Quanmin Ye

Cross-Layer Schemes for Reducing Delay in Multihop Wireless Networks

End-to-end delay is an important QoS metric in multihop wireless networks such as sensor networks and mesh networks. End-to-end delay is defined as the total time it takes for a single packet to reach the destination. It is a result of many factors including the length of the route and the interference level along the path. In this paper we address how to minimize end-to-end delay jointly through optimizing routing and link layer scheduling. We present two cross-layer schemes, a loosely coupled cross-layer scheme and a tightly coupled cross-layer scheme. In the loosely coupled cross-layer scheme, routing is computed first and then the information of routing is used for link layer scheduling; in the tightly coupled scheme, routing and link scheduling are solved in one optimization model. The two cross-layer schemes involve interference modeling in multihop wireless networks with omnidirectional antenna. A sufficient condition on conflict-free transmission is established, which can be transformed to polynomial-sized linear constraints, and a linear program based on the sufficient condition is developed. Through simulation, we show that the proposed routing and scheduling schemes can outperform their counterparts in each layer, and the integrated cross-layer schemes are superior to the combination of the existing routing and scheduling schemes.

Transmission scheduling based on a new conflict graph model for multicast in multihop wireless networks

In multicast applications, the end-to-end delay from the source to a group member is determined by the multicast tree topology and the waiting time at each relay node. This paper addresses when the multicast tree is given how to schedule wireless nodes for transmission so that network delay is minimized. We first model the conflict relation among wireless transmissions in a conflict graph, and then we compute a transmission schedule based on an Integer Linear Programming (ILP) model. Since solving ILP problem is NP-hard, a heuristic is designed to solve the ILP problem. The resulting schedule is conflict-free, which is guaranteed by the feasibility of the ILP model. Simulation results show significant reduction of delay when compared with a First Come First Serve (FCFS) scheduling policy.

A Combinatorial Solution for scheduling spatial multiplexing in MIMO-based ad hoc networks

In a MIMO-based ad hoc network, the conflict relation between transmissions is fundamentally changed due to the multiple packet reception capability of MIMO nodes. A MIMO node can receive from multiple data streams at the same time, which significantly increases data throughput. To fully utilize the additional spectrum capacity enabled by the MIMO technology, it is important that transmissions from different nodes are coordinated. The coordination scheme needs to consider both data stream multiplexing and transmission time scheduling for a maximum performance gain. In this paper, we use an optimization framework to jointly consider MIMO link spatial multiplexing and scheduling while the routing information is given. A linear program-based algorithm is proposed, and simulation results show it is advantageous over the spatial multiplexing scheme without joint design of scheduling.

Network coding and coding-aware scheduling for multicast in wireless networks

Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this paper, we develop a network coding method to address these two issues, i.e., decodability and delay, for multi-source multi-destination unicast and multicast sessions. We use linear programming to find the most efficient coding design solution with guaranteed decodability. To reduce network relay, we develop a scheduling algorithm to minimize the extra coding/decoding delay and store-and-forward delay. Our coding design method and scheduling algorithm are validated through experiments. Simulation results show improved transmission efficiency and reduced network delay.

Rate-Adaptive Concurrent Transmission Scheduling Schemes for WPANs With Directional Antennas

We consider the concurrent transmission scheduling problem in a rate-adaptive wireless personal area network (WPAN). In such networks, the physical layer can adaptively change modulation and coding schemes based on the interference level in the environment and accordingly change the data rate. The scheduling problem is to assign users to time slots so that the total throughput is maximized. The challenge is that the achieved data rate of one flow is limited by the interference from other flows in the same slot, which is unknown until the schedule is known. We propose to discretize data rate into several distinct levels supported by the PHY layer and then use a linear programming model to find the highest rate level a flow can achieve. The same model is extended to consider a mixture of omni-directional antennas and directional antennas with heterogeneous transmitting power. The simulation results show that the proposed algorithms outperform the previous work for adaptive-rate transmission scheduling in both throughput and fairness.

