This article aims to solve the optimal tracking problem (OTP) for a class of discrete-time (DT) nonlinear systems with completely unknown dynamics. A novel data-driven deterministic approximate dynamic programming (ADP) algorithm is proposed to solve this kind of problem with only input-output (I/O) data. The proposed algorithm has two advantages compared to existing data-driven deterministic ADP algorithms for the OTP. First, our algorithm can guarantee optimality while achieving better performance in the aspects of time-saving and robustness to data. Second, the near-optimal control policy learned by our algorithm can be implemented without considering expected control and enable the system states to track the user-specified reference signals. Therefore, the tracking performance is guaranteed while simplifying the algorithm implementation. Furthermore, the convergence and stability of the proposed algorithm are strictly proved through theoretical analysis, in which the errors caused by neural networks (NNs) are considered. At the end of this article, the developed algorithm is compared with two representative deterministic ADP algorithms through a numerical example and applied to solve the tracking problem for a two-link robotic manipulator. The simulation results demonstrate the effectiveness and advantages of the developed algorithm.

In this article, an adaptive vision-admittance-based leakage-type (LT) sliding mode control (SMC) is proposed to solve the uncertain robots’ compliant force control problem with actuator saturation. First, we design the vision-admittance-based model to generate an online reference trajectory concerning the predefined task trajectory and the interaction force command in the image feature space. Second, an adaptive SMC (ASMC) is created to track the generated reference image feature trajectory with high precision. An LT adaptive control law is applied in this controller to overcome the system uncertainties’ impacts in the situation where the upper bound of uncertainty is unknown. Moreover, an auxiliary dynamic term is taken into account in this controller’s design to solve the saturation nonlinearity problem. The feature tracking error can be guaranteed to be convergent according to the Lyapunov stability analysis. Finally, prototype experiments are conducted on a 6-degree-of-freedom (DOF) STEP SD7/700 robot to validate the superiority of the proposed control methodology.

In this article, a novel model-free dynamic inversion-based Q-learning (DIQL) algorithm is proposed to solve the optimal tracking control (OTC) problem of unknown nonlinear input-affine discrete-time (DT) systems. Compared with the existing DIQL algorithm and the discount factor-based Q-learning (DFQL) algorithm, the proposed algorithm can eliminate the tracking error while ensuring that it is model-free and off-policy. First, a new deterministic Q-learning iterative scheme is presented, and based on this scheme, a model-based off-policy DIQL algorithm is designed. The advantage of this new scheme is that it can avoid the training of unusual data and improve data utilization, thereby saving computing resources. Simultaneously, the convergence and stability of the designed algorithm are analyzed, and the proof that adding probing noise into the behavior policy does not affect the convergence is presented. Then, by introducing neural networks (NNs), the model-free version of the designed algorithm is further proposed so that the OTC problem can be solved without any knowledge about the system dynamics. Finally, three simulation examples are given to demonstrate the effectiveness of the proposed algorithm.

The problem of non-fragile pinning synchronization for complex networks is studied in this paper. Firstly, by using an inequality, which is introduced by using Newton-Leibniz formula, synchronization criterion is obtained by using novel mathematical skills. Secondly, a novel variable subintervals method is applied to expand the former results. Unlike previous results, the interval delay is divided into some dynamic variable sub-delays, which can introduce more weighting matrices. Thirdly, by resorting to Lyapunov function methods and some analysis techniques, the non-fragile pinning synchronization results are solved in terms of a set of LMI inequalities which are easy to be analyzed or computed. Finally, a numerical example is given to demonstrate the efficacy and efficiency of the proposed methods.

