Energy-constrained paths for optimization of energy consumption in wireless sensor networks

© 2014 IEEE. A sensor spends a large part of its energy in transmitting its data and relay its neighbours' data. The overall lifetime of a wireless sensor network depends strongly on how a sensor selects its relaying neighbours and the data path to the destination. One critical problem is that if a sensor has to support too many neighbours, its energy is exhausted rapidly and may bring down the whole network. This paper suggests algorithms for assigning weights to links between neighbours taking into account the number of neighbours who rely on them to relay traffic to the destination. In order to do so, the paper also proposes an algorithm for constructing node connectivity based on sensors position within the broadcast range of another sensor, and a shortest energy-constrained path from a sensor to the destination

A hierarchy energy driven architecture for wireless sensor networks

In Wireless Sensor Networks (WSNs) one of the critical issues is the maximization of their life time. These networks require a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. With such an architecture, the overall energy consumption can then be optimized with respect to the constraints of an application. Unlike most current researches that focus on a single aspect of WSNs, this paper presents a Hierarchy Energy Driven Architecture (HEDA) as a new architecture and a novel approach for minimising the total energy consumption of WSNs. The Energy Driven Architecture identifies generic and essential energy-consuming constituents of the network. HEDA as a constituent-based architecture is used to deploy WSNs according to energy dissipation through their constituents. This view of overall energy consumption in WSNs can be applied to optimizing and balancing energy consumption and increasing the network lifetime. © 2010 IEEE

A task based sensor-centric model for overall energy consumption

Sensors have limited resources so it is important to manage the resources efficiently to maximize their use. A sensor's battery is a crucial resource as it singly determines the lifetime of sensor network applications. Since these devices are useful only when they are able to communicate with the world, radio transceiver of a sensor as an I/O and a costly unit plays a key role in its lifetime. This resource often consumes a big portion of the sensor's energy as it must be active most of the time to announce the existence of the sensor in the network. As such the radio component has to deal with its embedded sensor network whose parameters and operations have significant effects on the sensor's lifetime. In existing energy models, hardware is considered, but the environment and the network's parameters did not receive adequate attention. Energy consumption components of traditional network architecture are often considered individually and separately, and their influences on each other have not been considered in these approaches. In this paper we consider all possible tasks of a sensor in its embedded network and propose an energy management model. We categorize these tasks in five energy consuming constituents. The sensor's Energy Consumption (EC) is modeled on its energy consuming constituents and their input parameters and tasks. The sensor's EC can thus be reduced by managing and executing efficiently the tasks of its constituents. The proposed approach can be effective for power management, and it also can be used to guide the design of energy efficient wireless sensor networks through network parameterization and optimization. © 2011 IEEE

Modeling overall energy consumption in Wireless Sensor Networks

Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. Optimizing a specific component of the total energy cost does not help in reducing the total energy cost as this reduction may be negated by an increase in the energy consumption of other components of the application. Recently we proposed Hierarchy Energy Driven Architecture as a robust architecture that takes into account all principal energy constituents of wireless sensor network applications. Based on the proposed architecture, this paper presents a single overall model and proposes a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of a wireless sensor network application, optimizing its constituent's operations, and designing more energy-efficient applications. The paper also presents simulation results to demonstrate the feasibility of our model and energy formulatio

MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data

The oil and gas industries have been great consumers of parallel and distributed computing systems, by frequently running technical applications with intensive processing of terabytes of data. By the emergence of cloud computing which gives the opportunity to hire high-throughput computing resources with lower operational costs, such industries have started to adopt their technical applications to be executed on such high-performance commodity systems. In this paper, we first give an overview of forward/inverse Prestack Kirchhoff Time Migration (PKTM) algorithm, as one of the well-known seismic imaging algorithms. Then we will explain our proposed approach to fit this algorithm for running on Google's MapReduce framework. Toward the end, we will analyse the relation between MapReduce-based PKTM completion time and the number of mappers/reducers on pseudo-distributed MapReduce mode