Target classification in Wireless Sensor Network using Particle Swarm Optimization (PSO)

Gharaibeh, Khaled M.; Yaqot, Abdullah

doi:10.1109/sas.2012.6166290

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…In fact, these hit rates outperform the values obtained by using PSO algorithm [12]. However, the two-stage SVM prodedure [11] results are better but they did not work in a real scenario and their radar echo signals presented no interference due to the fact that it worked in a controlled environment without the presence of multiple targets (Table IV).…”

Section: Discussionmentioning

confidence: 86%

“…Moreover, a two-stage support vector machine (SVM) classification method using Mel-Frequency Cepstrum coefficients is developed and proposed in [11]. Another alternative is proposed by Gharaibeh and Yaqot [12]. They presented a methodology based on Particle Swarm Optimization (PSO) that improved the computational speed for the nearest neighbor clustering technique.…”

Section: Related Workmentioning

confidence: 99%

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Low-cost radar-based target identification prototype using an expert system

Pérez

Villaverde

Moreno

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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Smart and green cities are hot topics in current research because people are becoming more conscious about their impact on the environment and the sustainability of their cities as the population increases. Many researchers are searching for mechanisms that can reduce power consumption and pollution in the city environment. This paper addresses the issue of public lighting and how it can be improved in order to achieve a more energy efficient city. This work is focused on making the process of turning the streetlights on and off more intelligent so that they consume less power and cause less light pollution. The proposed solution is comprised of a radar device and an expert system implemented on a low-cost platform based on a DSP. By analyzing the radar echo in both the frequency and time domains, the system is able to detect and identify objects moving in front of it. This information is used to decide whether or not the streetlight should be turned on. Experimental results show that the proposed system can provide hit rates over 80%, promising a good performance. In addition, the proposed solution could be useful in kind of other applications such as intelligent security and surveillance systems and home automation.

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Section: Discussionmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Low-cost radar-based target identification prototype using an expert system

Pérez

Villaverde

Moreno

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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show abstract

“…(3) while ( < max ) (4) Update current location information of ; (5) Update the neighbour nodes set and the number of ; (6) Replay EDQ with current coverage rate of monitored area, and determine whether is a redundant node; 7if ( is a redundant node) (8) Set into sleeping mode; (9) else (10) if ( is sleeping) (11) Wake up ; (12) ⃗ ← 0; (13) for ( = 0; < ; ++) (14) Calculate the virtual repulsive force ⃗ on node Si from ; (15) ⃗ ← ⃗ + ⃗ ;…”

Section: Case Studymentioning

confidence: 99%

“…Move to the target position; Update ; (5) Receive target location information of all nodes within the communication range. We use node as an example; (6) Calculate the coverage rate of monitored region Ω after moving to target location; (7) Calculate the coverage impact factor of ; (8) Send the coverage impact factor to ; (9) sleep(Δ ); (10) end; Algorithm 2 do not present a fluctuating state during the execution of RBCT algorithm since the introduction of coverage impact factor. By RBCT algorithm, the coverage rates are raised to 50.3%, 65.2%, 76.3%, 86.1%, and 92.8% without changing the number of initial nodes.…”

Section: Case Studymentioning

confidence: 99%

Redundancy Model and Boundary Effects Based Coverage-Enhancing Algorithm for 3D Underwater Sensor Networks

Huang

Sun

Wei

et al. 2014

International Journal of Distributed Sensor Networks

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In the study of three-dimensional underwater sensor networks, the nodes would produce changes in perception range under the influence of environmental factors and their own hardware. Requesting all nodes completely isomorphic is unrealistic. Ignoring boundary effects usually causes the coverage effect of the actual deployment of networks to not reach the anticipated result. This paper firstly presents an underwater sensing model with normal distributed node sensing radius. Secondly, it gives the relationship between expected deployment quality and the number of nodes in the premise of considering boundary effects. Then, it deduces nodes' redundancy formula based on the sensing model with normal distributed node sensing radius, making node could determine whether itself is a redundant node only based on the number of its neighbour nodes. Furthermore, this paper proposes a redundancy model and boundary effects based coverage-enhancing algorithm for three-dimensional underwater sensor networks (RBCT). Simulation results show that RBCT, compared to similar algorithms, has certain advantages in saving energy and enhancing coverage rate.

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“…Moreover, a two-stage support vector machine (SVM) classification method using Mel-Frequency Cepstrum coefficients is developed and proposed in [ 5 ]. Another alternative is proposed by Gharaibeh and Yaqot [ 6 ]. They presented a methodology based on Particle Swarm Optimization (PSO) that improved the computational speed for the nearest neighbor clustering technique.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

Villaverde

Pérez

Moreno

2015

Sensors

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The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

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Target classification in Wireless Sensor Network using Particle Swarm Optimization (PSO)

Cited by 7 publications

References 16 publications

Low-cost radar-based target identification prototype using an expert system

Low-cost radar-based target identification prototype using an expert system

Redundancy Model and Boundary Effects Based Coverage-Enhancing Algorithm for 3D Underwater Sensor Networks

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

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