Showing papers by "Min-You Wu published in 2013"

Data loss and reconstruction in sensor networks

Abstract: Reconstructing the environment in cyber space by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the accuracy of reconstruction. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when missing data become large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of spatial correlation, temporal stability and low-rank structure. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTICS) algorithm to optimize the missing data estimation. Finally, the extensive experiments with real-world sensory data shows that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy. Typically, ESTICS can successfully reconstruct the environment with less than 20% error in face of 90% missing data.

An overview of using directional antennas in wireless networks

Abstract: SUMMARY Compared with omni-directional antennas, directional antennas have many merits, such as lower interference, better spatial reuse, longer transmission range, and improved network capacity. Directional antennas enable numerous emerging outdoor and indoor applications, which have been addressed in many recent studies. Despite the advances in wireless networks with directional antennas (DAWNs), there are many research challenges in all layers of DAWNs. This paper presents a detailed study on recent advances and open research issues on DAWNs. Firstly, we briefly introduce the classification of directional antennas, antenna radiation patterns, antenna modes, and the challenges in the physical layer of DAWNs. We then present research issues on the medium access control (MAC) layer, followed by the current solutions as well as open research problems on the MAC layer of DAWNs. In addition, we also discuss the research issues on the routing layer and the transport layer. Moreover, other research challenges on the performance evaluation of DAWNs and a brief introduction of indoor DAWNs are given in this paper as well. In conclusion, we summarize the current research issues on DAWNs as well as prospects in the future. Copyright © 2011 John Wiley & Sons, Ltd.

WiBEST: A hybrid personal indoor positioning system

Abstract: This paper introduces WiBEST, Wireless Body and Environmental Sensor Tracking platform, for personal indoor positioning. WiBEST is built with portable on-body sensor nodes and assisted sensor nodes deployed in the targeted indoor area. It takes a hybrid approach with pedestrian dead reckoning and radio-based localization and explore the their cooperative efforts. Real-time inertial measurements are combined with RSSI-based information, and then processed with an Extended Kalman Filter to be weighted in the location estimation according to their reliability. WiBEST also incorporates with an adaptive Step Length Algorithm to reduce the deviation of the measurements.The experimentation results show that WiBEST can improve the accuracy of the positioning by 66.3% compared to pure inertial solution. With the popularity of wearable devices with inertial sensors and wireless communication chips, we believe that this approach is very promising for personal indoor positioning services.

Multiple attributes-based data recovery in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), since many basic scientific works heavily rely on the complete sensory data, data recovery is an indispensable operation against the data loss. Several works have studied the missing value problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a multiple attributes-based recovery algorithm which can provide high accuracy. Firstly, based on two real datasets, the Intel Indoor project and the GreenOrbs project, we reveal that such correlations are strong, e.g., the change of temperature and light illumination usually has strong correlation. Secondly, motivated by this observation, we develop a Multi-Attribute-assistant Compressive-Sensing-based (MACS) algorithm to optimize the recovery accuracy. Finally, real trace-driven simulation is performed. The results show that MACS outperforms the existing solutions. Typically, MACS can recover all data with less than 5% error when the loss rate is less than 60%. Even when losing 85% data, all missing data can be estimated by MACS with less than 10% error.

On Busy-Tone Based MAC Protocol for Wireless Networks with Directional Antennas

Abstract: The application of directional antennas in wireless ad hoc networks offers numerous benefits, such as the extended communication range, the increased spatial reuse, the improved capacity and the suppressed interference. However, directional antennas can cause new location-dependent carrier sensing problems, such as new hidden terminal and deafness problems, which can severely degrade the network performance. Recently, a few schemes have been proposed to address these problems. However, most of these existing methods can only partially solve the hidden terminal and deafness problems. Some of them even bring significant performance overhead. In this paper, we propose a novel MAC protocol, in terms of the busy-tone based directional medium access control (BT-DMAC) protocol. In BT-DMAC, when the transmission is in progress, the sender and the receiver will turn on their omni-directional busy tones to protect the on-going transmission. Integrating with the directional network allocation vector (DNAV), the scheme can almost mitigate the hidden terminal problem and the deafness problem completely. We then propose an analytical model to investigate the throughput performance of BT-DMAC. The numerical results show that BT-DMAC outperforms other existing directional MAC schemes. We next evaluate the performance of BT-DMAC through extensive simulation experiments. The results show that our proposed BT-DMAC scheme has superior performance to other existing solutions, in terms of higher throughput.

