16609668
Throughput vs. Distance Tradeoffs and Deployment Considerations for a Multi-Hop IEEE 802.16e Railroad Test Bed
Throughput vs. distance analysis and coverage requirements are critical issues in planning wireless network deployments. With the recent availability of IEEE 802.16e and its promise of larger coverage areas for high-speed data communication, evaluating it in a real-world environment is very important to us in our ongoing study of standards based wireless technologies for the Federal Railroad Administration (FRA). In this paper, we present results obtained with our IEEE 802.16e module for NS-2, particularly throughput vs. distance performance, and its consideration for designing a real-world multi-hop IEEE 802.16e testbed. Firstly, a quantitative analysis of throughput vs. distance performance is conducted via extensive simulation, and the optimal modulation and coding scheme as well as channel bandwidth profiles are identified for specific distances. Secondly, we present a real-world multi-hop IEEE 802.16e testbed design intended for installation along BNSF Railway track in Nebraska. We apply our findings towards selecting various base station deployment locations and their appropriate communications parameters in order achieve best possible multi-hop throughput performance. We also provide a client access coverage analysis for the various locations.
14028781
Study of an energy efficient multi rate scheme for wireless sensor network MAC protocol
Real-time Digital Signal Processing (DSP) applications in wireless sensor network are often multi-rate in nature. These multi rate attributes provide additional significant opportunities in network design for achieving higher energy efficiency, besides traditional approaches of saving energy. In this paper, we propose a novel multi rate sensor network scheme to take advantage of power scaling for saving energy consumption. It can be easily synthesized into existing wireless sensor network MAC protocols, providing multi rate service for upper layer, and achieving communication energy efficiency. By monitoring a desirable BER threshold as the critical system parameter, the quality of received multi-rate data is assured. Based on this BER and the corresponding modulation scheme, the SNR at receiver end can be derived. The transmission power can be supplied optimally according to this SNR and the channel attenuation between sender and receiver. This dynamic power scaling scheme can lead to optimal energy efficiency since it demands that only a necessary amount of communication energy be consumed for multi-rate DSP applications. Our simulation shows that this multi data rate scheme with dynamic power scaling achieves energy saving significantly
8653895
The Implementation of an Energy Balanced Routing Protocol with Dynamic Power Scaling in TinyOS
In this paper, we study an energy balanced routing protocol based on dynamic power scaling techniques for wireless sensor networks. In the proposed protocol, each node maintains a list of neighboring nodes and their corresponding statistical residual energy information. The energy balanced routing distributes the levels of residue energy evenly throughout the network, while the dynamic power scaling scheme achieves further energy savings by adjusting transmission power to meet signal strength requirement at the receiver. In order to demonstrate the increased energy efficiency of the proposed protocol, we have conducted an experimental study. The results of our experimental study clearly show that the proposed energy balanced routing scheme with dynamic power improves energy efficiency while achieving energy balance as well
16514948
Interplay Between Routing and Distributed Source Coding in Wireless Sensor Network
Recent advances in distributed source coding (DSC) for mission-driven wireless sensor networks (WSN) are related to the coding for multiple correlated sensors in applications such as real time target tracking and environment monitoring. The characteristic of these DSC applications provides significant potential opportunities in the associated sensor network to utilize multirate transmissions for enhancing network performance. In this paper, we study a methodology for interplay optimization between routing and DSC in WSN, and propose a novel multirate based routing scheme for mission-driven DSC applications that considerably extends network lifetime. The proposed scheme adopts the rate assignment based on the residual energy, and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate constraint, information precision requirement, and the energy constraints in the network for DSC. Simulation results show that this multirate based routing scheme achieves significantly longer network lifetime compared to other existing research in WSN for DSC applications.
