Genetic Programming for Proactive Aggregation Protocols

Weise, Thomas; Geihs, Kurt; Baer, Philipp A.

doi:10.1007/978-3-540-71618-1_19

Cited by 14 publications

(11 citation statements)

References 7 publications

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“…The goal of our work is to prove the utility of genetic programming as a tool for developing distributed algorithms. Recently we have contributed two successful applications [26,28] in that area.…”

Section: Discussionmentioning

confidence: 99%

Genetic Programming meets Model-Driven Development

Weise

Zapf

Khan

et al. 2007

7th International Conference on Hybrid Intelligent Systems (HIS 2007)

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Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimize specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the utility of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach-the generation of source code in different programming languages.

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Section: Discussionmentioning

confidence: 99%

Genetic Programming meets Model-Driven Development

Weise

Zapf

Khan

et al. 2007

7th International Conference on Hybrid Intelligent Systems (HIS 2007)

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“…A different approach to data aggregation was proposed by Weise et al [25], [26], who attempted to synthesize efficient distributed aggregation formulas via Genetic Programming, which should lead to fast convergence and hence fewer messages. This approach did not consider energy efficiently directly but hoped to improve it indirectly.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

Keegan

Mtenzi

et al. 2019

IEEE Access

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Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO.

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“…In the past we have introduced new applications of Genetic Programming in the area of distributed computing. Among these are automated aggregation protocol synthesis [43] and the breeding of election algorithms [42,41]. We now test our new approach on a hard problem in the same field, the distributed critical section.…”

Section: The Distributed Critical Sec-tionmentioning

confidence: 99%

“…In the past, we have applied Genetic Programming to different problems in distributed computing [43,41]. It is only natural to test the utility of the new rule-based Genetic Programming in the same domain.…”

Section: Introductionmentioning

confidence: 99%

Rule-based Genetic Programming

Weise¹,

Zapf²,

Geihs³

2007

Proceedings of the 2nd International Conference on Bio-Inspired Models of Network Information and Computing Systems

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In this paper we introduce a new approach for Genetic Programming, called rule-based Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent from each other in terms of position (mostly) and cardinality (always). This reduces the epistasis drastically and hence, the genetic reproduction operations are much more likely to produce good results than in other Genetic Programming methodologies. In order to verify the utility of our idea, we apply RBGP to a hard problem in distributed systems. With it, we are able to obtain emergent algorithms for mutual exclusion at a distributed critical section.

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Genetic Programming for Proactive Aggregation Protocols

Cited by 14 publications

References 7 publications

Genetic Programming meets Model-Driven Development

Genetic Programming meets Model-Driven Development

Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

Rule-based Genetic Programming

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