Conference Paper

Energy-efficient communication for ad-hoc wireless sensor networks

Massachusetts Inst. of Technol., Cambridge, MA, USA
DOI: 10.1109/ACSSC.2001.986894 Conference: Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on, Volume: 1
Source: IEEE Xplore

ABSTRACT The energy dissipated by communication is a key concern in the development of networks of hundreds to thousands of distributed wireless microsensors. To evaluate the dissipation of communication energy in this unique application domain, energy models based on actual microsensor hardware are incorporated into a simulation tool designed expressly for high-density, energy-conscious wireless networks. Assessing and leveraging the energy implications of microsensor hardware and applications is crucial to achieving energy-efficient microsensor network communication.

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    • "Reports transmitted by these sensors are collected by observers (e.g., base stations).The dense deployment and unattended nature of WSNs makes it quite difficult to recharge node batteries [2], [4]. "
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    ABSTRACT: The wireless sensor networks combines sensing, computation, and communication into a single small device. These devices depend on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus improving the energy of these networks becomes important. Clustering in wireless sensor network looks several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we study a protocol supporting an energy efficient clustering, cluster head selection and data routing method to extend the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection and data routing. The results of simulation show that at the end of some certain part of running the EECS and Fuzzy based clustering algorithm increases the number of alive nodes comparing with the LEACH and HEED methods and this can lead to an increase in sensor network lifetime. By using the EECS method the total number of messages received at base station is increased when compared with LEACH and HEED methods. The Fuzzy based clustering method compared with the K-Means Clustering by means of iteration count and time taken to die first node in wireless sensor network, as the result shows that the fuzzy based clustering method perform well than kmeans clustering methods.
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    • "Genetic Algorithm (GA) is one of the most powerful heuristics for solving optimization problems that is based on natural selection, the process that drives biological evolution. Several researchers have successfully implemented GAs in a sensor network design [10]-[17], this led to the development of several other GAbased application-specific approaches in WSN design, mostly by the construction of a single fitness function. However, these approaches either cover limited network characteristics or fail to incorporate several application specific requirements into the performance measure of the heuristic. "
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    ABSTRACT: Node placement is an important task in wireless sensor network. Node placement in wireless sensor network is a multi-objective combinatorial problem. A multi-objective evolutionary algorithm based framework has been proposed in this paper. Design parameters such as network density, connectivity and energy consumption have been taken into account for developing the framework. The framework optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths.
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    • "Examining various functionalities of sensor networks, communication can be singled out as one function that devours big share of the energy resources. A review of the literature suggests that many attempts have been made to minimize energy consumption during communication [1] [2] [3] [4] [5] [6] [7]. The process of routing data from all the nodes to one node (called a sink node) is called a convergecast. "
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    ABSTRACT: Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast – process of routing data from many sources to a sink – is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected.The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (= 1 − γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms’ performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.
    Ad Hoc Networks 07/2007; 5(5-5):626-648. DOI:10.1016/j.adhoc.2006.04.004 · 1.53 Impact Factor
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