Mobility-Aware Routing Algorithm for Mobile Ad Hoc Networks

Mobile ad hoc network (MANET) is a group of wireless mobile nodes that create a temporary network without the help of any central administration or standard support services. Mobility of nodes determines the overall performance of MANET networks. High mobility of nodes causes frequent changes in the network topology, and this leads to link breakage and increases reinitiating of the route discovery process. MANETs commonly use broadcasting techniques for route discovery process. However, it can cause redundant rebroadcasts, packet collisions, and channel contention. The main objective of this paper is to design and develop the mobility-aware routing algorithm (MARA) to enhance the performance of the routing protocol in MANETs. The proposed scheme allows mobile nodes to rebroadcast or discard received broadcasted messages. The decision is based on the combination of node speed, distance between nodes, and residual energy of nodes. These parameters are considered both in route request and route reply process to reduce the chance of link breakage and broadcast storm problems. The proposed algorithm has been evaluated based on the performance metrics: packet delivery ratio, average end-to-end delay, throughput, and routing overhead. We have used network simulator NS-2 V-2.35. The simulation results revealed that MARA outperforms ad hoc on-demand distance vector (AODV), mobility and direction aware (MAD), and mobility and energy-aware (MAE) routing protocols

Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Indoor localization is used in many applications like security, healthcare, location based services, and social networking. Fingerprinting-based methods are widely used for indoor localization. But received signal strength (RSS) variation due to device diversity and change of conditions in the localization environment (e.g., distribution of furniture, people presence and movement, and opening and closing of doors) induce a significant localization error. To overcome this, we propose a hybrid indoor localization system using radio frequency (RF) and pyroelectric infrared (PIR) sensors. Our localization system has two stages. In the first stage, the zone of the target person is identified by PIR sensors. In the second stage, we apply K -nearest neighbor ( K -NN) algorithm to the fingerprints within the zone identified and estimate position. Zone based processing of fingerprints will exclude deviated fingerprints because of RSS variation. We proposed two localization methods: Proposed_1 and Proposed_2 which use signal strength difference (SSD) and RSS, respectively. Simulation results show that the 0.8-meter accuracy of Proposed_1 achieves 84% and Proposed_2 achieves 65%, while traditional fingerprinting and SSD are 46% and 28%, respectively

Sleeping posture recognition using fuzzy c-means algorithm

Abstract Background Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet. Method We designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures. Results The experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy. Conclusions The proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture

Intensity and Wavelength Division Multiplexing FBG Sensor System Using a Raman Amplifier and Extreme Learning Machine

A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped