Alei Liang

An Area Localization Scheme for Large Scale Underwater Wireless Sensor Networks

In this paper, we study an area localization problem in large scale Underwater Wireless Sensor Networks (UWSNs). The limited bandwidth, the severely impaired channel and the cost of underwater equipment all makes the underwater localization problem very challenging. Exact localization is very difficult for UWSNs in deep underwater environment. We propose a Mobile DETs based efficient 3D multi-power Area Localization Scheme (3D-MALS) to address the challenging problem. In the proposed scheme, the ideas of 2D multi-power Area Localization Scheme(2D-ALS) [6] and utilizing Detachable Elevator Transceiver (DET) are used to achieve the simplicity, location accuracy, scalability and low cost performances. The DET can rise and down to broadcast its position. And it is assumed that all the underwater nodes underwater have pressure sensors and know their z coordinates. The simulation results show that our proposed scheme is very efficient.

Frontier-based multi-robot map exploration using Particle Swarm Optimization

Exploring an unknown environment using team of autonomous mobile robots is an important task in many real-world applications. Many existing map exploration algorithms are based on frontier, which is the boundary between unexplored space and known open space. In the context of multiple robots, the main problem of frontier-based algorithm is to choose appropriate target points for the individual robots so that they can efficiently explore the different part of the common area. This paper proposed a novel distributed frontier-based map exploration algorithm using Particle Swarm Optimization model for robot coordination. In this algorithm, the robot keeps moving to the nearby frontier to reduce the size of the unknown region, and is navigated towards frontier far away based on the PSO model after exploring the local area. The exploration is completed when there are no frontier cells on the map. Our algorithm has been implemented and tested both in simulation runs and real world experiment. The result shows that our method has a good scalability and efficiency.

A Hierarchical Localization Scheme for Large Scale Underwater Wireless Sensor Networks

In this paper, we study the localization problem in large-scale Underwater Wireless Sensor Networks (UWSNs). Unlike in the terrestrial positioning, the global positioning system(GPS) can not work efficiently underwater. The limited bandwidth,the severely impaired channel and the cost of underwater equipment all makes the localization problem very challenging. Most current localization schemes are not well suitable for deep underwater environment. We propose a hierarchical localization scheme to address the challenging problems. The new scheme mainly consists of four types of nodes, which are surface buoys, Detachable Elevator Transceivers (DETs), anchor nodes and ordinary nodes. Surface buoy is assumed to be equipped with GPS on the water surface. A DET is attached to a surface buoy and can rise and down to broadcast its position. The anchor nodes can compute their positions based on the position information from the DETs and the measurements of distance to the DETs. The hierarchical localization scheme is scalable, and can be used to make balances on the cost and localization accuracy. Initial simulation results show the advantages of our proposed scheme.

A Distributed Bidirectional Auction Algorithm for Multirobot Coordination

Multi-robot coordination is fundamental and significant in the robotics research community. With the increasing number of robots in one task, team efficiency improvement and energy consumption reduction research becomes more important in robots coordination. In this paper, we propose a novel multirobot coordination algorithm called Distributed Bidirectional Auction algorithm, in which robots bid for task and the task assignment is decided by both the auctioneer and bidder so that the task is completed through a more efficient coordination. We verify our algorithm through simulator on a large swarm of mobile robots in pattern formation scenario, which showed the approach improved team efficiency with robots increased. By comparison with traditional distributed auction algorithm and random allocation algorithm, it also performs better in energy consumption reduction.

