Firefly algorithm with neighborhood attraction

Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

/pdf/cuckoo-search-and-firefly-algorithm-hfldltlkep.pdf

Cuckoo Search and Firefly Algorithm

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

https://www.mdpi.com/1424-8220/16/3/414/pdf

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.

Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Due to intrinsic properties of aqueous environments, routing protocols for underwater wireless sensor network (UWSN) have to cope with many challenges such as long propagation delay, bad robustness, and high energy consumption. Basic ant colony optimization algorithm (ACOA) is an intelligent heuristic algorithm which has good robustness, distributed computing and combines with other algorithms easily. But its disadvantage is that it may converge at local solution, not global solution. Artificial fish swarm algorithm (AFSA) is one kind of intelligent algorithm that can converge at global solution set quickly but has lower precision in finding global solution. Therefore we can make use of AFSA and ACOA based on idea of complementary advantages. So ACOA-AFSA fusion routing algorithm is proposed which possesses advantages of AFSA and ACOA. As fusion algorithm has aforementioned virtues, it can reduce existing routing protocols’ transmission delay, energy consumption and improve routing protocols’ robustness theoretically. Finally we verify the feasibility and effectiveness of fusion algorithm through a series of simulations.

https://journals.sagepub.com/doi/pdf/10.1155/2012/920505

An ACOA-AFSA Fusion Routing Algorithm for Underwater Wireless Sensor Network:

The unique characteristics of high bit error rate, low bandwidth, and long propagation delay in underwater environments pose significant challenges to the design of the medium access control (MAC) protocol for underwater wireless sensor networks (UWSNs). Clustering is an effective and practical way to enhance the performance of UWSNs. In this paper, we propose a secure MAC protocol for cluster-based UWSNs, called SC-MAC, which aims to ensure the security of data transmission. In SC-MAC, the clusters are formed and updated dynamically and securely. We leverage MAC layer information by considering the link quality as well as residual energy of the modem's battery. After the successful mutual authentication, all sensor nodes from different clusters can protect the data transmission in the continuous communication. As the states of sensor nodes may not be observed accurately in a harsh underwater environment, we formulate the channel scheduling process as a stochastic partially observed Markov decision process (POMDP) multiarmed bandit problem and derive the optimal channel scheduling rules hereby. Simulation results show that SC-MAC can perform better than existing state-of-the-art MAC protocols in terms of network throughput, successful delivery ratio, and energy consumption in various circumstances.

/pdf/towards-a-secure-medium-access-control-protocol-for-cluster-181aisw11a.pdf

Towards a secure medium access control protocol for cluster-based underwater wireless sensor networks

Maritime search and rescue (MSR), as the last defense line for the life at sea, plays a great role in shipping industry and ocean environment protection. MSR is attracting more and more attention from technical and scientific aspects which are expected to help locate the victim goals quickly, precisely, and efficiently. So far, there have been some new techniques applied to MSR, such as computer vision. However, these techniques have a common drawback that they are too dependent on the effort of the search and rescue party, so that the victim goals can only passively wait for their search. To change this kind of passive status, wireless sensor network (WSN) is the deficiencies of the current maritime search and rescue. To make WSN localization suitable for the MSR, an improved microelectromechanical systems MEMS aided algorithm on the basis of triangle and centroid algorithm is proposed to locate and track the search targets in real time and more precisely. Finally, the simulation results show that the proposed method and algorithm are feasible and efficient.

https://journals.sagepub.com/doi/pdf/10.1155/2013/791981

Real-Time Localization Algorithm for Maritime Search and Rescue Wireless Sensor Network

Low data delivery efficiency and high energy consumption are the inherent problems in Underwater Wireless Sensor Networks (UWSNs) characterized by the acoustic channels. Existing energy-efficient routing algorithms have been shown to reduce energy consumption of UWSNs to some extent, but still neglect the correlation existing in the local data of sensor nodes. In this paper, we present a Multi-population Firefly Algorithm (MFA) for correlated data routing in UWSNs. We design three kinds of fireflies and their coordination rules in order to improve the adaptability of building, selecting, and optimization of routing path considering the data correlation and their sampling rate in various sensor nodes. Different groups of fireflies conduct their optimization in the evolution in order to improve the convergence speed and solution precision of the algorithm. Moreover, after the data packets are merged during the process of routing path finding, MFA can also eliminate redundant information before they are sent to the sink node, which in turn saves energy and bandwidth. Simulation results have shown that MFA achieves better performance than existing protocols in metrics of packet delivery ratio, energy consumption, and network throughput.

https://journals.sagepub.com/doi/pdf/10.1155/2013/865154

A multipopulation firefly algorithm for correlated data routing in underwater wireless sensor networks

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Papers

An ACOA-AFSA Fusion Routing Algorithm for Underwater Wireless Sensor Network:

Towards a secure medium access control protocol for cluster-based underwater wireless sensor networks

Real-Time Localization Algorithm for Maritime Search and Rescue Wireless Sensor Network

A multipopulation firefly algorithm for correlated data routing in underwater wireless sensor networks