Behavior-aware probabilistic routing for wireless body area sensor networks

Abstract: Recent advances in wireless communication and electronic manufacture have enabled a variety of sensors to be used for Wireless Body Area Networks (WBANs), which can provide real-time body monitoring and feedback for enabling patient diagnostics procedure, rehabilitation, sports training and interactive performance. However, existing single-hop wireless communication scheme faces several major challenges: rapid growth of channel conflicts as more sensors added, impermeability of human body to radio waves and highly dynamic network topology due to human movements. In this paper, a prototype of multi-hop WBAN has been built to quantify the channel conflict and to characterise the network connectivity during human motions. A probability based routing protocol fusing inertial sensor data and history link quality is then developed, which aims at capturing the high spatio-temporal change of network topology on the selection of a reliable relay node in WBAN routing. The performance of the protocol is experimentally evaluated on our prototype system. Compared with a number of existing routings, the proposed scheme is more splendid in terms of average delivery ratio, number of hops and end-to-end delay.

Poster: behavior-aware probabilistic routing for wireless body area sensor networks

Abstract: Wireless Body Area Networks (WBANs) can provide continuous and remote monitoring of patients’ physiological history, facilitate automatic sports training, and promote interactive games. However, existing single-hop wireless communication scheme faces two major challenges: the impermeability of human body to radio waves at frequencies commonly used in WBANs, and the highly dynamic network topology resulted from human movements. In this paper, (i) a prototype of multi-hop WBAN is built to characterize the network connectivity during human movements, (ii) a probabilistic routing algorithm is proposed, and (iii) the evaluation on our prototype system shows that the proposed scheme outperforms existing routings in terms of average delivery ratio, number of hops, and end-to-end delay.

An algorithm of lane change using two-lane NaSch model in traffic networks

Abstract: Few researches focus on lane change decisions achieving space or time optimality. In order to improve traffic efficiency, this paper studies the lane change problem. The goal of the lane change problem is to reduce the additional road space before all vehicles change to their target lanes. The widely adopted Nagel-Schreckenberg (NaSch) model is utilized, which reflects plenty of natural characteristics of actual traffic. An optimal lane change algorithm is proposed. Simulation shows that our approach outperforms the random lane change method. Typically, the performance is improved by 77.4% when the density of vehicles is 80% and the percentage of vehicles that requires lane change is 100%.

JSSDR: Joint-Sparse Sensory Data Recovery in wireless sensor networks

Abstract: Data loss is ubiquitous in wireless sensor networks (WSNs) mainly due to the unreliable wireless transmission, which results in incomplete sensory data sets. However, the completeness of a data set directly determines its availability and usefulness. Thus, sensory data recovery is an indispensable operation against the data loss problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a novel sensory data recovery algorithm which exploits the spatial and temporal joint-sparse feature. Firstly, by mining two real datasets, namely the Intel Indoor project and the GreenOrbs project, we find that: (1) for one attribute, sensory readings at nearby nodes exhibit inter-node correlation; (2) for two attributes, sensory readings at the same node exhibit inter-attribute correlation; (3) these inter-node and inter-attribute correlations can be modeled as the spatial and temporal joint-sparse features, respectively. Secondly, motivated by these observations, we propose two Joint-Sparse Sensory Data Recovery (JSSDR) algorithms to promote the recovery accuracy. Finally, real data-based simulations show that JSSDR outperforms existing solutions. Typically, when the loss rate is less than 65%, JSSDR can estimate missing values with less than 10% error. And when the loss rate reaches as high as 80%, the missing values can be estimated by JSSDR with less than 20% error.

