A Clonal Selection Algorithm for Energy-Efficient Mobile Agent Itinerary Planning in Wireless Sensor Networks

Chou, Yu-Cheng; Nakajima, Madoka

doi:10.1007/s11036-017-0814-0

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Over the last few years, mobile agent itinerary planning has drawn many researchers' attention in the field of WSNs. Many of these research efforts are towards optimizing and constructing an energy efficient itinerary planning mechanism [6,7,8,9,10,11,12,13]. One of the early work is proposed in [6] in which the authors have developed two heuristic algorithm to calculate the itinerary of the mobile agent namely local closest first (LCF) and global closest first (GCF).…”

Section: Related Workmentioning

confidence: 99%

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An energy efficient multi-mobile agent itinerary planning approach in wireless sensor networks

et al. 2021

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Mobile Agent (MA) technology in Wireless Sensor Networks (WSNs) have attracted lots of attention. The design of the itinerary planning for MA through the WSN is one of the most important features of the WSN. Numerous research has found the benefits of using MA technology in WSN to save network bandwidth as an efficient method to collect data. However, the way in which these agents are routed among sensor nodes must be intelligently planned to reduce, energy consumption, and improve information accuracy. Existing techniques rely on itinerary planning algorithms to deploy multiple MA and how sensor nodes for each MA should be grouped to define the route. These approaches determine the efficiency or effectiveness of aggregation. This paper proposes a new energy efficient mutli-mobile agent itinerary planning approach (GSMIP). In this way, it would be possible for nodes to be grouped in a Directed Acyclic Graph (DAG) and define MAs itineraries efficiently among nodes in each group. The experimental findings indicate the proposed approach's effectiveness in energy consumption and task delay (time) compared to the existing approaches.

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Section: Related Workmentioning

confidence: 99%

“…A new immune inspired algorithm, called the Clonal Selection Algorithm for Multi-agent Itinerary Planning (CSA-MIP), is proposed by the authors in [9] to solve the MIP problem in WSNs. The important components of CSA-MIP, including the encoding method, mutation operators, cloning of antibodies, and affinity function.…”

Section: Related Workmentioning

confidence: 99%

An energy efficient multi-mobile agent itinerary planning approach in wireless sensor networks

et al. 2021

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“…Many immune inspired algorithms have been proposed and proved to work in various engineering problems [20] [21] [22] [23]. Furthermore, some immune operators, such as hypermutation operation, receptor editing have been demonstrated to help algorithms to escape from a local optimum [24] [25] [26].…”

Section: Introductionmentioning

confidence: 99%

Immune Cross Entropy Algorithm for Robust Berth Allocation and Quay Crane Assignment Problem

Dai¹,

Ma²,

Yang³

et al. 2023

Preprint

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Considering different kinds of uncertainties such as late arrival of ships and extended handling time in the actual operation of container terminal, a robust berth allocation and quay crane assignment problem (BACAP) model which aims at minimizing the total stay time of all vessels is proposed. The buffer time between vessels based on weighted slack time can effectively absorb the impact of uncertainty on initial scheduling. Then an immune cross entropy algorithm (ICEA) is proposed to solve the problem. Based on the construction of discrete probability matrix, some solutions are generated and elite solutions are selected. Furthermore, according to the characteristics of BACAP, some immune maturation operators are utilized to balance the local search and global optimization ability of the algorithm. Finally, a robust berth allocation and quay crane scheduling plan in uncertain environment is generated. Computational experiment verifies the feasibility of the model and the effectiveness of the proposed algorithm.

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“…Para isso, a proposta revisita o conceito de AMs, que percorrem um itinerário, ou caminho, que inclui nós fontes de um determinado dado de interesse, como definido a priori pelo gateway da rede para a coleta. Diferente de propostas anteriores [Lu et al 2019, Chou and Nakajima 2018, Liu et al 2016, porém, há a coleta oportunista de dados, o que significa que dados podem ser coletados nos nós intermediários, mesmo se estes não forem nós fontes dos dados de interesse, e agregados ao agente. Como o encaminhamento das mensagensé feito por múltiplos saltos, a coleta oportunista pode ser realizada de forma eficiente através do uso do método de otimização discreta Knapsack.…”

Section: Introductionunclassified

Coleta oportunista de dados para a Internet das Coisas com o uso de otimização discreta

Filho

Campista

2019

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance)

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A Internet das Coisas (Internet of Things – IoT) se baseia na coleta de dados para futuro processamento e tomada de decisão. Em cenários de redes restritas (Low Power and Lossy Network - LLN) com múltiplos saltos, o encaminhamento eficiente de dados em termos de tráfego gerado e consumo de energia se torna fundamental. Este artigo revisita o conceito de agentes móveis para realizar coleta de dados ao longo dos itinerários dos agentes. A ideia é evitar o envio de requisições para rede quando conteúdos, não expirados e coletados de forma oportunista, estão armazenados no cache de um elemento central. No mecanismo proposto, o itinerário é composto por dispositivos de interesse e dispositivos intermediários em um ciclo fechado na origem. É utilizada a otimização Knapsack para acrescentar dados não solicitados de forma oportunista. A recompensa é calculada conforme a popularidade dos dados. As simulações mostram que é possı́vel reduzir o tráfego na rede e a energia con- sumida pelos dispositivos quando comparado com a coleta de agentes móveis tradicional.

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A Clonal Selection Algorithm for Energy-Efficient Mobile Agent Itinerary Planning in Wireless Sensor Networks

Cited by 12 publications

References 17 publications

An energy efficient multi-mobile agent itinerary planning approach in wireless sensor networks

An energy efficient multi-mobile agent itinerary planning approach in wireless sensor networks

Immune Cross Entropy Algorithm for Robust Berth Allocation and Quay Crane Assignment Problem

Coleta oportunista de dados para a Internet das Coisas com o uso de otimização discreta

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