In wireless local area networks (WLANs), link-layer multicast is a promising technology for many multimedia applications, e.g., video streaming, as multicast frames can reach multiple clients simultaneously. However, the efficiency of multicast in WLANs is usually low since multicast frames are transmitted at a basic data rate to reach clients with poor channel quality. Moreover, the reliability of multicast cannot be guaranteed either, as multicast transmissions are not acknowledged. Some recent works have utilized smart antennas to improve multicast performance. However, most of them require customized hardware and are not designed for the latest IEEE 802.11 standard, 802.11n WLANs. More importantly, these works did not consider the impact of AP association strategies on the performance of link-layer multicast. In this paper, we study the problem of link-layer multicast in large-scale 802.11n WLANs where each AP is equipped with a smart antenna that supports multiple antenna patterns. Based on channel gains of various antenna patterns from different APs, we choose the associated AP for multicast clients, partition associated clients of each AP into multiple groups, select an antenna pattern and data rate for each group of client, and transmit multicast frames to each group once. We first examine the limitation of multicast in 802.11n WLANs and the benefits of smart antennas to multicast via experiments. We then introduce the system model and formulate the problem into an optimization problem, and prove its NP-hardness. The objective is to minimize the time to transmit a multicast frame to all clients while guaranteeing high packet reception ratio (PRR) for each client. For generic WLANs where APs are sparsely deployed, we propose an optimal algorithm under the condition that the PRR of antenna patterns and data rates are known for every client. For generic large-scale WLANs, we further propose an on-line algorithm in which the AP association strategy, the partition of clients, the antenna pattern and data rate of each group are adapted dynamically, based on PRR reports from clients. We have implemented the on-line algorithm on off-the-shelf WLAN products, and conducted extensive experiments and simulations to evaluate the performance. The results show that the proposed algorithm can significantly improve multicast performance compared to other schemes, and at the same time guarantee high PRR for all clients.

Recent studies have shown that significant benefit can be achieved in wireless sensor networks (WSNs) by employing mobile collectors for data gathering via short-range communications. A typical scenario for such a scheme is that a mobile collector roams over the sensing field and pauses at some anchor points on its moving tour such that it can traverse the transmission range of all the sensors in the field and directly collect data from each sensor. In this paper, we study the performance optimization of such mobile data gathering by formulating it into a cost minimization problem constrained by channel capacity, required minimum data uploads from each sensor and bound of total sojourn time at all anchor points. In order to provide an efficient and distributed algorithm, we decompose this global optimization problem into two subproblems that can be solved by each sensor and the mobile collector, respectively. We show that such decomposition can be characterized as a pricing mechanism, in which each sensor independently adjusts its payment for the data uploading opportunity based on the shadow prices of different anchor points. Correspondingly, we give an efficient algorithm to jointly solve the two subproblems. Our theoretical analysis demonstrates that the proposed algorithm can achieve the optimal data control for each sensor and the optimal sojourn time allocation for the mobile collector, which minimizes the overall network cost. Finally, extensive simulation results further validate that our algorithm achieves lower cost than the compared data gathering strategy.

As the latest amendment of IEEE 802.11 standard, 802.11n allows a maximum raw data rate as high as 600 Mbps, making it a desirable candidate for wireless local area network (WLAN) deployment. In typical deployment, the coverage areas of nearby access points (APs) usually overlap with each other to provide satisfactory coverage and seamless mobility support. Clients tend to associate (connect) to the AP with the strongest signal strength, which may lead to poor client throughput and overloaded APs. Although a number of AP association schemes have been proposed for IEEE 802.11 WLANs in the literature, the challenges brought by the new features in 802.11n have not been thoroughly studied nor the impact of legacy 802.11a/b/g clients in 802.11n WLANS on AP association. To fill in this gap, in this paper, we explore AP association for 802.11n with heterogeneous clients (802.11a/b/g/n). We first present a bi-dimensional Markov model to estimate the uplink and downlink throughput of clients and formulate AP association into an optimization problem, aiming at providing each client a bandwidth proportional to its usable data rate. Based on this Markov model, we propose an on-line AP association algorithm under the condition that each client can acquire timely information of all clients associated with nearby APs. Furthermore, for WLANs with densely deployed APs, we provide another on-line AP association algorithm with lower complexity, which takes full advantage of 802.11n transmissions by simply associating different types of clients with different APs. We have conducted extensive simulations and experiments to validate the proposed algorithms. The results show that our algorithms can significantly improve both 802.11n throughput and aggregated network throughput under various network scenarios, compared to previous AP association schemes. Our experiments also confirm the effectiveness of the algorithms in enhancing network throughput, maintaining proportional fairness among clients, and balancing load among APs.

Data gathering is a fundamental operation in various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multi-hop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Constructing a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability. Moreover, the algorithms also have a comparable energy efficiency and network lifetime to other algorithms.

Data gathering is a fundamental operation for various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multihop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Construction of a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability.