CoDBT: A multi-source dynamic binary translator using hardware-software collaborative techniques

For implementing a dynamic binary translation system, traditional software-based solutions suffer from significant runtime overhead and are not suitable for extra complex optimization. This paper proposes using hardware-software collaboration techniques to create an high efficient dynamic binary translation system, CoDBT, which emulates several heterogeneous ISAs (Instruction Set Architectures) on a host processor without changing to the existing processor. We analyze the major performance bottlenecks via evaluating overhead of a pure software-solution DBT. Guidelines are provided for applying a suitable hardware-software partition process to CoDBT, as are algorithms for designing hardware-based binary translator and code cache management. An intermediate instruction set is introduced to make multi-source translation more practicable and scalable. Meantime, a novel runtime profiling strategy is integrated into the infrastructure to collect program hot spots information to supporting potential future optimizations. The advantages of using co-design as an implementation approach for DBT system are assessed by several SPEC benchmarks. Our results demonstrate that significant performance improvements can be achieved with appropriate hardware support choices. CoDBT could be an efficient and cost-effective solution for situations where the usual methods of performance acceleration for dynamic binary translation are inappropriate.

A statistical-feature-based approach to internet traffic classification using Machine Learning

This Internet traffic classification using Machine Learning is an emerging research field since 1990s, and now it is widely used in numerous network activities. The classification technique focuses on modeling attributes and features of data flows to accomplish the identification of applications. In the paper we design and implement the classification model based on header-derived flow statistical features. Compared with the traditional methods, the model designed here, which is totally insensitive to port numbers and contents of payload on application level, overcomes difficulty in operation caused by unreliable port numbers and complexity of payload interpretation. Rather than relatively complex ML algorithms or even in mixture, supervised k-Nearest Neighbor estimator is adopted for the sake of computational efficiency, along with the effective and easy-to-calculate statistical features selected according to the operational background. Our results indicate that about 90% accuracy on per-flow classification can be achieved, which is a vast improvement over traditional techniques that achieve 50–70%.

Performance Tuning Towards a KVM-Based Low Latency Virtualization System

Utilizing virtualization technology to combine real-time operating system(RTOS)and off-the-shelf time-sharing general purpose operating system (GPOS)is attracting much more interest recently.Such combination has the potential to provide a large application base,and to guarantee timely deterministic response to real-time applications,yet there is no convincible experimental result about its real-time property.In this paper,we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings,and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable interval.Furthermore,we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks,so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system.

A Map-Coverage Algorithm Basing on Particle Swarm Optimization

With the development of multi-robots technology, with multi-robots, large scale swarms are often applied to improve the efficiency and robustness of coverage process. The traditional methods basing on negotiation and task allocation often meet their bottleneck in this scenario of large scale swarm task for the dramatic increase of communication and computation. In this paper we propose a novel algorithm basing on the Particle Swarm Optimization (PSO) model. In this algorithm, a virtual pheromone based communication mechanism is adopted to decrease the communication cost and optimize the cooperation between nodes. For the nodes, there are two phases, exploration and exploitation, with different indicators in the PSO model. By switching between these two phases, the swarm completes the task of map coverage. A series of experiments on simulator is carried out and proves the convergence and excellent scalability of our algorithm. By optimizing some parameter in the PSO model with the simulator, the efficiency of map-coverage is further improved.

Service differentiation in OFDM-based IEEE 802.16 networks

IEEE 802.16 network is widely viewed as a strong candidate solution for broadband wireless access systems. Various flexible mechanisms related to QoS provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the standards. Among the mechanisms, bandwidth request scheme can be used to indicate and request bandwidth demands to the base station for different services. Due to the diverse QoS requirements of the applications, service differentiation (SD) is desirable for the bandwidth request scheme. In this paper, we propose several SD approaches. The approaches are based on the contention-based bandwidth request scheme and achieved by the means of assigning different channel access parameters and/or bandwidth allocation priorities to different services. Additionally, we propose effective analytical model to study the impacts of the SD approaches, which can be used for the configuration and optimization of the SD services. It is observed from simulations that the analytical model has high accuracy. Service can be efficiently differentiated with initial backoff window in terms of throughput and channel access delay. Moreover, the service differentiation can be improved if combined with the bandwidth allocation priority approach without adverse impacts on the overall system throughput.

A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the Particle Swarm Optimization PSO model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.