Traffic Aware Routing in urban vehicular networks

Abstract: An urban vehicular network is a typical type of Delay Tolerant Network (DTN). Based on the routing analysis in a DTN, we first put forward a Minimum Delay and Hop Algorithm (MDHA), which requires both historical and future information on all the vehicles in the network. Since MDHA is not practical, we then design a Traffic Aware Routing Algorithm (TARA), which uses the historical and the real-time vehicle information to make routing decisions on the road structure level. A simulation using real GPS data in Shanghai shows that TARA significantly reduces the transmission delay and the hop count compared to the traditional GEO routing and GPSR.

Asymmetry-Aware Scheduling in Heterogeneous Multi-core Architectures

Abstract: As threads of execution in a multi-programmed computing environment have different characteristics and hardware resource requirements, heterogeneous multi-core processors can achieve higher performance as well as power efficiency than homogeneous multi-core processors. To fully tap into that potential, OS schedulers need to be heterogeneity-aware, so they can match threads to cores according to characteristics of both. We propose two heterogeneity-aware thread schedulers, PBS and LCSS. PBS makes scheduling based on applications' sensitivity on large cores, and assigns large cores to applications that can achieve better performance gains. LCSS balances the large core resource among all applications. We have implemented these two schedulers in Linux and evaluated their performance with the PARSEC benchmark on different heterogeneous architectures. Overall, PBS outperforms Linux scheduler by 13.3% on average and up to 18%. LCSS achieves a speedup of 5.3% on average and up to 6% over Linux scheduler. Besides, PBS brings good performance with both asymmetric and symmetric workloads, while LCSS is more suitable for scheduling symmetric workloads. In summary, PBS and LCSS provide repeatability of performance measurement and better performance than the Linux OS scheduler.

Channel Allocation in Wireless Networks with Directional Antennas

Abstract: In this paper, we study the channel allocation in multi-channel wireless ad hoc networks with directional antennas. In particular, we investigate the problem: given a set of wireless nodes equipped with directional antennas, how many channels are needed to ensure collision-free communications? We derive the upper bounds on the number of channels, which heavily depend on the node density and the interference ratio (i.e., the ratio of the interference range to the transmission range). We construct several scenarios to examine the tightness of the derived bounds. We also take the side-lobes and back-lobes as well as the signal path loss into our analysis. Our results can be used to estimate the number of channels required for a practical wireless network (e.g., wireless sensor network) with directional antennas.

A social-network-enabled green transportation system

Abstract: We focus on the new models of urban transportation system based on green-energy vehicles. The main goal is to design and demonstrate a prototype of social network enabled transportation system which enables communication between electrical vehicles, monitoring, information gathering, assistant driving and traffic flow control. This service-oriented system targets on significant reduction in energy consumption, pollution impact, traffic congestion, and provides solutions with affordable costs from perspective of both individual travelers and transportation agencies.

DEBUT: Delay bounded service discovery in urban Vehicular Ad-Hoc Networks

Abstract: This paper studies delay-bounded service discovery in urban Vehicular Ad-hoc Networks (VANETs), which refers to locating resources and services (e.g., local sensor data and multimedia content) distributed on individual vehicles in the network within a certain delay bound. To facilitate the discovery process, a set of vehicles, called service directories (SDs), can be selected to store the index information of all the resources in the network. Selecting an optimal SD set with minimal size while satisfying the users' requirement of a bounded query response delay is very difficult due to the disruptive nature of VANETs. In this paper, we formulate the Delay Bounded Service Directory Selection (DB-Sel) problem as an optimization problem that minimizes the number of SDs under the delay bound constraint. We prove theoretically that the DB-Sel problem is NP-Complete even when the future positions of vehicles are known a priori. We observe and prove that the number of vehicles encountered by arbitrarily selected SDs within a given delay follows a normal distribution. We also find the contact probabilities among the vehicles exhibit strong temporal correlation. With these observations, we develop a heuristic algorithm which iteratively selects the best candidate according to the normal distribution property and the historical contact probability. We prove that our algorithms have a guaranteed performance approximation ratio compared to the optimal solution. Extensive trace-driven simulation results demonstrate that our algorithm can guarantee the required query delay and select SD sets 20% smaller than those selected by alternative algorithms.