A wireless mesh network (WMN) is a special type of wireless ad-hoc network, which consists of mesh clients, mesh routers and gateways to the Internet, organized in a mesh topology. The mesh clients are often laptops, cell phones and other wireless devices. Mesh routers forward traffic between mesh clients and gateways. Despite a number of promising features provided by WMNs, such as low deployment cost, self-healing, etc., the throughput of WMNs is often limited by severe congestion and collisions, and thus cannot satisfy the increasing traffic demands of numerous applications. In this paper, we study how to maximize the throughput of IEEE 802.11n WMNs by joint routing and frame aggregation. Frame aggregation is to aggregate multiple frames into a large frame before transmission, to reduce communication overhead and alleviate collisions. We first show that previous frame aggregation strategies cannot achieve optimal network throughput. We then formulate the joint problem into a linear programming (LP) problem by considering traffic in the network as flow. As most previous algorithms for LP are centralized and difficult to deploy in large-scale WMNs, we propose a distributed algorithm to solve the formulated problem, in which each mesh router determines the amount of traffic flow for its adjacent links based on the traffic information of neighbors and interfering links. However, in realistic 802.11n WMNs, traffic is transmitted in frames instead of flow, and the traffic to different routers needs to be distinguished. Thus, we further provide an algorithm to determine the routing and frame aggregation strategy for each mesh router, using the traffic flow derived from the first algorithm. We have conducted extensive simulations to evaluate the proposed algorithms and the results demonstrate that the network throughput can be significantly improved compared with existing schemes.

Recently, cooperative retransmissions have exhibited great potentials in enhancing the reliability and efficiency of wireless communications by exploring spatial diversity. With cooperative retransmissions, a cooperative node helps retransmit an overheard frame if the frame from a sender fails to reach the destination. Several cooperative retransmission schemes have been proposed for wireless local area networks (WLANs) in the literature. However, most of them require explicit coordination between the sender and the cooperative node before each retransmission, which results in a non-negligible overhead. Moreover, these schemes are not designed for the latest IEEE 802.11n standard, and are incompatible with the frame aggregation and block ACK mechanisms of 802.11n. In this paper, we propose an efficient cooperative retransmission MAC (CAR-MAC) protocol that utilizes new features of 802.11n and is compatible with standard 802.11n transmissions. In CAR-MAC, all nodes periodically broadcast a C-Beacon message to release their retransmitting capability, and each node selects a cooperative node based on received CBeacon messages. If some sub-frames in the aggregated frame from the sender fail to reach the destination, the cooperative node retransmits the failed sub-frames together with its own new sub frames, such that overhead from cooperative retransmissions is amortized by normal frame transmissions. We have theoretically analyzed the improvement on network throughput brought by CAR-MAC protocol. In addition, we have conducted extensive simulations to evaluate CAR-MAC protocol under various channel conditions. Both theoretical and simulation results show that the proposed protocol can greatly improve network throughput and reduce packet delay, compared with the 802.11n standard and existing cooperative retransmission schemes.

In wireless local area networks (WLANs), link-layer multicast is a promising technology for many multimedia applications, e.g., video conference, as multicast frames can reach multiple clients simultaneously. However, the efficiency of multicast in WLANs is unsatisfactory since multicast frames are transmitted at low data rates to reach clients with poor channel quality. Moreover, the reliability of multicast cannot be guaranteed either, as multicast transmissions are not acknowledged. Some recent works have utilized smart antennas to improve multicast performance. But most of them require customized hardware and are not designed for the latest IEEE 802.11 standard, 802.11n WLANs. In this paper, we consider link-layer multicast in 802.11n WLANs with smart antennas. We partition clients into several groups, then select an antenna pattern from smart antennas and a multicast rate for each group, and transmit the same frame to each group. We first examine the gain of smart antennas and reliability of various 802.11n data rates for multicast in indoor WLANs via experiments. We then present the system model for multicast over smart antennas and formulate the problem into a mixed integer program. After that, we propose an optimal algorithm for the mixed integer program, under the condition that the packet reception ratio (PRR) of all antenna patterns and data rates is known for every client. As clients join and leave the network frequently and the wireless channel is time varying, we also propose an on-line algorithm that is able to adapt the partition of clients, antenna pattern and multicast rate for each group dynamically, based on PRR reports from clients. We have implemented the on-line algorithm on off-the-shelf WLAN products and conducted extensive experiments to evaluate the performance. The results show that the proposed algorithm can significantly improve multicast throughput compared to other strategies, and at the same time guarantee high PRR for all clients.

