http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

/pdf/energy-efficient-communication-protocol-for-wireless-4vhuagusz2.pdf

Energy-efficient communication protocol for wireless microsensor networks

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Energy-efficient communication protocols for wireless microsensor networks

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

/pdf/an-application-specific-protocol-architecture-for-wireless-1s7y9fiv40.pdf

An application-specific protocol architecture for wireless microsensor networks

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of such highly specialized features requires a considerable amount of time, manual oversight and careful quality control to ensure the absence of errors in the computational process. Recent advances in Deep Representation Learning have shown great promise in extracting highly non-linear and information-rich features from data. In this paper, we present a novel large-scale deep unsupervised approach to learn generic feature representations of structural brain MRI scans, which requires no specialized domain knowledge or manual intervention. Our method produces low-dimensional representations of brain structure, which can be used to reconstruct brain images with very low error and exhibit performance comparable to FreeSurfer features on various classification tasks.

Large-Scale Unsupervised Deep Representation Learning for Brain Structure.

Long flows contribute huge volumes of traffic over inter-datacenter WAN. The flow completion time (FCT) is a vital network performance metric that affects the running time of distributed applications and the users’ quality of experience. Flow routing techniques based on propagation or queuing latency or instantaneous link utilization are insufficient for minimization of the long flows’ FCT. We propose a routing approach that uses the remaining sizes and paths of all ongoing flows to minimize the worst case completion time of incoming flows assuming no knowledge of future flow arrivals. Our approach can be formulated as an NP-Hard graph optimization problem. We propose BWRH , a heuristic to quickly generate an approximate solution. We evaluate BWRH against several real-WAN topologies and two different traffic patterns. We see that BWRH provides solutions with an average optimality gap of less than 0.25%. Furthermore, we show that compared with other popular routing heuristics, BWRH reduces the mean and tail FCT by up to $1.46\times $ and $1.53\times $ , respectively.

/pdf/on-minimizing-the-completion-times-of-long-flows-over-inter-3y6a26jmqm.pdf

On Minimizing the Completion Times of Long Flows Over Inter-Datacenter WAN

A novel coding scheme is proposed for applications over wireless microsensor networks to meet both wireless link bandwidth and node energy constraints First, an analysis is performed on the energy efficiency of coding schemes in wireless microsensor networks Based on this analysis, we devise a power aware coding scheme, called EESPIHT which exploits the spatio-temporal correlation of multiple sensor readings It is also made resilient to channel errors: it selects an error correcting code based on source coding information and transmission power so that energy dissipation is minimized Experimental results on a LINUX implementation and simulation results based on OPNET using field data sets show that the proposed scheme can reduce energy on communication by more than 60% and maintain a signal-to-noise ratio (SNR) gain of 24 dB or better compared to the non-coded case

/pdf/power-aware-coding-for-spatio-temporally-correlated-wireless-adz1qusbdj.pdf

Power aware coding for spatio-temporally correlated wireless sensor data

The load balancing and routing problems are combined as a single problem to capture the interaction between them. An optimization problem for joint load balancing and routing is formulated using a linear combination of the average job response time and average message delay as the performance criterion. Given an initial feasible solution, the Frank-Wolfe method is applied to solve the problem. An algorithm which gives an optimal solution is obtained. The algorithm is then extended to solve the same problem with multiple types of jobs. >

Optimal joint load balancing and routing in message switched computer networks

Ray tracing is a well known technique to generate life-like images. Unfortunately, ray tracing complex scenes can require large amounts of CPU time and memory storage. Distributed memory parallel computers with large memory capacities and high processing speeds are ideal candidates to perform ray tracing. However, the computational cost of rendering pixels and patterns of data access cannot be predicted until runtime. To parallelize such an application efficiently on distributed memory parallel computers, the issues of database distribution, dynamic data management and dynamic load balancing must be addressed. In this paper, we present a parallel implementation of a ray tracing algorithm on the Intel Delta parallel computer. In our database distribution, a small fraction of database is duplicated on each processor, while the remaining part is evenly distributed among groups of processors. In the system, there are multiple copies of the entire database in the memory of groups of processors. Dynamic data management is acheived by an ALRU cache scheme which can exploit image coherence to reduce data movements in ray tracing consecutive pixels. We balance load among processors by distributing subimages to processors in a global fashion based on previous workload requests. The success of our implementation depends crucially on a number of parameters which are experimentally evaluated. 1997 by John Wiley & Sons, Ltd.

Cauligi S. Raghavendra

Papers

Large-Scale Unsupervised Deep Representation Learning for Brain Structure.

On Minimizing the Completion Times of Long Flows Over Inter-Datacenter WAN

Power aware coding for spatio-temporally correlated wireless sensor data

Optimal joint load balancing and routing in message switched computer networks

Parallel implementation of a ray tracing algorithm for distributed memory parallel computers