358 research outputs found

    Vessel Tracking System Based LoRa SX1278

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    This research presents a vessel tracking system that provides real-time coordinate and speed information. The idea behind the development of this system originated from Automatic Identification System (AIS) technology, which functions as a vessel monitoring system in maritime areas. The system aims to improve navigation safety, monitor vessel traffic, and maritime security. In Indonesia, AIS is regulated by the Ministry of Transportation. However, this technology has not yet been implemented in river waters. In addition, AIS is a complex and expensive system. In this research, geographic location detection information in the form of a vessel tracking system is obtained using the UBlox Neo-6M GPS module based on LoRa technology. The LoRa mechanism periodically sends location data and vessel speed from the node to the gateway. The data is then sent to the ThingSpeak server using the MQTT protocol. On the server, the data can be accessed for further analysis. The developed system shows that the research can be realized and the system functions properly through a series of experimental tests. While in the in situ test, the system displayed good performance on LoRa SF 7 configuration with a signal strength of -118 dBm within the communication range of 1000 meters. This result can be improved by considering the MAPL value of -138 dBm

    Software defined wireless network (sdwn) for industrial environment: case of underground mine

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    Avec le développement continu des industries minières canadiennes, l’établissement des réseaux de communications souterrains avancés et sans fil est devenu un élément essentiel du processus industriel minier et ceci pour améliorer la productivité et assurer la communication entre les mineurs. Cette étude vise à proposer un système de communication minier en procurant une architecture SDWN (Software Defined Wireless Network) basée sur la technologie de communication LTE. Dans cette étude, les plateformes les plus importantes de réseau mobile 4G ont été étudiées, configurées et testées dans deux zones différentes : un tunnel de mine souterrain et un couloir intérieur étroit. Également, une architecture mobile combinant SDWN et NFV (Network Functions Virtualization) a été réalisée

    LoRa-based Network for Water Quality Monitoring in Coastal Areas

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    [EN] Agriculture Farming activity near to rivers and coastal areas sometimes imply spills of chemical and fertilizers products in aquifers and rivers. These spill highly affect the water quality in rivers' mouths and beaches close to those rivers. The presence of these elements can worse the quality for its normal use, even for its enjoying. When this polluted water reaches the sea can also have problematic consequences for fauna and flora. For this reason, it is important to rapidly detect where these spills are taking place and where the water does not have the minimum of quality to be used. In this article we propose the design and implementation of a LoRa (Long Range) based wireless sensor network for monitoring the quality of water in coastal areas, rivers and ditches with the aim to generate an observatory of water quality of the monitored areas. This network is composed by several wireless sensor nodes endowed with several sensors to physically measure parameters of water quality, such as turbidity, temperature, etc., and weather conditions such as temperature and relative humidity. The data collected by the sensors is sent to a gateway that forwards them to our storage database. The database is used to create an observatory that will permit the monitoring of the environment where the network is deployed. We test different devices to select the one that presents the best performance. Finally, the final solution is tested in a real environment for checking its correct operation. Two different tests will be carried out. The first test checks the correct operation of sensors and the network architecture while the second test show us the devices performance in terms of coverage.Sendra, S.; Parra-Boronat, L.; Jimenez, JM.; GarcĂ­a-GarcĂ­a, L.; Lloret, J. (2023). LoRa-based Network for Water Quality Monitoring in Coastal Areas. Mobile Networks and Applications (Online). 28(1):65-81. https://doi.org/10.1007/s11036-022-01994-8658128

    LT10 A LIGHTWEIGHT PROPOSED ENCRYPTION ALGORITHM FOR IOT

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    In this paper, algorithm (LT10) which is originally consist of four kasumi elements is proposed as a lightweight encryption algorithm, the proposed algorithm take into account that the IOT devices have a limit computation abilities and the sensitivity of smart homes and IOT network information that need to be exchanged the key length is 128 bit and the block length is 128 bi

    Intrusion Detection System for detecting internal threats in 6LoWPAN

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    6LoWPAN (IPv6 over Low-power Wireless Personal Area Network) is a standard developed by the Internet Engineering Task Force group to enable the Wireless Sensor Networks to connect to the IPv6 Internet. This standard is rapidly gaining popularity for its applicability, ranging extensively from health care to environmental monitoring. Security is one of the most crucial issues that need to be considered properly in 6LoWPAN. Common 6LoWPAN security threats can come from external or internal attackers. Cryptographic techniques are helpful in protecting the external attackers from illegally joining the network. However, because the network devices are commonly not tampered-proof, the attackers can break the cryptography codes of such devices and use them to operate like an internal source. These malicious sources can create internal attacks, which may downgrade significantly network performance. Protecting the network from these internal threats has therefore become one of the centre security problems on 6LoWPAN. This thesis investigates the security issues created by the internal threats in 6LoWPAN and proposes the use of Intrusion Detection System (IDS) to deal with such threats. Our main works are to categorise the 6LoWPAN threats into two major types, and to develop two different IDSs to detect each of this type effectively. The major contributions of this thesis are summarised as below. First, we categorise the 6LoWPAN internal threats into two main types, one that focuses on compromising directly the network performance (performance-type) and the other is to manipulate the optimal topology (topology-type), to later downgrade the network service quality indirectly. In each type, we select some typical threats to implement, and assess their particular impacts on network performance as well as identify performance metrics that are sensitive in the attacked situations, in order to form the basis detection knowledge. In addition, on studying the topology-type, we propose several novel attacks towards the Routing Protocol for Low Power and Lossy network (RPL - the underlying routing protocol in 6LoWPAN), including the Rank attack, Local Repair attack and DIS attack. Second, we develop a Bayesian-based IDS to detect the performance-type internal threats by monitoring typical attacking targets such as traffic, channel or neighbour nodes. Unlike other statistical approaches, which have a limited view by just using a single metric to monitor a specific attack, our Bayesian-based IDS can judge an abnormal behaviour with a wiser view by considering of different metrics using the insightful understanding of their relations. Such wiser view helps to increase the IDS’s accuracy significantly. Third, we develop a Specification-based IDS module to detect the topology-type internal threats based on profiling the RPL operation. In detail, we generalise the observed states and transitions of RPL control messages to construct a high-level abstract of node operations through analysing the trace files of the simulations. Our profiling technique can form all of the protocol’s legal states and transitions automatically with corresponding statistic data, which is faster and easier to verify compare with other manual specification techniques. This IDS module can detect the topology-type threats quickly with a low rate of false detection. We also propose a monitoring architecture that uses techniques from modern technologies such as LTE (Long-term Evolution), cloud computing, and multiple interface sensor devices, to expand significantly the capability of the IDS in 6LoWPAN. This architecture can enable the running of both two proposed IDSs without much overhead created, to help the system to deal with most of the typical 6LoWPAN internal threats. Overall, the simulation results in Contiki Cooja prove that our two IDS modules are effective in detecting the 6LoWPAN internal threats, with the detection accuracy is ranging between 86 to 100% depends on the types of attacks, while the False Positive is also satisfactory, with under 5% for most of the attacks. We also show that the additional energy consumptions and the overhead of the solutions are at an acceptable level to be used in the 6LoWPAN environment

    Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

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    Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
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