Simultaneous routing and multiplexing in ad hoc networks with MIMO links

This paper addresses how to leverage the spatial multiplexing function of MIMO links to improve wireless network throughput. Wireless interference modeling of a half-duplex MIMO node is presented, based on which, routing, spatial multiplexing and scheduling are jointly considered in one optimization model. A linear program-based algorithm is proposed for the joint optimization, and numerical simulation results show that the joint optimization of routing with spatial-temporal multiplexing is superior to the separate design approaches, including separating routing from the other two designs, and separating scheduling from the other two designs.

Maximizing coding gain in wireless networks with decodable network coding

Network coding improves transmission efficiency by combining packets at relay nodes and thus reduces the number of packets sent to the network. It is a network layer solution to improve network throughput and transmission efficiency. However, a coded packet must be decodable by the destination, otherwise it is a waste of resource to combine them together and to deliver the coded packet. This paper addresses how to find the coding solution that guarantees decodability at the destination. We first quantify the coding gain as the number of transmissions reduced, and then provide a method for runtime check whether a coding pair can be separated at the destination. The optimal coding solution is selected as the one that provides the maximum coding gain among all the decodable pairs. The algorithms can be applied to both unicast and multicast traffic. Simulation results show the number of transmissions can be reduced significantly, especially for multicast traffic where there are rich opportunities to apply network coding.

Concurrent transmission scheduling for WPANs with adaptive data rate

We consider the maximum throughput scheduling problem in a millimeter-wave wireless personal area network in which users can use adaptive modulation and coding schemes to change their data rates. The scheduling problem is to map transmissions to time slots so that the total throughput is maximized. Due to the ultra-wide bandwidth of the mm Wave band, bad scheduling tends to waste significant channel resource. It is worth the effort to consider a more sophisticated scheduling scheme than the simple serial TDMA scheme. The challenge is that the achieved data rate of one flow is limited by the interference from other transmissions in the same slot, which is unknown until the scheduling decision is known. We propose two scheduling algorithms for variable data rate transmissions. The first algorithm is a greedy algorithm, which always chooses the best option at the moment; the second one uses sorting to decide the order that flows are included in a slot. Both algorithms are interference-aware. The algorithms can be applied to transmissions with omnidirectional antennas as well as directional antennas. The simulation results show that the proposed algorithms achieve higher throughput than previous work for adaptive-rate scheduling.

Characterization and visualization of sophisticated scanning attacks

Detection of sophisticated stealthy network scans requires analyzing large amounts of network data collected over long periods of time. The sheer volume of the data prohibits efficient detection from a pure algorithmic approach. However timely detection of such sophisticated scanning attacks is critical since the attacker employing these approaches is usually well-resourced and potentially can bring high impact to the network than a naive attacker can. To detect such sophisticated scans we propose the integration of algorithmic detection and visualization for human detection to simultaneously optimize computational complexity and human analyst time. The proposed approach provides real world detection capabilities without excessive computation overhead. We characterize the features of scanning attacks in a graph theory context, propose efficient graph algorithms to extract these features in real time, employ visualization techniques to show the relevant multidimensional characteristics, and provide test scenarios to show that the proposed work is more efficient and effective than previous approaches.

Visualizing graph features for fast port scan detection

Detection of sophisticated network scans, such as low and slow scans, requires correlation of large amounts of network data over long periods of time. The volume of data obfuscating such scans can be overwhelming and makes computation challenging. Such scans pose network security risks since identifying running services, the goal of executing such scans, is the first step in launching an attack on the scanned host. To detect sophisticated scans we propose the integration of graph feature extraction techniques with visualization to simultaneously optimize computational complexity and human analyst time. The integrated approach uses graph modeling and preprocessing to make visual displays easy to comprehend, and uses human intervention to avoid solving NP-hard computational problems while still providing real-time visualization.