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

In this paper, we study a general mapping problem where a set of independent tasks compete for the shared resources of a Grid environment. Tasks have resource co-allocationrequirements. Each task requires multiple and different resources to be allocated simultaneously. At run-time, a task may release its allocated resources during its execution and before its completion time. Our objective is to minimize the overall schedule length of all submitted tasks while satisfying all resource sharing constraints among them. We develop a two-phase mapping approach for solving this problem. The first phase of our approach is off-line planning phase where a schedule plan, which gives a scheduling order and resource assignments of tasks, is generated at compile-time. The second phase is run-time adaptationphase. The goal of the second phase is to improve the performance of the schedule plan by adapting to run-time changes such as the early release of resources and the variationin computation and communication costs. Adaptation may involve changing the scheduling order and resource assignments of the original schedule plan. Our experimental results demonstrate the effectiveness of our approach compared to a baseline algorithm that performs no adaptation at run-time and to a dynamic algorithm that performs no planning at compile-time. Our two-phase mapping approach outperforms both algorithms by up to 20% with respect to the overall schedule length.

Run-time adaptation for grid environments

Optical Matrix-Vector Implementation of Crossbar Interconnection Networks.

We consider the problem of embedding and reconfiguring binary tree structures in faulty hypercubes. We assume that the number of faulty nodes is at most (n-2), where n is the number of dimensions of the hypercube; we further assume that the location of faulty nodes are known. Our embedding techniques are based on a key concept called free dimension, which can be used to partition a cube into subcubes such that each subcube contains at most one faulty node. Using this approach, two distributed schemes are provided for embedding and reconfiguration in faulty hypercubes. We extend the free dimension concept to degree of occupancy and use this to develop a distributed scheme for reconfiguration of binary tree in faulty hypercubes with up to [3n/2] node faults.

Embedding and reconfiguration of binary trees in faulty hypercubes

The authors present a distributed scheme for reconfiguration of embedded binary trees in hypercubes. Their scheme can reconfigure around any 3n/2 faulty nodes in O(n) time, in an n-dimensional hypercube. Their technique, which is based on a key concept called degree of occupancy, can be generalized to any task graph. >

Reconfiguration of binary trees in faulty hypercubes

Embedded sensors of a Body Sensor Network need to efficiently utilize their energy resources to operate for an extended amount of time. A Markov Decision Process (MDP) framework has been used to obtain a globally optimal policy that coordinated the sampling of multiple sensors to achieve high efficiency in such sensor networks. However, storing the coordinated sampling policy table requires a large amount of memory which may not be available at the embedded sensors. Computing a compact representation of the MDP global policy will be useful for such sensor nodes. In this paper we show that a decision tree-based learning of a compact representation is feasible with little loss in performance. The global optimal policy is computed offline using the MDP framework and this is then used as training data in a decision tree learner. Our simulation results show that both unpruned and high confidence-pruned decision trees provide an error rate of less than 1% while significantly reducing the memory requirements. Ensembles of lower-confidence trees are capable of perfect representation with only small increase in classifier size compared to individual pruned trees.

Cauligi S. Raghavendra

Papers

Run-time adaptation for grid environments

Optical Matrix-Vector Implementation of Crossbar Interconnection Networks.

Embedding and reconfiguration of binary trees in faulty hypercubes

Reconfiguration of binary trees in faulty hypercubes

Compact representation of coordinated sampling policies for Body Sensor Networks