Mobile Agent Routing Based on a Two-Stage Optimization Model and a Hybrid Evolutionary Algorithm in Wireless Sensor Networks

Yang, Sen; Huang, Rui; Shi, Hongbo

doi:10.1007/11881223_119

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…A hybridized PSO-GA is used in [21] to select the optimal multi-hop routing scheme which minimizes the communication energy consumption. A hybrid PSO-GA algorithm is presented also in [22].…”

Section: Routingmentioning

confidence: 99%

Introducing DOWSN: Distributed optimization in wireless sensor networks

Iacca¹

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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Driven by a broad range of applications, the Computational Intelligence research community has recently put a growing interest on the emergent technology of Wireless Sensor Networks (WSNs). Due to their distributed structure, WSNs pose several technical challenges caused by local failures, network issues and severely constrained hardware resources. Nevertheless, the possibility to perform an online optimization within WSNs is appealing since it might lead the path to advanced network features like intelligent sensing, distributed modelling, self-optimizing protocols, anomaly detection, etc. just to name a few. In this paper we present DOWSN, a novel Distributed Optimization framework for WSNs. Based on an island model, DOWSN is characterized by a peer-to-peer infrastructure in which each node executes an optimization process and shares pieces of information, i.e. local achievements, with its neighbors. Preliminary experiments show that DOWSN is able to efficiently exploit the communication capabilities and the inherently parallel nature of WSNs, thus finding optimal solutions fast and reliably.

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Section: Routingmentioning

confidence: 99%

Introducing DOWSN: Distributed optimization in wireless sensor networks

Iacca¹

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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“…In [36], an onchip implementation of Ant Colony Optimization (ACO) for energy-efficient routing is described. Two examples of hybrid PSO-GA algorithms for energy-aware routing are instead presented in [8] and [62].…”

Section: Routingmentioning

confidence: 99%

Distributed optimization in wireless sensor networks: an island-model framework

Iacca¹

2013

Soft Comput

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Wireless Sensor Networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational Intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (Distributed Optimization for WSN) is an islandmodel infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of 15 continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.

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“…Equation (2) denotes the updated swarm location in the design space. A substantial amount of success has been achieved in utilizing PSO for applications such as aircraft design [14,15], topology optimization [16], optimal truss structure design [17], wireless network routing problems [18], and optimization in manufacturing and production operations [19,20] to name a few.…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

Foo

Knutzon

Kalivarapu

et al. 2009

Journal of Aerospace Computing, Information, and Communication

112

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Military operations are turning to more complex and advanced automation technologies for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method. Nomenclature C total cost function for a path C T , C L , C R threat, fuel, and reconnaissance components cost for a path c 1 , c 2 first and second confidence parameters for PSO K T , K L , K R weighting factors for threat, fuel, and reconnaissance components cost L length of path M number of control points for B-spline curve N number of line segments that define the B-spline curve N(u) bernstein basis function for B-spline curve p • u parametric equation for B-spline curve u set of line segments for B-spline curve V velocity vector for particle swarm optimization (PSO) w inertia weight for particle swarm optimization X i ith design variable in an optimization objective function in PSO x knot vector for B-spline curve Z T , Z R threat zone and reconnaissance zone λ w decay factor for inertia weight for PSO

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Mobile Agent Routing Based on a Two-Stage Optimization Model and a Hybrid Evolutionary Algorithm in Wireless Sensor Networks

Cited by 9 publications

References 18 publications

Introducing DOWSN: Distributed optimization in wireless sensor networks

Introducing DOWSN: Distributed optimization in wireless sensor networks

Distributed optimization in wireless sensor networks: an island-model framework

Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

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