Location Finding in Wireless Sensor Network Based on Soft Computing Methods

Nekooei, Seyed Mohammad; Shalmani, Mohammad Taghi Manzuri

doi:10.1109/iccase.2011.5997582

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…Figure 10 shows a comparison of the proposed hybrid PSO-ANN algorithm with other artificial intelligent algorithms employed for outdoor and indoor wireless SN localization in other studies. The results reveal that the location estimated using the hybrid PSO-ANN algorithm outperforms the algorithms of previous studies [33,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] in terms of MAE.…”

Section: Hybrid Pso-ann Algorithm For Distance Estimationmentioning

confidence: 94%

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

2016

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In most wireless sensor network (WSN) applications, the sensor nodes (SNs) are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT) applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC) based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN) using the received signal strength indicator (RSSI), while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm) by 78%.

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Section: Hybrid Pso-ann Algorithm For Distance Estimationmentioning

confidence: 94%

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

2016

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“…We have utilized SA as described in [15] a search method to explore the restoration space comprising of a set of alternative WSN topologies with various lifespans. (1) and (2). Hereby, the parameter …”

Section: Problem Formulationmentioning

confidence: 98%

Soft computing tool for restoring failures within wireless sensor networks

Habib

Marimuthu

Sudha

2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

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The proposed soft computing tool is developed as a suite of software programs capable of envisaging the evolution within wireless sensor networks (WSN) by accomplishing a series of processes. These processes are: modelling a real problem scenario in computing form, formulating the problem as an optimization problem, evaluating the outcomes and finally, presenting the outcomes in a statistical form. We have examined many possible failing scenarios of WSN operations through the proposed tool, which was capable to restore WSN in the presence of failures to continue normal operations. Simulated Annealing (SA) is utilized as a search method, which is embedded within the soft computing tool to explore the restoration space comprising of possible alternative topologies generated for each failing scenario, taking lifespan into consideration. The tested scenarios and their restorations can be hard-coded into a real WSN, thus preparing WSN to evolve through failures. The simulation results of a typical WSN illustrates the capability of the proposed computing tool to restore 79% of the lost days when no restoration scheme was applied.

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“…The proposed ANN localization technique can be compared with several studies that adopted different soft computing localization techniques in terms of MAE, such as particle bacterial foraging algorithm (BFA) and PSO [104], ANN [64,[105][106][107][108][109], gravitational search algorithm hybrid with neural network (GSA-ANN) [26], PSO hybrid with neural network (PSO-ANN) [63,110], quantum swarm optimization (QPSO) [111], neuro-fuzzy (NF) and genetic fuzzy (GF) [112], and extreme learning machine (ELM) [113] for indoor environments, as shown in Figure 26. The performance of the current work in terms of MAE is achieved based on the methodology that has been presented in Section 3.2 through 3.5, and this is validated by simulation implementation (Section 3.2) and simulation results (Section 5.2) for LOS and NLOS environments.…”

Section: Comparison Of Localization Errorsmentioning

confidence: 99%

“…Clearly, the proposed FDS based on DDA outperforms previous systems in terms of battery life, which is extended to 1480 h (62 days), as shown in Figure 27. Proposed ANN (LOS) [104],BFA Proposed ANN (NLOS) [104],PSO [105], ANN [106],ANN [26],GSA-ANN [63],PSO-ANN [111 ],QPSO [110],PSO-ANN [107],ANN [108],ANN [112],NF [112],GF [109],ANN [64],ANN [113],ELM MAE (m) Figure 26. Comparison between MAE of the proposed ANN technique and previous techniques.…”

Section: Power Consumption Comparisonmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Location Finding in Wireless Sensor Network Based on Soft Computing Methods

Cited by 16 publications

References 17 publications

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

Soft computing tool for restoring failures within wireless sensor networks

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

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