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Proceedings ArticleDOI

Efficient Implementation of localization in Wireless Sensor Networks Using Optimization Techniques

TL;DR: In wireless sensor network (WSN), Invasive Weed Optimization Algorithm (IWO) is proposed which reaches an optimal solution more easily by giving a chance for inappropriate one's to survive and reproduce similar to the mechanism that happens in nature.
Abstract: In wireless sensor network (WSN) there are many sensors and tiny devices which are used to sense the real-time environmental circumstances. The sensed data will be meaningless if each node in WSN doesn't know its location in the real world. There are many cost-effective techniques for localization used to locate the sensor node. Among those techniques, the range-based localization techniques are known for their accuracy in predicting sensor node location. Differential Evolution Algorithm (DEA) is popular optimization technique as it has good convergence properties but it has few control parameters, which are fixed throughout the entire iteration process and it is not an easy task to tune that control parameters. So, in this paper, we propose Adaptive Differential Evolution Algorithm (ADEA) for obtaining adaptive control over the parameters. In DEA we consider appropriate solutions to have more probability of reproduction compared to inappropriate ones, but there is always a possibility that population elements that look inappropriate in each stage may contain more useful information than appropriate ones. So, we propose Invasive Weed Optimization Algorithm (IWO) which reaches an optimal solution more easily by giving a chance for inappropriate one's to survive and reproduce similar to the mechanism that happens in nature.
Citations
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Journal ArticleDOI
16 Jul 2019
TL;DR: It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied and will continue to be utilized in this context.
Abstract: Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.

9 citations


Cites methods from "Efficient Implementation of localiz..."

  • ...Moreover, localisation by using DE can be improved by adaptive controls over the parameters to ensure adequate tuning [163]....

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References
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Journal ArticleDOI
Rainer Storn1, Kenneth Price
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

24,053 citations

Proceedings ArticleDOI
09 Jul 2003
TL;DR: This paper proposes a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability.
Abstract: Position information of individual nodes is useful in implementing functions such as routing and querying in ad-hoc networks. Deriving position information by using the capability of the nodes to measure time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and signal strength have been used to localize nodes relative to a frame of reference. The nodes in an ad-hoc network can have multiple capabilities and exploiting one or more of the capabilities can improve the quality of positioning. In this paper, we show how AOA capability of the nodes can be used to derive position information. We propose a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability.

2,285 citations


"Efficient Implementation of localiz..." refers methods in this paper

  • ...Among range-based localization techniques, Angle of Arrival (AOA) [4], Time Difference of Arrival (TDoA) [5], Received Signal Strength (RSS) [6], Time of Arrival (TOA) [7] are famous techniques....

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Journal ArticleDOI
TL;DR: An overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements are provided and a detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented.

1,870 citations


"Efficient Implementation of localiz..." refers background in this paper

  • ...WSN has many applications like precision agriculture, disaster management, and forest fire detection [1]....

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Journal ArticleDOI
TL;DR: A novel numerical stochastic optimization algorithm inspired from colonizing weeds to mimic robustness, adaptation and randomness of Colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO).

1,183 citations

Journal ArticleDOI
TL;DR: An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented and a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for W SNs.
Abstract: Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

683 citations


"Efficient Implementation of localiz..." refers background in this paper

  • ...The evolutionary computing makes algorithm inteligent and provides the capability of decision making [9]....

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