As the latest IEEE 802.11 standard, 802.11n allows a maximum physical data rate as high as 600Mbps, making it a desirable candidate for wireless local area network (WLAN) deployment. In WLANs, access points (APs) are often densely deployed, and thus neighboring APs should be assigned with orthogonal channels to avoid performance degradation caused by interference. It is challenging to find the optimal channel assignment strategy, as the number of channels is very limited. Many channel assignment schemes have been proposed for WLANs in the literature. However, most of them were not designed for 802.11n WLANs, and did not consider the challenges from the new channel bonding and frame aggregation mechanisms. Moreover, the impact of multi-rate clients on channel assignment is not fully investigated yet. In this paper, we study channel assignment in multi-rate 802.11n WLANs, aiming at maximizing the network throughput. We first present a network model and an interference model, and estimate the client throughput based on them. We then formulate the channel assignment problem into a throughput optimization problem. As the formulated problem is NP-hard, we propose a distributed channel assignment algorithm to provide practical solutions. We have conducted extensive simulations to evaluate the proposed algorithm and the results show that the network throughput can be significantly improved compared with existing schemes.

This paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time-varying delay. First, a novel augmented Lyapunov-Krasovskii functional (LKF) method is proposed to develop delay-dependent synchronization criteria for the networks, which makes use of more relaxed conditions by employing the new type of augmented matrices with Kronecker product operation. The proposed method can handle a multitude of Kronecker product operations in the LKF and alleviates the requirements of the positive definiteness of some conditional matrices which are usually considered in the existing methods for complex networks. This leads to a significant improvement in the performance of the synchronization criteria, i.e., less conservative synchronization results can be obtained. Meanwhile, the case of fast time-varying delay can also be handled by the proposed method. Furthermore, based on the derived criteria, a robust synchronization criterion is obtained for the system with uncertainties both in coefficient and coupling matrix terms. Since an expression based on linear matrix inequality is used, the proposed criteria can be easily checked in practice. Finally, numerical examples are provided to show the effectiveness of the proposed method.

As the latest amendment of IEEE 802.11 standard, 802.11n allows a maximum raw data rate as high as 300Mbps, making it a desirable candidate for wireless local area network (WLAN) deployment. In typical deployment, the coverage areas of nearby access points (APs) usually overlap with one another to provide satisfactory coverage and seamless mobility support. Clients tend to associate (connect) to the AP with the strongest signal strength, which might lead to poor client throughput and overloaded APs. Although a number of AP association schemes have been proposed for IEEE 802.11 WLANs in previous studies, none of them have considered the frame aggregation feature in 802.11n. Moreover, the impact of legacy 802.11a/b/g clients in 802.11n WLANS has not been considered in AP association. To fill in this gap, in this paper we explore AP association for 802.11n with heterogeneous clients (802.11a/b/g/n). We first formulate it into an optimization problem based on a bi-dimensional Markov model, aiming at providing clients with the bandwidth proportional to their highest physical data rates, and then propose two heuristic AP association algorithms that can efficiently make online decisions on AP association. We have also conducted extensive simulations and experiments to validate the proposed algorithms. Our simulation results show that under hotspot client distribution, the proposed algorithms can boost the throughput of 802.11n clients and overall throughput by 106% and 89%, respectively, compared to other AP association schemes. Experiments also confirm the effectiveness of the algorithms in enhancing aggregated throughput, maintaining proportional fairness among clients and balancing load among APs.

In wireless local area networks (WLANs), collision-free channel access is desirable for real-time services that require guaranteed bandwidth and bounded delay. In WLANs with a single access point (AP), collision-free access can be achieved by applying the point coordination function (PCF), where the AP polls all associated clients for data transmissions. However, in larger WLANs with multiple APs, if there is no inter-AP coordination mechanism, concurrent transmissions from nearby basic service sets (BSSs) may collide and thus degrade service performance even if all APs operate on the PCF mode. So far only few schemes have been proposed to resolve this problem, and the client throughput in these schemes is quite limited due to the inaccurate modeling of polling conflicts. In this paper, we study client polling in multi-AP WLANs, with the objective of providing high-throughput, collision-free channel access for each client, and maximizing network capacity. We first give a WLAN framework in which the PCF of all APs is coordinated and clients are polled in a time slotted manner.We then formulate client polling into a time slot allocation problem and propose a collision-free polling scheme consisting of three procedures: (1) a basic polling procedure that determines the minimum number of time slots required to poll every client once to obtain the polling frequencies of all clients; (2) a complementary polling procedure that makes extra polls for APs that have idle time slots without causing collisions, to improve spatial reuse of the network; (3) a backup poll selecting procedure that finds backup clients to poll in case the current polled client has no data to transmit, to utilize the otherwise wasted bandwidth. We have conducted extensive simulations and compared it with two existing schemes. The simulation results show that the proposed scheme can provide high throughput and uniform channel access time for all clients, while boosting spatial reuse 2 to 3 times compared to other schemes.