21523302
Image Rate Based Cross Layer Optimizations for Image Delivery in Wireless Sensor Networks
11683146
Collaborative Image Transmissions Based on Region and Path Diversity in Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor networks (WSN). The large size image transmissions cause bottlenecks in WSN due to the limited energy resources and network capacity. In this paper, we propose a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on transmission path diversities, which achieves minimal energy consumption, balanced sensor lifetime and required image quality. This optimization scheme not only allows each image sensor to transmit optimal fractions of the overlapped images through appropriate transmission paths in energy-efficient way, but also provides unequal protection on the overlap image regions through path selections and resource allocations to achieve good transmission image quality. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the network lifetime extension, image distortion reduction, and energy efficiency.
1107425
Information Coverage in Randomly Deployed Wireless Sensor Networks
Coverage is an important issue in wireless sensor networks. The most commonly used coverage model in the literature defines a point to be covered if its Euclidian distance to at least one sensor is less than a fixed threshold. This is a conservative definition of coverage which implicitly assumes that each sensor makes a decision independent of other sensors in the field. Sensors can cooperate to make an accurate estimation, even if any single sensor is unable to do so. We have previously proposed a new notion of information coverage and investigated its properties. In this paper, we study sensor density requirements for complete information coverage of a field with random sensor deployment. We provide an upper bound on the probability that an arbitrary point in a randomly deployed sensor field is not information covered and find the relationship between the sensor density and the average field vacancy. Simulation results validate our theoretical analysis and show that significant savings in terms of sensor density for complete coverage can be achieved with information coverage.
20644298
MASSIVE: a multidisciplinary applications-oriented simulation and visualization environment
The objective of the MASSIVE project is to build a grid platform to enable large-scale and distributed scientific and engineering computations. Its focus is numerical simulation and visualization. This paper addresses a prototype grid utilizing resources at the Center for Engineering and Scientific Computation (CESC) in Zhejiang University (ZJU). This grid is capable of taking a model geometry generated by CAD systems, transferring it to an SGI Onyx 3900 supercomputer where it is meshed. The meshed geometry is then transferred to a Dawning PC cluster where it is used to solve computational fluid dynamics and computational solid mechanics problems and it is finally visualized showing numerical results.
5733267
KemaDom: a web server for domain prediction using kernel machine with local context
Predicting domains of proteins is an important and challenging problem in computational biology because of its significant role in understanding the complexity of proteomes. Although many template-based prediction servers have been developed, ab initio methods should be designed and further improved to be the complementarity of the template-based methods. In this paper, we present a novel domain prediction system KemaDom by ensembling three kernel machines with the local context information among neighboring amino acids. KemaDom, an alternative ab initio predictor, can achieve high performance in predicting the number of domains in proteins. It is freely accessible at and .
34121335
Data-Aware Clustering Hierarchy for Wireless Sensor Networks
In recent years, the wireless sensor network (WSN) is employed a wide range of applications. But existing communication protocols for WSN ignore the characteristics of collected data and set routes only according to the mutual distance and residual energy of sensors. In this paper we propose a Data-Aware Clustering Hierarchy (DACH), which organizes the sensors based on both distance information and data distribution in the network Furthermore, we also present a multi-granularity query processing method based on DACH, which can estimate the query result more efficiently. Our empirical study shows that DACH has higher energy efficiency than Low-Energy Adaptive Clustering Hierarchy (LEACH), and the multi-granularity query processing method based on DACH brings more accurate results than a random access system using same cost of energy.
8941360
Genotype Sequence Segmentation: Handling Constraints and Noise
Recombination plays an important role in shaping the genetic variations present in current-day populations. We consider populations evolved from a small number of founders, where each individual's genomic sequence is composed of segments from the founders. We study the problem of segmenting the genotype sequences into the minimum number of segments attributable to the founder sequences. The minimum segmentation can be used for inferring the relationship among sequences to identify the genetic basis of traits, which is important for disease association studies. We propose two dynamic programming algorithms which can solve the minimum segmentation problem in polynomial time. Our algorithms incorporate biological constraints to greatly reduce the computation, and guarantee that only minimum segmentation solutions with comparable numbers of segments on both haplotypes of the genotype sequence are computed. Our algorithms can also work on noisy data including genotyping errors, point mutations, gene conversions, and missing values.