Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user\n(PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance\nto the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the\noccurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision\nbased on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along\nwith z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The\nproposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite\nmalicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious\nuser (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the\nexisting hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA....
Fires usually occur in homes because of carelessness and changes in environmental\nconditions. They cause threats to the residential community and may result in human death and\nproperty damage. Consequently, house fires must be detected early to prevent these types of\nthreats. The immediate notification of a fire is the most critical issue in domestic fire detection\nsystems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a\nconsequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and\nlow-power sensor devices that have the ability to sense the environment and can provide real-time\nfire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor\nnetwork using multiple sensors for early detection of house fires. In addition, we used the Global\nSystem for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire\ndetection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a\nlanguage program. The simulation results showed that our system is able to detect early fire, even\nwhen a sensor is not working, while keeping the energy consumption of the sensors at an\nacceptable level....
A wireless local area network (WLAN) is an important type of wireless network which\nconnotes different wireless nodes in a local area network. Network traffic or data traffic in a WLAN is\nthe amount of network packets moving across a wireless network from each wireless node to another\nwireless node, which provide the load of sampling in a wireless network. WLAN�s network traffic\nis the main component for network traffic measurement, network traffic control, and simulation.\nIn addition, traffic classification technique is an essential tool for improving the Quality of Service\n(QoS) in different wireless networks in the complex applications, such as local area networks, wireless\nlocal area networks, wireless personal area networks, wireless metropolitan area networks, and wide\narea networks. Network traffic classification is also an essential component in the products for QoS\ncontrol in different wireless network systems and applications. Classifying network traffic in a WLAN\nallows one to see what kinds of traffic we have in each part of the network, organize the various\nkinds of network traffic in each path into different classes in each path, and generate network traffic\nmatrix in order to identify and organize network traffic, which is an important key for improving\nthe QoS feature. In this paper, a new architecture based on the following algorithms is presented\nfor improving the QoS feature in a wireless local area network: (1) Real-Time Network Traffic\nClassification (RTNTC) algorithm for WLANs based on Compressed Sensing (CS); (2) Real-Time\nNetwork Traffic Monitoring (RTNTM) approach based on CS. This architecture enables continuous\ndata acquisition and compression of WLAN�s signals that are suitable for a variety of other wireless\nnetworking applications. At the transmitter side of each wireless node, an analog CS framework is\napplied at the sensing step before an analog to digital converter in order to generate the compressed\nversion of the input signal. At the receiver side of the wireless node, a reconstruction algorithm is\napplied in order to reconstruct the original signals from the compressed signals with high probability\nand enough accuracy. The proposed architecture allows reducing Data Delay Probability (DDP) to\n15%, Bit Error Rate (BER) to 14% at each wireless node, False Detection Rate (FDR) to 25%, and Packet\nDelay (PD) to 15%, which are good records for WLANs. The proposed architecture is increased\nData Throughput (DT) to 22% and Signal to Noise (S/N) ratio to 17%, and 10% accuracy of wireless\ntransmission. The proposed algorithm outperforms existing algorithms by achieving a good level of\nQuality of Service (QoS), which provides a good background for establishing high quality wireless\nlocal area networks....
Bone loss and osteoporosis is a serious health problem worldwide. The impact of osteoporosis\nis far greater than many other serious health problems, such as breast and prostate cancers.\nStatistically, one in three women and one in five men over 50 years of age will experience osteoporotic\nfractures in their life. In this paper, the design and development of a portable IoT-based sensing\nsystem for early detection of bone loss have been presented. The CTx-I biomarker was measured\nin serum samples as a marker of bone resorption. A planar interdigital sensor was used to evaluate\nthe changes in impedance by any variation in the level of CTx-I. Artificial antibodies were used to\nintroduce selectivity to the sensor for CTx-I molecule. Artificial antibodies for CTx-I molecules were\ncreated using molecular imprinted polymer (MIP) technique in order to increase the stability of the\nsystem and reduce the production cost and complexity of the assay procedure. Real serum samples\ncollected from sheep blood were tested and the result validation was done by using an ELISA kit.\nThe PoC device was able to detect CTx-I concentration as low as 0.09 ng/mL. It exhibited an excellent\nlinear behavior in the range of 0.1ââ?¬â??2.5 ng/mL, which covers the normal reference ranges required for\nbone loss detection. Future possibilities to develop a smart toilet for simultaneous measurement of\ndifferent bone turnover biomarkers was also discussed....
Rice (Oryza sativa ) is the second staple food largely grown and widely consumed\nin Pakistan. About 10% of the total crop area of Pakistan is cultivated\nby rice that takes a part in value addition of almost 1.3% - 1.6% in the total\nGross Domestic Product (GDP). Due to global warming, temperature has a\nprofound impact on rice crop phenology. Low temperature is the main factor\nof delay in rice plant growth and very high temperature results in stressed and\nshort heighted plant so the crop sown in a region at the same time is not ready\nto harvest at same hours but a delay is observed. The study area under investigation\nwas district Sheikhupura, Nankana, Lahore, Gujranawala and Hafizabad,\nwhich are famous for rice productivity. Landsat 7, 8 freely available\nthermal dataset are used to calculated pixel based temperature values to evaluate\ngrowth using agricultural growth indicators. The total covered area was\n13,480 km2 in which 484 km2 area was marked as less growth rate area with\nlow temperature values due to water body and excess of vegetation over there.\nAbout 7960 km2 area is marked as good for growth experiencing optimum\ntemperature for rice plant. Approximately 4944 km2 area is marked as stressed\nrice plant area experiencing high temperature values adjacent to urban population.\nAn attempt is made here to map this effect of temperature-based\ngrowth variability of the rice plant across the study area....
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