GA-MIP: Genetic algorithm based multiple Mobile Agents itinerary planning in wireless sensor networks

Cai, Wei; Chen, Min; Hara, Takahiro; Shu, Lei

doi:10.4108/icst.wicon2010.8518

Cited by 30 publications

(31 citation statements)

References 17 publications

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“…Another algorithm, Genetic Algorithm-based Multi agents Itinerary Planning (GA-MIP) is proposed in [10]. GA-MIP first proposes a new method for two-level encoding of MA to solve the MIP problem.…”

Section: Contributions Of the Cl-mip Include The Followingmentioning

confidence: 99%

Optimal itinerary planning for mobile multiple agents in WSN

Bendjima¹,

Feham²

2012

IJACSA

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Abstract-Multi mobile agents based on data collection and aggregations have proved their effective in wireless sensor networks (WSN). However, the way in which these agents are deployed must be intelligently planned and should be adapted to the characteristics of wireless sensor networks. While most existing studies are based on the algorithms of itinerary planning for multiple agents i.e. determining the number of mobile agents (MAs) to be deployed, how to group source nodes for each MA, attributing the itinerary of each MA for its source nodes. These algorithms determine the efficiency or effectiveness of aggregation. This paper aims at presenting the drawbacks of these approaches in large-scale network, and proposes a method for grouping network nodes to determine the optimal itinerary based on multi agents using a minimum amount of energy with efficiency of aggregation in a minimum time. Our approach is based on the principle of visiting central location (VCL). The simulation experiments show that our approach is more efficient than other approaches in terms of the amount of energy consumed in relation with the efficiency of aggregation in a minimum time.

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Section: Contributions Of the Cl-mip Include The Followingmentioning

confidence: 99%

Optimal itinerary planning for mobile multiple agents in WSN

Bendjima¹,

Feham²

2012

IJACSA

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“…In [10][11][12][13], the authors proposed multi-agent itinerary planning (MIP) algorithms that help in the collection of concurrent sensor data to reduce latency. These algorithms differ in source node grouping methods.…”

Section: Centralized Approach-based Schemesmentioning

confidence: 99%

“…This approach does not describe how to find out an optimal angle gap threshold. In [13], the authors proposed a genetic algorithm by encoding how many agents are dispatched and which sensor nodes are visited by individual agents. The limitation of a genetic algorithm-based approach is its higher computational complexity [20].…”

Section: Centralized Approach-based Schemesmentioning

confidence: 99%

“…The efficiency of a mobile agent-based data aggregation and collection scheme depends upon the agent migration scheme, which affects the overall energy consumption and aggregation cost [5]. The solutions that have been proposed until now, are either using a centralized approach [6][7][8][9][10][11][12][13][14] or a dynamic and distributed approach [15][16][17][18] to determine the migration paths of the agents. In a dynamic and distributed approach, the migration path of an agent is determined using local information at each host, while in a centralized approach, it is pre-computed at the sink node before an agent is dispatched and it is based on the global information of the network topology.…”

Section: Introductionmentioning

confidence: 99%

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An Energy Efficient Distributed Approach-Based Agent Migration Scheme for Data Aggregation in Wireless Sensor Networks

2014

J Inf Process Syst

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The use of mobile agents for collaborative processing in wireless sensor network has gained considerable attention. This is when mobile agents are used for data aggregation to exploit redundant and correlated data. The efficiency of agent-based data aggregation depends on the agent migration scheme. However, in general, most of the proposed schemes are centralized approach-based schemes where the sink node determines the migration paths for the agents before dispatching them in the sensor network. The main limitations with such schemes are that they need global network topology information for deriving the migration paths of the agents, which incurs additional communication overhead, since each node has a very limited communication range. In addition, a centralized approach does not provide fault tolerant and adaptive migration paths. In order to solve such problems, we have proposed a distributed approach-based scheme for determining the migration path of the agents where at each hop, the local information is used to decide the migration of the agents. In addition, we also propose a local repair mechanism for dealing with the faulty nodes. The simulation results show that the proposed scheme performs better than existing schemes in the presence of faulty nodes within the networks, and manages to report the aggregated data to the sink faster.

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“…Existing approaches for generating agent itineraries can be classified into two categories: (i) single agent itinerary planning [17][18][19][20] and (ii) multi-agent itinerary planning (MIP) [21][22][23]. For example, Xu et al [17] investigated static, dynamic, and predictive dynamic schemes to solve the target tracking problem in WSNs.…”

Section: Introductionmentioning

confidence: 99%

Disjoint multi mobile agent itinerary planning for big data analytics

Liu

Cao

Yin

et al. 2016

J Wireless Com Network

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Sensor networks are often part of a cyber-physical system. A large-scale sensor network often involves big data collection and data fusion. The agent technology has drawn much attention in wireless sensor networks (WSNs) to perform data fusion and energy balancing. Existing multi-agent itinerary algorithms are either time-consuming or too complicated in practice. In this paper, we design a routing itinerary planning scheme for the multi-agent itinerary problem by constructing the spanning tree of WSN nodes. First, we build a multi-agent-based distributed WSN (DWSN) model and energy consumption model. Second, we present a novel routing itinerary algorithm named DMAIP, which can group all the sensor nodes into multiple itineraries for agents. We also extend DMAIP and design DMAIP-E, which can avoid long-distance transmission in DMAIP. Our evaluation results demonstrate that our algorithms are better in terms of life cycle and energy consumption than the existing DWSN data collecting schemes.

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GA-MIP: Genetic algorithm based multiple Mobile Agents itinerary planning in wireless sensor networks

Cited by 30 publications

References 17 publications

Optimal itinerary planning for mobile multiple agents in WSN

Optimal itinerary planning for mobile multiple agents in WSN

An Energy Efficient Distributed Approach-Based Agent Migration Scheme for Data Aggregation in Wireless Sensor Networks

Disjoint multi mobile agent itinerary planning for big data analytics

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