The tradeoffs between data delivery ratio and energy costs in wireless sensor networks

Bucur, Doina; Iacca, Giovanni; Squillero, Giovanni; Tonda, Alberto

doi:10.1145/2576768.2598384

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…MOEAs have been used with great success in a number of real-world applications, as surveyed for instance in [4,6,8]. In the SNs domain, so far MOEAs have been used mostly for community detection [16,18,23] and network clustering [14].…”

Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

Bucur

Iacca²,

Marcelli

et al. 2017

Lecture Notes in Computer Science

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As the pervasiveness of social networks increases, new NPhard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging from social studies to marketing. The influence maximization problem is typically formulated assuming that the number of the seed nodes is a parameter. Differently, in this paper, we choose to formulate it in a multi-objective fashion, considering the minimization of the number of seed nodes among the goals, and we tackle it with an evolutionary approach. As a result, we are able to identify sets of seed nodes of different size that spread influence the best, providing factual data to trade-off costs with quality of the result. The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms. The results show that the proposed approach is almost always able to outperform the heuristics.

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Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

Bucur

Iacca²,

Marcelli

et al. 2017

Lecture Notes in Computer Science

Self Cite

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“…Furthermore, the gathered data enabled us to pinpoint a set of topological factors which correlate with extreme traffic under collection routing. In [19], we further enhanced such analysis through the use of a multi-objective evolutionary algorithm, in the attempt to explore the WSN search space from a multi-objective perspective rather than using lexicographic order of fitness functions.…”

Section: Bio-inspired Heuristicsmentioning

confidence: 99%

Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

Bucur

Iacca²,

Gaudesi³

et al. 2016

Applied Soft Computing

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In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users' mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration.

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“…However, an implicit bias can be introduced into the decision-making process, which might be harmful, especially if the objectives are conflicting. Moreover, such approaches are not usually able to provide a full set of trade-off solutions, but rather they tend to offer a few solutions (perhaps only a single solution), which are often not homogeneously distributed in the objective space [78].…”

Section: Multiple Criteria Decision Making Schemementioning

confidence: 99%

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

et al. 2015

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Along with the development of the technologies of various kinds, wireless sensor networks are gradually entering into the practicalization phase. Therefore, whether its performance is good or not will directly affect the use of WSNs. Routing is one of the most important technologies. In addition, the routing performance is a significant part of the evaluation index system of wireless sensor networks. Energy efficiency is focused on in the traditional routing protocols, while quality of service (QoS) (i.e., delay, reliability, robustness) becomes important in practical application. Routing protocols should not only ensure the energy efficiency, but also realize QoS performances. As a consequence how to achieve the equilibrium between energy efficiency and other performances is the issue to be solved. In this paper, some performance trade-off mechanisms and methods that have existed in the routing protocols of wireless sensor networks are analyzed and summarized. At the same time, the related technical features of the routing protocols are mentioned, the purpose of which is to provide a better understanding of the current research issues within the field of performance trade-off mechanisms and to guide the researcher to develop the routing protocols with better performance in order to fulfill the actual requirements.

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The tradeoffs between data delivery ratio and energy costs in wireless sensor networks

Cited by 8 publications

References 30 publications

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

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