Recently, several multi-channel MAC protocols have been proposed for wireless sensor networks (WSNs) to improve network capacity and boost energy efficiency. In addition, cooperative multiple-input multiple-output (MIMO) technique has been shown to be able to significantly enhance the energy efficiency of WSNs if properly configured. However, these two promising techniques have not been jointly utilized in WSNs. In this paper, we explore such a joint design by proposing a novel MAC protocol for WSNs that takes advantage of both multiple channels and cooperative MIMO. Specifically, sensor nodes in a WSN are organized into clusters and each cluster head selects some cooperative nodes to help forward traffic to or receive from other clusters by utilizing cooperative MIMO technique. For intra-cluster communications, different channels are assigned to adjacent clusters to reduce collisions, while for inter-cluster communications, cooperative MIMO links are scheduled to improve energy efficiency and concurrent transmissions are enabled by assigning different channels to them. We carry out extensive simulations and the results demonstrate that the proposed protocol can significantly increase throughput and improve energy efficiency compared to other schemes. For example, for a WSN with 150 nodes in a 250m × 250m field, with five or more available channels, the protocol can achieve over three times saturated throughput while saving up to 17% energy per bit for inter-cluster communications.

Recent studies have shown that significant benefit can be achieved in wireless sensor networks (WSNs) by employing mobile collectors for data gathering via short-range communications. A typical scenario for such a scheme is that a mobile collector roams over the sensing field and pauses at some anchor points on its moving tour such that it can traverse the transmission range of all the sensors in the field and directly collect data from each sensor. In this paper, we study the performance optimization of such mobile data gathering by formulating it into a cost minimization problem constrained by the channel capacity, the minimum amount of data gathered from each sensor and the bound of total sojourn time at all anchor points. We assume that the cost of a sensor for a particular anchor point is a function of the data amount a sensor uploads to the mobile collector during its sojourn time at this anchor point. In order to provide an efficient and distributed algorithm, we decompose this global optimization problem into two subproblems to be solved by each sensor and the mobile collector, respectively. We show that such decomposition can be characterized as a pricing mechanism, in which each sensor independently adjusts its payment for the data uploading opportunity to the mobile collector based on the shadow prices of different anchor points. Correspondingly, we give an efficient algorithm to jointly solve the two subproblems. Our theoretical analysis demonstrates that the proposed algorithm can achieve the optimal data control for each sensor and the optimal sojourn time allocation for the mobile collector, which minimizes the overall network cost. Finally, extensive simulation results further validate that our algorithm achieves lower cost than the compared data gathering strategy.

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for WSNs by taking into account of the lossy nature of wireless links. We first formulate the one-hop clustering problem that maintains reliability as well as saves energy into an integer program and prove its NP-hardness. We then propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that can indicate both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. Extensive simulations have been conducted under a realistic link model and the results demonstrate that the proposed clustering algorithm can reduce the total energy consumption in the network and prolong network lifetime significantly compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.

In recent years, wireless mesh networks (WMNs) have been widely deployed to provide wireless access to the Internet. However, due to inter-link interference, the aggregated capacity of WMNs is limited, even with multiple channels. As a result, many links in WMNs are suppressed since interfering links cannot be active (i.e., transmitting packets) simultaneously. In this paper, we propose a joint design of channel assignment and space-division multiple access (SDMA) technique with the objective of maximizing the number of active links in WMNs. We assign different channels to transmission links based on their interference relationship to alleviate the interference. We also apply the SDMA technique to link scheduling, which enables two interfering links that share the same destination to communicate simultaneously on the same channel. By utilizing SDMA, more concurrent transmission links can be accommodated such that the network capacity can be greatly improved. We formulate this joint design into an optimization problem, prove its NP-hardness and then provide two heuristic algorithms to give practically good solutions to the problem. Our simulation results demonstrate that performance of the two heuristic algorithm is close to the optimal solution, and when 90% SDMA pairs are compatible, the proposed algorithms can increase the percentage of active links in a WMN by up to 40% as compared to non-SDMA schedules.