Change Of Queue Length Research Articles

Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control

Wireless sensor networks (WSNs) act as a building block of Internet of Things and have been used in various applications to sense environment and transmit data to the Internet. However, WSNs are very vulnerable to congestion problem, resulting in higher packet loss ratio, longer delay and lower throughput. To address this issue, this paper presents a fuzzy sliding mode congestion control algorithm (FSMC) for WSNs. Firstly, by applying the signal-to-noise ratio of wireless channel to TCP model, a new cross-layer congestion control model between transmission layer and MAC layer is proposed. Then, by combining fuzzy control with sliding mode control (SMC), a fuzzy sliding mode controller (FSMC) is designed, which adaptively regulates the queue length of buffer in congested nodes and significantly reduces the impact of external uncertain disturbance. Finally, numerous simulations are implemented in MATLAB/Simulink and NS-2.35 by comparing with traditional control strategies such as fuzzy, PID and SMC, which show that the proposed FSMC effectively adapts to the change of queue length and has good performance, such as rapid convergence, lower average delay, less packet loss ratio and higher throughput.

Robust scheduling for flexible processing networks

Abstract Modern processing networks often consist of heterogeneous servers with widely varying capabilities, and process job flows with complex structure and requirements. A major challenge in designing efficient scheduling policies in these networks is the lack of reliable estimates of system parameters, and an attractive approach for addressing this challenge is to design robust policies, i.e. policies that do not use system parameters such as arrival and/or service rates for making scheduling decisions. In this paper we propose a general framework for the design of robust policies. The main technical novelty is the use of a stochastic gradient projection method that reacts to queue-length changes in order to find a balanced allocation of service resources to incoming tasks. We illustrate our approach on two broad classes of processing systems, namely the flexible fork-join networks and the flexible queueing networks, and prove the rate stability of our proposed policies for these networks under nonrestrictive assumptions.

Novel congestion control algorithms for a class of delayed networks

Time delays are often instability sources and give rise to undesired oscillation and performance degradation, so it is necessary to consider delay in general congestion control schemes. In this paper, a new system model for wireless sensor networks (WSNs) is established in discrete time, subsystems including delay are presented, and the overall model is achieved by blending these subsystems. For controller synthesis, common quadratic Lyapunov functions and a new approach based on nonquadratic Lyapunov functions are utilized, and a controller is designed to stabilize each subsystem. The controller synthesis results are expressed in terms of linear matrix inequalities. Moreover, the performance of our proposed scheme is considered and the decay rate is guaranteed. Finally, a set of novel congestion control schemes is derived for WSNs, and the resulting closed-loop systems are globally asymptotically stable in case of queue length changes and the consequent delay changes. The simulation results are also presented to illustrate the effectiveness of our proposed method.

Robust decentralized adaptive nonquadratic congestion control algorithm for a class of delayed networks

Due to the recent advances in communication networks, congestion control can be considered as of paramount importance and has spurred a significant amount of research. In this paper, the problem of congestion control in the presence of channel uncertainty is addressed in wireless sensor networks (WSNs). Since WSNs are normally set up in adverse conditions, robustness against environmental strains and also the ability to autonomously recover from error situations is mostly demanded. Based on nonlinear dynamics point of view, it is essential to consider delay in the analysis of general congestion control schemes. This paper presents a novel, robust decentralized adaptive nonquadratic congestion control scheme (RDANQCC), which is robust to queue length changes and the consequent delay changes. RDANQCC includes an adaptive back-off interval selection that works in concert with a robust controller. Using queue utilization and channel estimation algorithm, congestion can be detected and a suitable rate is enforced by adaptive back-off interval selection. An iterative linear matrix inequality (ILMI) is utilized to solve our control problem. Unlike previous schemes, varying desired queue length is considered in this paper and two cases are proposed for fixed and varying desired queue length and it is shown that in the latter our controller renders better performance compared with the former. The convergence analysis of RDANQCC is demonstrated by the use of nonquadratic Lyapunov-based approach. Extended simulation results demonstrate that RDANQCC renders better performance in comparison with other schemes available in the literature.

Real time queue length estimation for signalized intersections using travel times from mobile sensors

We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.

Markovian Controllable Queueing Systems with Hysteretic Policies: Busy Period and Waiting Time Analysis

We study Markovian queueing systems in which the service rate varies whenever the queue length changes. More specifically we consider controllable queues operating under the so-called hysteretic policy which provides a rather versatile class of operating rules for increasing and decreasing service rate at the arrival and service completion times. The objective of this paper is to investigate algorithmically the busy period and the waiting time distributions. Our analysis supplements the classical work of Yadin and Naor (1967) who focused on the steady-state probabilities of the system state.

M/M/1 Queueing Decision Processes with Monotone Hysteretic Optimal Policies

This paper studies an M/M/1 queueing decision process in which the arrival and service rates are chosen from a finite set whenever the queue length changes. Each choice has a switching cost depending on the chosen rates and those currently in use, and a usage cost depending on the chosen rates and the queue length; these switching and usage cost functions are submodular and satisfy additional technical conditions. The system objective is to find a policy for dynamically choosing the rates, based on the current rates and queue length, that minimizes the expected total discounted cost or average cost over an infinite horizon. We prove that there is a monotone hysteretic optimal policy in which the arrival and service rates are decreasing and increasing, respectively, in the queue length; there is a hysteresis (retardation) in the changing of the actions due to the switching costs. We establish this result by showing that such optimal policies exist for an equivalent discrete-time random walk decision process. Our results confirm a decade-old conjecture that the M/M/1 queue has a monotone hysteretic optimal control policy.