design such an algorithm. We mathematically prove that the proposed algorithm achieves the optimal performance.

A joint data transmission and queue management for emergency application in delay-tolerant mobile sensor networks

A joint data transmission and queue management is proposed for emergency application in delay-tolerant mobile sensor networks (DT-MSNs). Based on AHP method, data queue can be prioritised in an organised way via structuring hierarchy, comparing weight metrics and then making decision. With the joint transfers of control and initiate data it can achieve fast response to emergency and deal with the urgent case timely. Evaluation results show that the proposal outperforms the conventional schemes with more effective data management and lower transmission delay for urgent data.