Youxian Sun

Distributed Collaborative Control for Industrial Automation With Wireless Sensor and Actuator Networks

Wireless sensor and actuator networks (WSANs) bring many benefits to industrial automation systems. When a control system is integrated by a WSAN, and particularly if the network scale is large, distributed communication and control methods are quite necessary. However, unreliable wireless and multihop communications among sensors and actuators cause challenges in designing such systems. This paper proposes and evaluates a new distributed estimation and collaborative control scheme for industrial control systems with WSANs. Extensive results show that the proposed method effectively achieves control objectives and maintains robust against inaccurate system parameters. We also discuss how to dynamically extend the scale of a WSAN with only local adjustments of sensors and actuators.

Time Synchronization in WSNs: A Maximum-Value-Based Consensus Approach

This paper considers time synchronization in wireless sensor networks. When the communication delay is negligible, the maximum time synchronization (MTS) protocol is proposed by which the skew and offset of each node can be synchronized simultaneously. For a more practical case where the intercommunication delays between each connected node are positive random variables, we propose the weighted maximum time synchronization (WMTS), which is able to counteract the impact of random communication delays. Despite the clock offset that cannot be synchronized, WMTS can synchronize the clock skew completely in expectation and achieve acceptable synchronization accuracy. For both protocols, we provide rigorous proofs of global convergence as well as the upper bounds of their convergence time. Compared with existing consensus-based synchronization protocols, the main advantages of our protocols include: 1) a faster convergence speed so that the synchronization can be achieved in a finite time for MTS, and in a finite time in expectation for WMTS, respectively; 2) simultaneous synchronization of both skews and offsets; and 3) random communication delays can be handled effectively. Numerical examples are presented to demonstrate the effectiveness of the proposed protocols.

Utility-based asynchronous flow control algorithm for wireless sensor networks

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results.

Energy-Efficient Cooperative Spectrum Sensing by Optimal Scheduling in Sensor-Aided Cognitive Radio Networks

A promising technology that tackles the conflict between spectrum scarcity and underutilization is cognitive radio (CR), of which spectrum sensing is one of the most important functionalities. The use of dedicated sensors is an emerging service for spectrum sensing, where multiple sensors perform cooperative spectrum sensing. However, due to the energy constraint of battery-powered sensors, energy efficiency arises as a critical issue in sensor-aided CR networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. In this paper, we divide the sensors into a number of nondisjoint feasible subsets such that only one subset of sensors is turned on at a period of time while guaranteeing that the necessary detection and false alarm thresholds are satisfied. Each subset is activated successively, and nonactivated sensors are put in a low-energy sleep mode to extend the network lifetime. We formulate such problem of energy-efficient cooperative spectrum sensing in sensor-aided CR networks as a scheduling problem, which is proved to be NP-complete. We employ Greedy Degradation to degrade it into a linear integer programming problem and propose three approaches, namely, Implicit Enumeration (IE), General Greedy (GG), and λ-Greedy (λG), to solve the subproblem. Among them, IE can achieve an optimal solution with the highest computational complexity, whereas GG can provide a solution with the lowest complexity but much poorer performance. To achieve a better tradeoff in terms of network lifetime and computational complexity, a brand new λG is proposed to approach IE with the complexity comparable with GG. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance.

Mobility and Intruder Prior Information Improving the Barrier Coverage of Sparse Sensor Networks

The barrier coverage problem in emerging mobile sensor networks has been an interesting research issue due to many related real-life applications. Existing solutions are mainly concerned with deciding one-time movement for individual sensors to construct as many barriers as possible, which may not be suitable when there are no sufficient sensors to form a single barrier. In this paper, we aim to achieve barrier coverage in the sensor scarcity scenario by dynamic sensor patrolling. Specifically, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensors current movement strategy is derived from the information of intruder arrivals in the past. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. We study the scenario where sensors are moving on two barriers and propose two heuristic algorithms to guide the movement of sensors. Finally, we generalize our results to work for different intruder arrival models. Through extensive simulations, we demonstrate that the proposed algorithms have desired barrier coverage performances.

Multi-Channel Assignment in Wireless Sensor Networks: A Game Theoretic Approach

In this paper, we formulate multi-channel assignment in Wireless Sensor Networks (WSNs) as an optimization problem and show it is NP-hard. We then propose a distributed Game Based Channel Assignment algorithm (GBCA) to solve the problem. GBCA takes into account both the network topology information and transmission routing information. We prove that there exists at least one Nash Equilibrium in the channel assignment game. Furthermore, we analyze the sub-optimality of Nash Equilibrium and the convergence of the Best Response in the game. Simulation results are given to demonstrate that GBCA can reduce interference significantly and achieve satisfactory network performance in terms of delivery ratio, throughput, channel access delay and energy consumption.

Simultaneous stabilization via static output feedback and state feedback

In this paper, the simultaneous stabilization problem is considered using the matrix inequality approach. Some necessary and sufficient conditions for simultaneous stabilizability of strictly proper multi-input/multi-output (MIMO) plants via static output feedback and state feedback are obtained in the form of coupled algebraic Riccati inequalities. It is shown that any such stabilizing feedback gain is the solution of some coupled linear quadratic control problems where every cost functional has a suitable cross term. A heuristic iterative algorithm based on the linear matrix inequality technique is presented to solve the coupled matrix inequalities. The effectiveness of the approach is illustrated by numerical examples.

Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks

As the binary sensing model is a coarse approximation of reality, the probabilistic sensing model has been proposed as a more realistic model for characterizing the sensing region. A point is covered by sensor networks under the probabilistic sensing model if the joint sensing probability from multiple sensors is larger than a predefined threshold ε. Existing work has focused on probabilistic point coverage since it is extremely difficult to verify the coverage of a full continuous area (i.e., probabilistic area coverage). In this paper, we tackle such a challenging problem. We first study the sensing probabilities of two points with a distance of d and obtain the fundamental mathematical relationship between them. If the sensing probability of one point is larger than a certain value, the other is covered. Based on such a finding, we transform probabilistic area coverage into probabilistic point coverage, which greatly reduces the problem dimension. Then, we design the ε-full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so that the network lifetime can be prolonged as much as possible. We also theoretically derive the approximation ratio obtained by FCO to that by the optimal one. Finally, through extensive simulations, we demonstrate that FCO outperforms the state-of-the-art solutions significantly.

Energy provisioning in wireless rechargeable sensor networks

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition, and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Technical Communique: Two Degree-of-Freedom Smith Predictor for Processes with Time Delay

In this paper, the conventional Smith predictor is modified, which leads to significant improvements in its regulatory capacities for reference inputs and disturbances. The proposed modification utilizes the existing prediction of the conventional time delay compensator and decouples the setpoint response from the disturbance response. The setpoint response and the disturbance response of the closed-loop system are adjusted by two parameters, respectively. This allows the designer to optimize them simultaneously. Illustrative examples are provided to show the advantages of the modified scheme.